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def to_memory_units(memory_bytes, round_up):
'Convert from bytes -> memory units.'
value = (memory_bytes / MEMORY_RESOURCE_UNIT_BYTES)
if (value < 1):
raise ValueError('The minimum amount of memory that can be requested is {} bytes, however {} bytes was asked.'.format(MEMORY_RESOURCE_UNIT_BYTES, memory_bytes))
if (isinstance(value, float) and (not value.is_integer())):
if round_up:
value = int(math.ceil(value))
else:
value = int(math.floor(value))
return int(value) | -8,472,874,613,192,096,000 | Convert from bytes -> memory units. | python/ray/ray_constants.py | to_memory_units | stephanie-wang/ray | python | def to_memory_units(memory_bytes, round_up):
value = (memory_bytes / MEMORY_RESOURCE_UNIT_BYTES)
if (value < 1):
raise ValueError('The minimum amount of memory that can be requested is {} bytes, however {} bytes was asked.'.format(MEMORY_RESOURCE_UNIT_BYTES, memory_bytes))
if (isinstance(value, float) and (not value.is_integer())):
if round_up:
value = int(math.ceil(value))
else:
value = int(math.floor(value))
return int(value) |
def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) | -7,717,226,299,015,962,000 | Args:
train/dev/test (list[list] or str):
Filenames of the train/dev/test datasets.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
update_steps (int):
Gradient accumulation steps. Default: 1.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs. | supar/parsers/dep.py | train | LiBinNLP/HOSDP | python | def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) |
def evaluate(self, data, buckets=8, batch_size=5000, punct=False, tree=True, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) | 534,450,388,935,096,700 | Args:
data (str):
The data for evaluation, both list of instances and filename are allowed.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating evaluation configs.
Returns:
The loss scalar and evaluation results. | supar/parsers/dep.py | evaluate | LiBinNLP/HOSDP | python | def evaluate(self, data, buckets=8, batch_size=5000, punct=False, tree=True, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) |
def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, tree=True, proj=False, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) | 7,355,355,497,957,411,000 | Args:
data (list[list] or str):
The data for prediction, both a list of instances and filename are allowed.
pred (str):
If specified, the predicted results will be saved to the file. Default: ``None``.
lang (str):
Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.
``None`` if tokenization is not required.
Default: ``None``.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
prob (bool):
If ``True``, outputs the probabilities. Default: ``False``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating prediction configs.
Returns:
A :class:`~supar.utils.Dataset` object that stores the predicted results. | supar/parsers/dep.py | predict | LiBinNLP/HOSDP | python | def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, tree=True, proj=False, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) |
@classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'biaffine-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('biaffine-dep-en')\n >>> parser = Parser.load('./ptb.biaffine.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) | 5,227,721,562,502,721,000 | Loads a parser with data fields and pretrained model parameters.
Args:
path (str):
- a string with the shortcut name of a pretrained model defined in ``supar.MODEL``
to load from cache or download, e.g., ``'biaffine-dep-en'``.
- a local path to a pretrained model, e.g., ``./<path>/model``.
reload (bool):
Whether to discard the existing cache and force a fresh download. Default: ``False``.
src (str):
Specifies where to download the model.
``'github'``: github release page.
``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).
Default: None.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs and initializing the model.
Examples:
>>> from supar import Parser
>>> parser = Parser.load('biaffine-dep-en')
>>> parser = Parser.load('./ptb.biaffine.dep.lstm.char') | supar/parsers/dep.py | load | LiBinNLP/HOSDP | python | @classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'biaffine-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('biaffine-dep-en')\n >>> parser = Parser.load('./ptb.biaffine.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) |
@classmethod
def build(cls, path, min_freq=2, fix_len=20, **kwargs):
'\n Build a brand-new Parser, including initialization of all data fields and model parameters.\n\n Args:\n path (str):\n The path of the model to be saved.\n min_freq (str):\n The minimum frequency needed to include a token in the vocabulary.\n Required if taking words as encoder input.\n Default: 2.\n fix_len (int):\n The max length of all subword pieces. The excess part of each piece will be truncated.\n Required if using CharLSTM/BERT.\n Default: 20.\n kwargs (dict):\n A dict holding the unconsumed arguments.\n '
args = Config(**locals())
args.device = ('cuda' if torch.cuda.is_available() else 'cpu')
os.makedirs((os.path.dirname(path) or './'), exist_ok=True)
if (os.path.exists(path) and (not args.build)):
parser = cls.load(**args)
parser.model = cls.MODEL(**parser.args)
parser.model.load_pretrained(parser.WORD.embed).to(args.device)
return parser
logger.info('Building the fields')
(TAG, CHAR, ELMO, BERT) = (None, None, None, None)
if (args.encoder != 'lstm'):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
WORD = SubwordField('words', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
WORD.vocab = t.get_vocab()
else:
WORD = Field('words', pad=PAD, unk=UNK, bos=BOS, lower=True)
if ('tag' in args.feat):
TAG = Field('tags', bos=BOS)
if ('char' in args.feat):
CHAR = SubwordField('chars', pad=PAD, unk=UNK, bos=BOS, fix_len=args.fix_len)
if ('elmo' in args.feat):
from allennlp.modules.elmo import batch_to_ids
ELMO = RawField('elmo')
ELMO.compose = (lambda x: batch_to_ids(x).to(WORD.device))
if ('bert' in args.feat):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
BERT = SubwordField('bert', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
BERT.vocab = t.get_vocab()
TEXT = RawField('texts')
ARC = Field('arcs', bos=BOS, use_vocab=False, fn=CoNLL.get_arcs)
REL = Field('rels', bos=BOS)
transform = CoNLL(FORM=(WORD, TEXT, CHAR, ELMO, BERT), CPOS=TAG, HEAD=ARC, DEPREL=REL)
train = Dataset(transform, args.train)
if (args.encoder == 'lstm'):
WORD.build(train, args.min_freq, (Embedding.load(args.embed, args.unk) if args.embed else None))
if (TAG is not None):
TAG.build(train)
if (CHAR is not None):
CHAR.build(train)
REL.build(train)
args.update({'n_words': (len(WORD.vocab) if (args.encoder != 'lstm') else WORD.vocab.n_init), 'n_rels': len(REL.vocab), 'n_tags': (len(TAG.vocab) if (TAG is not None) else None), 'n_chars': (len(CHAR.vocab) if (CHAR is not None) else None), 'char_pad_index': (CHAR.pad_index if (CHAR is not None) else None), 'bert_pad_index': (BERT.pad_index if (BERT is not None) else None), 'pad_index': WORD.pad_index, 'unk_index': WORD.unk_index, 'bos_index': WORD.bos_index})
logger.info(f'{transform}')
logger.info('Building the model')
model = cls.MODEL(**args).load_pretrained((WORD.embed if hasattr(WORD, 'embed') else None)).to(args.device)
logger.info(f'''{model}
''')
return cls(args, model, transform) | 8,270,736,086,687,907,000 | Build a brand-new Parser, including initialization of all data fields and model parameters.
Args:
path (str):
The path of the model to be saved.
min_freq (str):
The minimum frequency needed to include a token in the vocabulary.
Required if taking words as encoder input.
Default: 2.
fix_len (int):
The max length of all subword pieces. The excess part of each piece will be truncated.
Required if using CharLSTM/BERT.
Default: 20.
kwargs (dict):
A dict holding the unconsumed arguments. | supar/parsers/dep.py | build | LiBinNLP/HOSDP | python | @classmethod
def build(cls, path, min_freq=2, fix_len=20, **kwargs):
'\n Build a brand-new Parser, including initialization of all data fields and model parameters.\n\n Args:\n path (str):\n The path of the model to be saved.\n min_freq (str):\n The minimum frequency needed to include a token in the vocabulary.\n Required if taking words as encoder input.\n Default: 2.\n fix_len (int):\n The max length of all subword pieces. The excess part of each piece will be truncated.\n Required if using CharLSTM/BERT.\n Default: 20.\n kwargs (dict):\n A dict holding the unconsumed arguments.\n '
args = Config(**locals())
args.device = ('cuda' if torch.cuda.is_available() else 'cpu')
os.makedirs((os.path.dirname(path) or './'), exist_ok=True)
if (os.path.exists(path) and (not args.build)):
parser = cls.load(**args)
parser.model = cls.MODEL(**parser.args)
parser.model.load_pretrained(parser.WORD.embed).to(args.device)
return parser
logger.info('Building the fields')
(TAG, CHAR, ELMO, BERT) = (None, None, None, None)
if (args.encoder != 'lstm'):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
WORD = SubwordField('words', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
WORD.vocab = t.get_vocab()
else:
WORD = Field('words', pad=PAD, unk=UNK, bos=BOS, lower=True)
if ('tag' in args.feat):
TAG = Field('tags', bos=BOS)
if ('char' in args.feat):
CHAR = SubwordField('chars', pad=PAD, unk=UNK, bos=BOS, fix_len=args.fix_len)
if ('elmo' in args.feat):
from allennlp.modules.elmo import batch_to_ids
ELMO = RawField('elmo')
ELMO.compose = (lambda x: batch_to_ids(x).to(WORD.device))
if ('bert' in args.feat):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
BERT = SubwordField('bert', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
BERT.vocab = t.get_vocab()
TEXT = RawField('texts')
ARC = Field('arcs', bos=BOS, use_vocab=False, fn=CoNLL.get_arcs)
REL = Field('rels', bos=BOS)
transform = CoNLL(FORM=(WORD, TEXT, CHAR, ELMO, BERT), CPOS=TAG, HEAD=ARC, DEPREL=REL)
train = Dataset(transform, args.train)
if (args.encoder == 'lstm'):
WORD.build(train, args.min_freq, (Embedding.load(args.embed, args.unk) if args.embed else None))
if (TAG is not None):
TAG.build(train)
if (CHAR is not None):
CHAR.build(train)
REL.build(train)
args.update({'n_words': (len(WORD.vocab) if (args.encoder != 'lstm') else WORD.vocab.n_init), 'n_rels': len(REL.vocab), 'n_tags': (len(TAG.vocab) if (TAG is not None) else None), 'n_chars': (len(CHAR.vocab) if (CHAR is not None) else None), 'char_pad_index': (CHAR.pad_index if (CHAR is not None) else None), 'bert_pad_index': (BERT.pad_index if (BERT is not None) else None), 'pad_index': WORD.pad_index, 'unk_index': WORD.unk_index, 'bos_index': WORD.bos_index})
logger.info(f'{transform}')
logger.info('Building the model')
model = cls.MODEL(**args).load_pretrained((WORD.embed if hasattr(WORD, 'embed') else None)).to(args.device)
logger.info(f'{model}
')
return cls(args, model, transform) |
def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, mbr=True, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) | 4,529,348,555,688,951,000 | Args:
train/dev/test (list[list] or str):
Filenames of the train/dev/test datasets.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
update_steps (int):
Gradient accumulation steps. Default: 1.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
mbr (bool):
If ``True``, performs MBR decoding. Default: ``True``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs. | supar/parsers/dep.py | train | LiBinNLP/HOSDP | python | def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, mbr=True, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) |
def evaluate(self, data, buckets=8, batch_size=5000, punct=False, mbr=True, tree=True, proj=True, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) | -7,514,356,962,041,115,000 | Args:
data (str):
The data for evaluation, both list of instances and filename are allowed.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
mbr (bool):
If ``True``, performs MBR decoding. Default: ``True``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating evaluation configs.
Returns:
The loss scalar and evaluation results. | supar/parsers/dep.py | evaluate | LiBinNLP/HOSDP | python | def evaluate(self, data, buckets=8, batch_size=5000, punct=False, mbr=True, tree=True, proj=True, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) |
def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, mbr=True, tree=True, proj=True, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) | 3,519,811,644,094,867,000 | Args:
data (list[list] or str):
The data for prediction, both a list of instances and filename are allowed.
pred (str):
If specified, the predicted results will be saved to the file. Default: ``None``.
lang (str):
Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.
``None`` if tokenization is not required.
Default: ``None``.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
prob (bool):
If ``True``, outputs the probabilities. Default: ``False``.
mbr (bool):
If ``True``, performs MBR decoding. Default: ``True``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating prediction configs.
Returns:
A :class:`~supar.utils.Dataset` object that stores the predicted results. | supar/parsers/dep.py | predict | LiBinNLP/HOSDP | python | def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, mbr=True, tree=True, proj=True, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) |
@classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'crf-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('crf-dep-en')\n >>> parser = Parser.load('./ptb.crf.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) | -8,849,996,489,005,211,000 | Loads a parser with data fields and pretrained model parameters.
Args:
path (str):
- a string with the shortcut name of a pretrained model defined in ``supar.MODEL``
to load from cache or download, e.g., ``'crf-dep-en'``.
- a local path to a pretrained model, e.g., ``./<path>/model``.
reload (bool):
Whether to discard the existing cache and force a fresh download. Default: ``False``.
src (str):
Specifies where to download the model.
``'github'``: github release page.
``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).
Default: None.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs and initializing the model.
Examples:
>>> from supar import Parser
>>> parser = Parser.load('crf-dep-en')
>>> parser = Parser.load('./ptb.crf.dep.lstm.char') | supar/parsers/dep.py | load | LiBinNLP/HOSDP | python | @classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'crf-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('crf-dep-en')\n >>> parser = Parser.load('./ptb.crf.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) |
def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, mbr=True, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) | 4,529,348,555,688,951,000 | Args:
train/dev/test (list[list] or str):
Filenames of the train/dev/test datasets.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
update_steps (int):
Gradient accumulation steps. Default: 1.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
mbr (bool):
If ``True``, performs MBR decoding. Default: ``True``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs. | supar/parsers/dep.py | train | LiBinNLP/HOSDP | python | def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, mbr=True, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) |
def evaluate(self, data, buckets=8, batch_size=5000, punct=False, mbr=True, tree=True, proj=True, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) | -7,514,356,962,041,115,000 | Args:
data (str):
The data for evaluation, both list of instances and filename are allowed.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
mbr (bool):
If ``True``, performs MBR decoding. Default: ``True``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating evaluation configs.
Returns:
The loss scalar and evaluation results. | supar/parsers/dep.py | evaluate | LiBinNLP/HOSDP | python | def evaluate(self, data, buckets=8, batch_size=5000, punct=False, mbr=True, tree=True, proj=True, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) |
def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, mbr=True, tree=True, proj=True, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) | 3,519,811,644,094,867,000 | Args:
data (list[list] or str):
The data for prediction, both a list of instances and filename are allowed.
pred (str):
If specified, the predicted results will be saved to the file. Default: ``None``.
lang (str):
Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.
``None`` if tokenization is not required.
Default: ``None``.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
prob (bool):
If ``True``, outputs the probabilities. Default: ``False``.
mbr (bool):
If ``True``, performs MBR decoding. Default: ``True``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating prediction configs.
Returns:
A :class:`~supar.utils.Dataset` object that stores the predicted results. | supar/parsers/dep.py | predict | LiBinNLP/HOSDP | python | def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, mbr=True, tree=True, proj=True, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n mbr (bool):\n If ``True``, performs MBR decoding. Default: ``True``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) |
@classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'crf2o-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('crf2o-dep-en')\n >>> parser = Parser.load('./ptb.crf2o.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) | -8,496,512,337,751,790,000 | Loads a parser with data fields and pretrained model parameters.
Args:
path (str):
- a string with the shortcut name of a pretrained model defined in ``supar.MODEL``
to load from cache or download, e.g., ``'crf2o-dep-en'``.
- a local path to a pretrained model, e.g., ``./<path>/model``.
reload (bool):
Whether to discard the existing cache and force a fresh download. Default: ``False``.
src (str):
Specifies where to download the model.
``'github'``: github release page.
``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).
Default: None.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs and initializing the model.
Examples:
>>> from supar import Parser
>>> parser = Parser.load('crf2o-dep-en')
>>> parser = Parser.load('./ptb.crf2o.dep.lstm.char') | supar/parsers/dep.py | load | LiBinNLP/HOSDP | python | @classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'crf2o-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('crf2o-dep-en')\n >>> parser = Parser.load('./ptb.crf2o.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) |
@classmethod
def build(cls, path, min_freq=2, fix_len=20, **kwargs):
'\n Build a brand-new Parser, including initialization of all data fields and model parameters.\n\n Args:\n path (str):\n The path of the model to be saved.\n min_freq (str):\n The minimum frequency needed to include a token in the vocabulary. Default: 2.\n fix_len (int):\n The max length of all subword pieces. The excess part of each piece will be truncated.\n Required if using CharLSTM/BERT.\n Default: 20.\n kwargs (dict):\n A dict holding the unconsumed arguments.\n '
args = Config(**locals())
args.device = ('cuda' if torch.cuda.is_available() else 'cpu')
os.makedirs((os.path.dirname(path) or './'), exist_ok=True)
if (os.path.exists(path) and (not args.build)):
parser = cls.load(**args)
parser.model = cls.MODEL(**parser.args)
parser.model.load_pretrained(parser.WORD.embed).to(args.device)
return parser
logger.info('Building the fields')
(TAG, CHAR, ELMO, BERT) = (None, None, None, None)
if (args.encoder != 'lstm'):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
WORD = SubwordField('words', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
WORD.vocab = t.get_vocab()
else:
WORD = Field('words', pad=PAD, unk=UNK, bos=BOS, lower=True)
if ('tag' in args.feat):
TAG = Field('tags', bos=BOS)
if ('char' in args.feat):
CHAR = SubwordField('chars', pad=PAD, unk=UNK, bos=BOS, fix_len=args.fix_len)
if ('elmo' in args.feat):
from allennlp.modules.elmo import batch_to_ids
ELMO = RawField('elmo')
ELMO.compose = (lambda x: batch_to_ids(x).to(WORD.device))
if ('bert' in args.feat):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
BERT = SubwordField('bert', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
BERT.vocab = t.get_vocab()
TEXT = RawField('texts')
ARC = Field('arcs', bos=BOS, use_vocab=False, fn=CoNLL.get_arcs)
SIB = ChartField('sibs', bos=BOS, use_vocab=False, fn=CoNLL.get_sibs)
REL = Field('rels', bos=BOS)
transform = CoNLL(FORM=(WORD, TEXT, CHAR, ELMO, BERT), CPOS=TAG, HEAD=(ARC, SIB), DEPREL=REL)
train = Dataset(transform, args.train)
if (args.encoder == 'lstm'):
WORD.build(train, args.min_freq, (Embedding.load(args.embed, args.unk) if args.embed else None))
if (TAG is not None):
TAG.build(train)
if (CHAR is not None):
CHAR.build(train)
REL.build(train)
args.update({'n_words': (len(WORD.vocab) if (args.encoder != 'lstm') else WORD.vocab.n_init), 'n_rels': len(REL.vocab), 'n_tags': (len(TAG.vocab) if (TAG is not None) else None), 'n_chars': (len(CHAR.vocab) if (CHAR is not None) else None), 'char_pad_index': (CHAR.pad_index if (CHAR is not None) else None), 'bert_pad_index': (BERT.pad_index if (BERT is not None) else None), 'pad_index': WORD.pad_index, 'unk_index': WORD.unk_index, 'bos_index': WORD.bos_index})
logger.info(f'{transform}')
logger.info('Building the model')
model = cls.MODEL(**args).load_pretrained((WORD.embed if hasattr(WORD, 'embed') else None)).to(args.device)
logger.info(f'''{model}
''')
return cls(args, model, transform) | 1,164,278,246,627,036,400 | Build a brand-new Parser, including initialization of all data fields and model parameters.
Args:
path (str):
The path of the model to be saved.
min_freq (str):
The minimum frequency needed to include a token in the vocabulary. Default: 2.
fix_len (int):
The max length of all subword pieces. The excess part of each piece will be truncated.
Required if using CharLSTM/BERT.
Default: 20.
kwargs (dict):
A dict holding the unconsumed arguments. | supar/parsers/dep.py | build | LiBinNLP/HOSDP | python | @classmethod
def build(cls, path, min_freq=2, fix_len=20, **kwargs):
'\n Build a brand-new Parser, including initialization of all data fields and model parameters.\n\n Args:\n path (str):\n The path of the model to be saved.\n min_freq (str):\n The minimum frequency needed to include a token in the vocabulary. Default: 2.\n fix_len (int):\n The max length of all subword pieces. The excess part of each piece will be truncated.\n Required if using CharLSTM/BERT.\n Default: 20.\n kwargs (dict):\n A dict holding the unconsumed arguments.\n '
args = Config(**locals())
args.device = ('cuda' if torch.cuda.is_available() else 'cpu')
os.makedirs((os.path.dirname(path) or './'), exist_ok=True)
if (os.path.exists(path) and (not args.build)):
parser = cls.load(**args)
parser.model = cls.MODEL(**parser.args)
parser.model.load_pretrained(parser.WORD.embed).to(args.device)
return parser
logger.info('Building the fields')
(TAG, CHAR, ELMO, BERT) = (None, None, None, None)
if (args.encoder != 'lstm'):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
WORD = SubwordField('words', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
WORD.vocab = t.get_vocab()
else:
WORD = Field('words', pad=PAD, unk=UNK, bos=BOS, lower=True)
if ('tag' in args.feat):
TAG = Field('tags', bos=BOS)
if ('char' in args.feat):
CHAR = SubwordField('chars', pad=PAD, unk=UNK, bos=BOS, fix_len=args.fix_len)
if ('elmo' in args.feat):
from allennlp.modules.elmo import batch_to_ids
ELMO = RawField('elmo')
ELMO.compose = (lambda x: batch_to_ids(x).to(WORD.device))
if ('bert' in args.feat):
from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
t = AutoTokenizer.from_pretrained(args.bert)
BERT = SubwordField('bert', pad=t.pad_token, unk=t.unk_token, bos=(t.bos_token or t.cls_token), fix_len=args.fix_len, tokenize=t.tokenize, fn=(None if (not isinstance(t, (GPT2Tokenizer, GPT2TokenizerFast))) else (lambda x: (' ' + x))))
BERT.vocab = t.get_vocab()
TEXT = RawField('texts')
ARC = Field('arcs', bos=BOS, use_vocab=False, fn=CoNLL.get_arcs)
SIB = ChartField('sibs', bos=BOS, use_vocab=False, fn=CoNLL.get_sibs)
REL = Field('rels', bos=BOS)
transform = CoNLL(FORM=(WORD, TEXT, CHAR, ELMO, BERT), CPOS=TAG, HEAD=(ARC, SIB), DEPREL=REL)
train = Dataset(transform, args.train)
if (args.encoder == 'lstm'):
WORD.build(train, args.min_freq, (Embedding.load(args.embed, args.unk) if args.embed else None))
if (TAG is not None):
TAG.build(train)
if (CHAR is not None):
CHAR.build(train)
REL.build(train)
args.update({'n_words': (len(WORD.vocab) if (args.encoder != 'lstm') else WORD.vocab.n_init), 'n_rels': len(REL.vocab), 'n_tags': (len(TAG.vocab) if (TAG is not None) else None), 'n_chars': (len(CHAR.vocab) if (CHAR is not None) else None), 'char_pad_index': (CHAR.pad_index if (CHAR is not None) else None), 'bert_pad_index': (BERT.pad_index if (BERT is not None) else None), 'pad_index': WORD.pad_index, 'unk_index': WORD.unk_index, 'bos_index': WORD.bos_index})
logger.info(f'{transform}')
logger.info('Building the model')
model = cls.MODEL(**args).load_pretrained((WORD.embed if hasattr(WORD, 'embed') else None)).to(args.device)
logger.info(f'{model}
')
return cls(args, model, transform) |
def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) | -7,717,226,299,015,962,000 | Args:
train/dev/test (list[list] or str):
Filenames of the train/dev/test datasets.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
update_steps (int):
Gradient accumulation steps. Default: 1.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs. | supar/parsers/dep.py | train | LiBinNLP/HOSDP | python | def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, tree=False, proj=False, partial=False, verbose=True, **kwargs):
'\n Args:\n train/dev/test (list[list] or str):\n Filenames of the train/dev/test datasets.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n update_steps (int):\n Gradient accumulation steps. Default: 1.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs.\n '
return super().train(**Config().update(locals())) |
def evaluate(self, data, buckets=8, batch_size=5000, punct=False, tree=True, proj=True, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) | -1,705,285,004,826,690,300 | Args:
data (str):
The data for evaluation, both list of instances and filename are allowed.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
punct (bool):
If ``False``, ignores the punctuation during evaluation. Default: ``False``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
partial (bool):
``True`` denotes the trees are partially annotated. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating evaluation configs.
Returns:
The loss scalar and evaluation results. | supar/parsers/dep.py | evaluate | LiBinNLP/HOSDP | python | def evaluate(self, data, buckets=8, batch_size=5000, punct=False, tree=True, proj=True, partial=False, verbose=True, **kwargs):
'\n Args:\n data (str):\n The data for evaluation, both list of instances and filename are allowed.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n punct (bool):\n If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n partial (bool):\n ``True`` denotes the trees are partially annotated. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating evaluation configs.\n\n Returns:\n The loss scalar and evaluation results.\n '
return super().evaluate(**Config().update(locals())) |
def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, tree=True, proj=True, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) | 606,635,800,211,581,400 | Args:
data (list[list] or str):
The data for prediction, both a list of instances and filename are allowed.
pred (str):
If specified, the predicted results will be saved to the file. Default: ``None``.
lang (str):
Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.
``None`` if tokenization is not required.
Default: ``None``.
buckets (int):
The number of buckets that sentences are assigned to. Default: 32.
batch_size (int):
The number of tokens in each batch. Default: 5000.
prob (bool):
If ``True``, outputs the probabilities. Default: ``False``.
tree (bool):
If ``True``, ensures to output well-formed trees. Default: ``False``.
proj (bool):
If ``True``, ensures to output projective trees. Default: ``False``.
verbose (bool):
If ``True``, increases the output verbosity. Default: ``True``.
kwargs (dict):
A dict holding unconsumed arguments for updating prediction configs.
Returns:
A :class:`~supar.utils.Dataset` object that stores the predicted results. | supar/parsers/dep.py | predict | LiBinNLP/HOSDP | python | def predict(self, data, pred=None, lang=None, buckets=8, batch_size=5000, prob=False, tree=True, proj=True, verbose=True, **kwargs):
'\n Args:\n data (list[list] or str):\n The data for prediction, both a list of instances and filename are allowed.\n pred (str):\n If specified, the predicted results will be saved to the file. Default: ``None``.\n lang (str):\n Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.\n ``None`` if tokenization is not required.\n Default: ``None``.\n buckets (int):\n The number of buckets that sentences are assigned to. Default: 32.\n batch_size (int):\n The number of tokens in each batch. Default: 5000.\n prob (bool):\n If ``True``, outputs the probabilities. Default: ``False``.\n tree (bool):\n If ``True``, ensures to output well-formed trees. Default: ``False``.\n proj (bool):\n If ``True``, ensures to output projective trees. Default: ``False``.\n verbose (bool):\n If ``True``, increases the output verbosity. Default: ``True``.\n kwargs (dict):\n A dict holding unconsumed arguments for updating prediction configs.\n\n Returns:\n A :class:`~supar.utils.Dataset` object that stores the predicted results.\n '
return super().predict(**Config().update(locals())) |
@classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'vi-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('vi-dep-en')\n >>> parser = Parser.load('./ptb.vi.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) | -6,535,850,461,008,373,000 | Loads a parser with data fields and pretrained model parameters.
Args:
path (str):
- a string with the shortcut name of a pretrained model defined in ``supar.MODEL``
to load from cache or download, e.g., ``'vi-dep-en'``.
- a local path to a pretrained model, e.g., ``./<path>/model``.
reload (bool):
Whether to discard the existing cache and force a fresh download. Default: ``False``.
src (str):
Specifies where to download the model.
``'github'``: github release page.
``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).
Default: None.
kwargs (dict):
A dict holding unconsumed arguments for updating training configs and initializing the model.
Examples:
>>> from supar import Parser
>>> parser = Parser.load('vi-dep-en')
>>> parser = Parser.load('./ptb.vi.dep.lstm.char') | supar/parsers/dep.py | load | LiBinNLP/HOSDP | python | @classmethod
def load(cls, path, reload=False, src=None, **kwargs):
"\n Loads a parser with data fields and pretrained model parameters.\n\n Args:\n path (str):\n - a string with the shortcut name of a pretrained model defined in ``supar.MODEL``\n to load from cache or download, e.g., ``'vi-dep-en'``.\n - a local path to a pretrained model, e.g., ``./<path>/model``.\n reload (bool):\n Whether to discard the existing cache and force a fresh download. Default: ``False``.\n src (str):\n Specifies where to download the model.\n ``'github'``: github release page.\n ``'hlt'``: hlt homepage, only accessible from 9:00 to 18:00 (UTC+8).\n Default: None.\n kwargs (dict):\n A dict holding unconsumed arguments for updating training configs and initializing the model.\n\n Examples:\n >>> from supar import Parser\n >>> parser = Parser.load('vi-dep-en')\n >>> parser = Parser.load('./ptb.vi.dep.lstm.char')\n "
return super().load(path, reload, src, **kwargs) |
def parse_arguments():
'Argument parsing for the script'
parser = argparse.ArgumentParser(description='Liftbridge sub script.')
parser.add_argument('subject', metavar='subject')
parser.add_argument('stream', metavar='stream')
parser.add_argument('-s', '--server', metavar='s', nargs='?', default='127.0.0.1:9292', help='(default: %(default)s)')
parser.add_argument('-t', '--timestamp', action='store_true', help='Display timestamps')
parser.add_argument('-c', '--create', action='store_true', help="Creates the stream in case it doesn't exist")
parser.add_argument('-d', '--debug', action='store_true', help='Shows debug logs')
return parser.parse_args() | 2,814,873,715,566,704,000 | Argument parsing for the script | examples/lift-sub.py | parse_arguments | LaPetiteSouris/python-liftbridge | python | def parse_arguments():
parser = argparse.ArgumentParser(description='Liftbridge sub script.')
parser.add_argument('subject', metavar='subject')
parser.add_argument('stream', metavar='stream')
parser.add_argument('-s', '--server', metavar='s', nargs='?', default='127.0.0.1:9292', help='(default: %(default)s)')
parser.add_argument('-t', '--timestamp', action='store_true', help='Display timestamps')
parser.add_argument('-c', '--create', action='store_true', help="Creates the stream in case it doesn't exist")
parser.add_argument('-d', '--debug', action='store_true', help='Shows debug logs')
return parser.parse_args() |
def __init__(__self__, resource_name, opts=None, instance_ports=None, load_balancer=None, __name__=None, __opts__=None):
'\n Provides a proxy protocol policy, which allows an ELB to carry a client connection information to a backend.\n \n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[list] instance_ports: List of instance ports to which the policy\n should be applied. This can be specified if the protocol is SSL or TCP.\n :param pulumi.Input[str] load_balancer: The load balancer to which the policy\n should be attached.\n '
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (not resource_name):
raise TypeError('Missing resource name argument (for URN creation)')
if (not isinstance(resource_name, str)):
raise TypeError('Expected resource name to be a string')
if (opts and (not isinstance(opts, pulumi.ResourceOptions))):
raise TypeError('Expected resource options to be a ResourceOptions instance')
__props__ = dict()
if (instance_ports is None):
raise TypeError('Missing required property instance_ports')
__props__['instance_ports'] = instance_ports
if (load_balancer is None):
raise TypeError('Missing required property load_balancer')
__props__['load_balancer'] = load_balancer
super(ProxyProtocolPolicy, __self__).__init__('aws:ec2/proxyProtocolPolicy:ProxyProtocolPolicy', resource_name, __props__, opts) | 6,222,031,625,073,402,000 | Provides a proxy protocol policy, which allows an ELB to carry a client connection information to a backend.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[list] instance_ports: List of instance ports to which the policy
should be applied. This can be specified if the protocol is SSL or TCP.
:param pulumi.Input[str] load_balancer: The load balancer to which the policy
should be attached. | sdk/python/pulumi_aws/ec2/proxy_protocol_policy.py | __init__ | lemonade-hq/pulumi-aws | python | def __init__(__self__, resource_name, opts=None, instance_ports=None, load_balancer=None, __name__=None, __opts__=None):
'\n Provides a proxy protocol policy, which allows an ELB to carry a client connection information to a backend.\n \n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[list] instance_ports: List of instance ports to which the policy\n should be applied. This can be specified if the protocol is SSL or TCP.\n :param pulumi.Input[str] load_balancer: The load balancer to which the policy\n should be attached.\n '
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (not resource_name):
raise TypeError('Missing resource name argument (for URN creation)')
if (not isinstance(resource_name, str)):
raise TypeError('Expected resource name to be a string')
if (opts and (not isinstance(opts, pulumi.ResourceOptions))):
raise TypeError('Expected resource options to be a ResourceOptions instance')
__props__ = dict()
if (instance_ports is None):
raise TypeError('Missing required property instance_ports')
__props__['instance_ports'] = instance_ports
if (load_balancer is None):
raise TypeError('Missing required property load_balancer')
__props__['load_balancer'] = load_balancer
super(ProxyProtocolPolicy, __self__).__init__('aws:ec2/proxyProtocolPolicy:ProxyProtocolPolicy', resource_name, __props__, opts) |
def q_shift_variants(q_values_prediction, q_values_input, corrected_reflectivity, n_variants, scale=0.001):
'Create ``n_variants`` interpolated reflectivity curve variants with randomly distributed q shifts.'
shift = np.random.normal(loc=0, size=n_variants, scale=scale).reshape(n_variants, 1)
shifted_qs = (np.tile(q_values_input, (n_variants, 1)) + shift)
interpolated_curves = np.zeros((n_variants, len(q_values_prediction)))
for i in range(n_variants):
interpolated_curves[i] = interp_reflectivity(q_values_prediction, shifted_qs[i], corrected_reflectivity)
return (interpolated_curves, shift) | -2,858,009,355,907,476,500 | Create ``n_variants`` interpolated reflectivity curve variants with randomly distributed q shifts. | mlreflect/curve_fitter/minimizer.py | q_shift_variants | schreiber-lab/mlreflect | python | def q_shift_variants(q_values_prediction, q_values_input, corrected_reflectivity, n_variants, scale=0.001):
shift = np.random.normal(loc=0, size=n_variants, scale=scale).reshape(n_variants, 1)
shifted_qs = (np.tile(q_values_input, (n_variants, 1)) + shift)
interpolated_curves = np.zeros((n_variants, len(q_values_prediction)))
for i in range(n_variants):
interpolated_curves[i] = interp_reflectivity(q_values_prediction, shifted_qs[i], corrected_reflectivity)
return (interpolated_curves, shift) |
def curve_scaling_variants(corrected_reflectivity, n_variants, scale=0.1):
'Create ``n_variants`` reflectivity curve variants with randomly distributed scaling factors.'
scalings = np.random.normal(loc=1, size=n_variants, scale=scale).reshape(n_variants, 1)
scaled_curves = np.zeros((n_variants, len(corrected_reflectivity)))
for i in range(n_variants):
scaled_curves[i] = (corrected_reflectivity.copy() * scalings[i])
return (scaled_curves, scalings) | -7,762,106,858,442,012,000 | Create ``n_variants`` reflectivity curve variants with randomly distributed scaling factors. | mlreflect/curve_fitter/minimizer.py | curve_scaling_variants | schreiber-lab/mlreflect | python | def curve_scaling_variants(corrected_reflectivity, n_variants, scale=0.1):
scalings = np.random.normal(loc=1, size=n_variants, scale=scale).reshape(n_variants, 1)
scaled_curves = np.zeros((n_variants, len(corrected_reflectivity)))
for i in range(n_variants):
scaled_curves[i] = (corrected_reflectivity.copy() * scalings[i])
return (scaled_curves, scalings) |
def curve_variant_log_mse(curve, variant_curves):
'Calculate the log MSE of a curve and a :class:`ndarray` of curves'
errors = (np.log10(curve) - np.log10(variant_curves))
return np.mean((errors ** 2), axis=1) | 8,469,554,744,767,416,000 | Calculate the log MSE of a curve and a :class:`ndarray` of curves | mlreflect/curve_fitter/minimizer.py | curve_variant_log_mse | schreiber-lab/mlreflect | python | def curve_variant_log_mse(curve, variant_curves):
errors = (np.log10(curve) - np.log10(variant_curves))
return np.mean((errors ** 2), axis=1) |
def least_log_mean_squares_fit(q_values, data, predicted_labels, sample, output_preprocessor, fraction_bounds=(0.5, 0.5, 0.1)):
'Fits the data with a model curve with ``scipy.optimize.curve_fit`` using ``predicted_labels`` as start values.'
prep_labels = output_preprocessor.apply_preprocessing(predicted_labels)[0]
start_values = np.array(prep_labels)[0]
bounds = ([(val - (bound * abs(val))) for (val, bound) in zip(start_values, fraction_bounds)], [(val + (bound * abs(val))) for (val, bound) in zip(start_values, fraction_bounds)])
fit_result = curve_fit(fitting_model(q_values, sample, output_preprocessor), q_values, np.log10(data), p0=start_values, bounds=bounds)
return output_preprocessor.restore_labels(np.atleast_2d(fit_result[0])) | -8,441,526,859,497,473,000 | Fits the data with a model curve with ``scipy.optimize.curve_fit`` using ``predicted_labels`` as start values. | mlreflect/curve_fitter/minimizer.py | least_log_mean_squares_fit | schreiber-lab/mlreflect | python | def least_log_mean_squares_fit(q_values, data, predicted_labels, sample, output_preprocessor, fraction_bounds=(0.5, 0.5, 0.1)):
prep_labels = output_preprocessor.apply_preprocessing(predicted_labels)[0]
start_values = np.array(prep_labels)[0]
bounds = ([(val - (bound * abs(val))) for (val, bound) in zip(start_values, fraction_bounds)], [(val + (bound * abs(val))) for (val, bound) in zip(start_values, fraction_bounds)])
fit_result = curve_fit(fitting_model(q_values, sample, output_preprocessor), q_values, np.log10(data), p0=start_values, bounds=bounds)
return output_preprocessor.restore_labels(np.atleast_2d(fit_result[0])) |
def log_mse_loss(prep_labels, data, generator, output_preprocessor):
'MSE loss between a reflectivity curve and a model curve generated with the given normalized labels.'
restored_labels = output_preprocessor.restore_labels(np.atleast_2d(prep_labels))
model = generator.simulate_reflectivity(restored_labels, progress_bar=False)[0]
loss = mean_squared_error(np.log10(data), np.log10(model))
return loss | 2,051,504,903,990,957,800 | MSE loss between a reflectivity curve and a model curve generated with the given normalized labels. | mlreflect/curve_fitter/minimizer.py | log_mse_loss | schreiber-lab/mlreflect | python | def log_mse_loss(prep_labels, data, generator, output_preprocessor):
restored_labels = output_preprocessor.restore_labels(np.atleast_2d(prep_labels))
model = generator.simulate_reflectivity(restored_labels, progress_bar=False)[0]
loss = mean_squared_error(np.log10(data), np.log10(model))
return loss |
def mean_squared_error(array1, array2):
'Returns element-wise mean squared error between two arrays.'
if (len(array1) != len(array2)):
raise ValueError(f'array1 and array2 must be of same length ({len(array1)} != {len(array2)})')
else:
error = (np.asarray(array1) - np.asarray(array2))
return np.mean(np.atleast_2d((error ** 2)), axis=1) | 2,385,236,979,828,822,000 | Returns element-wise mean squared error between two arrays. | mlreflect/curve_fitter/minimizer.py | mean_squared_error | schreiber-lab/mlreflect | python | def mean_squared_error(array1, array2):
if (len(array1) != len(array2)):
raise ValueError(f'array1 and array2 must be of same length ({len(array1)} != {len(array2)})')
else:
error = (np.asarray(array1) - np.asarray(array2))
return np.mean(np.atleast_2d((error ** 2)), axis=1) |
@hapic.with_api_doc()
@hapic.output_body(AboutSchema())
def about(self):
'\n This endpoint allow to check that the API is running. This description\n is generated from the docstring of the method.\n '
return {'version': '1.2.3', 'datetime': datetime.now()} | 5,390,935,259,571,241,000 | This endpoint allow to check that the API is running. This description
is generated from the docstring of the method. | example/usermanagement/serve_flask_marshmallow.py | about | algoo/hapic | python | @hapic.with_api_doc()
@hapic.output_body(AboutSchema())
def about(self):
'\n This endpoint allow to check that the API is running. This description\n is generated from the docstring of the method.\n '
return {'version': '1.2.3', 'datetime': datetime.now()} |
@hapic.with_api_doc()
@hapic.output_body(UserDigestSchema(many=True))
def get_users(self):
'\n Obtain users list.\n '
return UserLib().get_users() | -3,739,341,336,160,205,000 | Obtain users list. | example/usermanagement/serve_flask_marshmallow.py | get_users | algoo/hapic | python | @hapic.with_api_doc()
@hapic.output_body(UserDigestSchema(many=True))
def get_users(self):
'\n \n '
return UserLib().get_users() |
@hapic.with_api_doc()
@hapic.handle_exception(UserNotFound, HTTPStatus.NOT_FOUND)
@hapic.input_path(UserIdPathSchema())
@hapic.output_body(UserSchema())
def get_user(self, id, hapic_data: HapicData):
'\n Return a user taken from the list or return a 404\n '
return UserLib().get_user(int(hapic_data.path['id'])) | -8,173,223,262,207,807,000 | Return a user taken from the list or return a 404 | example/usermanagement/serve_flask_marshmallow.py | get_user | algoo/hapic | python | @hapic.with_api_doc()
@hapic.handle_exception(UserNotFound, HTTPStatus.NOT_FOUND)
@hapic.input_path(UserIdPathSchema())
@hapic.output_body(UserSchema())
def get_user(self, id, hapic_data: HapicData):
'\n \n '
return UserLib().get_user(int(hapic_data.path['id'])) |
@hapic.with_api_doc()
@hapic.input_body(UserSchema(exclude=('id',)))
@hapic.output_body(UserSchema())
def add_user(self, hapic_data: HapicData):
'\n Add a user to the list\n '
new_user = User(**hapic_data.body)
return UserLib().add_user(new_user) | 2,054,484,460,010,922,200 | Add a user to the list | example/usermanagement/serve_flask_marshmallow.py | add_user | algoo/hapic | python | @hapic.with_api_doc()
@hapic.input_body(UserSchema(exclude=('id',)))
@hapic.output_body(UserSchema())
def add_user(self, hapic_data: HapicData):
'\n \n '
new_user = User(**hapic_data.body)
return UserLib().add_user(new_user) |
def transform_audio(self, audio_segment):
'Add background noise audio.\n\n Note that this is an in-place transformation.\n\n :param audio_segment: Audio segment to add effects to.\n :type audio_segment: AudioSegmenet|SpeechSegment\n '
noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0]
if (noise_json['duration'] < audio_segment.duration):
raise RuntimeError('The duration of sampled noise audio is smaller than the audio segment to add effects to.')
diff_duration = (noise_json['duration'] - audio_segment.duration)
start = self._rng.uniform(0, diff_duration)
end = (start + audio_segment.duration)
noise_segment = AudioSegment.slice_from_file(noise_json['audio_filepath'], start=start, end=end)
snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB)
audio_segment.add_noise(noise_segment, snr_dB, allow_downsampling=True, rng=self._rng) | 515,606,146,558,555,200 | Add background noise audio.
Note that this is an in-place transformation.
:param audio_segment: Audio segment to add effects to.
:type audio_segment: AudioSegmenet|SpeechSegment | deepspeech/frontend/augmentor/noise_perturb.py | transform_audio | qq1440837150/DeepSpeech | python | def transform_audio(self, audio_segment):
'Add background noise audio.\n\n Note that this is an in-place transformation.\n\n :param audio_segment: Audio segment to add effects to.\n :type audio_segment: AudioSegmenet|SpeechSegment\n '
noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0]
if (noise_json['duration'] < audio_segment.duration):
raise RuntimeError('The duration of sampled noise audio is smaller than the audio segment to add effects to.')
diff_duration = (noise_json['duration'] - audio_segment.duration)
start = self._rng.uniform(0, diff_duration)
end = (start + audio_segment.duration)
noise_segment = AudioSegment.slice_from_file(noise_json['audio_filepath'], start=start, end=end)
snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB)
audio_segment.add_noise(noise_segment, snr_dB, allow_downsampling=True, rng=self._rng) |
def test_oc_get_ocp_server_version():
'\n This method get ocp server version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_ocp_server_version() | 1,846,085,871,210,349,300 | This method get ocp server version
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_ocp_server_version | RobertKrawitz/benchmark-runner | python | def test_oc_get_ocp_server_version():
'\n This method get ocp server version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_ocp_server_version() |
def test_oc_get_kata_version():
'\n This method gets the sandboxed containers (kata) version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_kata_version() | 6,685,231,822,196,794,000 | This method gets the sandboxed containers (kata) version
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_kata_version | RobertKrawitz/benchmark-runner | python | def test_oc_get_kata_version():
'\n This method gets the sandboxed containers (kata) version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_kata_version() |
def test_oc_get_cnv_version():
'\n This method get cnv version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_cnv_version() | 924,310,108,413,792,000 | This method get cnv version
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_cnv_version | RobertKrawitz/benchmark-runner | python | def test_oc_get_cnv_version():
'\n This method get cnv version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_cnv_version() |
def test_oc_get_ocs_version():
'\n This method get ocs version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_ocs_version() | 8,321,662,595,867,105,000 | This method get ocs version
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_ocs_version | RobertKrawitz/benchmark-runner | python | def test_oc_get_ocs_version():
'\n This method get ocs version\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_ocs_version() |
def test_oc_get_master_nodes():
'\n This method test get master nodes\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_master_nodes() | -2,892,055,765,142,323,700 | This method test get master nodes
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_master_nodes | RobertKrawitz/benchmark-runner | python | def test_oc_get_master_nodes():
'\n This method test get master nodes\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_master_nodes() |
def test_login():
'\n This method test login\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
assert oc.login() | 8,158,043,458,917,190,000 | This method test login
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_login | RobertKrawitz/benchmark-runner | python | def test_login():
'\n This method test login\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
assert oc.login() |
def test_oc_get_pod_name():
'\n This test run oc get pod by name\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
assert (oc._get_pod_name(pod_name='erererer', namespace=test_environment_variable['namespace']) == '') | -593,645,872,348,187,500 | This test run oc get pod by name
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_pod_name | RobertKrawitz/benchmark-runner | python | def test_oc_get_pod_name():
'\n This test run oc get pod by name\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
assert (oc._get_pod_name(pod_name='erererer', namespace=test_environment_variable['namespace']) == ) |
def test_oc_get_pods():
'\n This test run oc get pods\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
assert oc.get_pods() | -7,074,968,122,280,273,000 | This test run oc get pods
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_get_pods | RobertKrawitz/benchmark-runner | python | def test_oc_get_pods():
'\n This test run oc get pods\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
assert oc.get_pods() |
def test_get_prom_token():
'\n This method return prom token from cluster\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_prom_token() | 4,325,391,949,915,039,000 | This method return prom token from cluster
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_get_prom_token | RobertKrawitz/benchmark-runner | python | def test_get_prom_token():
'\n This method return prom token from cluster\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.get_prom_token() |
def test_is_cnv_installed():
'\n This method check if cnv operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_cnv_installed() | 1,317,381,338,846,734,600 | This method check if cnv operator is installed
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_is_cnv_installed | RobertKrawitz/benchmark-runner | python | def test_is_cnv_installed():
'\n This method check if cnv operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_cnv_installed() |
def test_is_kata_installed():
'\n This method checks if the sandboxed containers (kata) operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_kata_installed() | -48,029,872,576,008,216 | This method checks if the sandboxed containers (kata) operator is installed
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_is_kata_installed | RobertKrawitz/benchmark-runner | python | def test_is_kata_installed():
'\n This method checks if the sandboxed containers (kata) operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_kata_installed() |
def test_is_ocs_installed():
'\n This method check if ocs operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_ocs_installed() | -5,860,578,108,085,043,000 | This method check if ocs operator is installed
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_is_ocs_installed | RobertKrawitz/benchmark-runner | python | def test_is_ocs_installed():
'\n This method check if ocs operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_ocs_installed() |
def test_is_kata_installed():
'\n This method check if kata operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_kata_installed() | -612,831,646,245,680,800 | This method check if kata operator is installed
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_is_kata_installed | RobertKrawitz/benchmark-runner | python | def test_is_kata_installed():
'\n This method check if kata operator is installed\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
assert oc.is_kata_installed() |
def test_oc_exec():
'\n Test that oc exec works\n :return:\n '
test_message = 'I am here'
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
answer = oc.exec(pod_name='prometheus-k8s-0', namespace='openshift-monitoring', container='prometheus', command=f'echo "{test_message}"')
assert (answer == test_message) | 3,037,614,774,960,477,700 | Test that oc exec works
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_oc_exec | RobertKrawitz/benchmark-runner | python | def test_oc_exec():
'\n Test that oc exec works\n :return:\n '
test_message = 'I am here'
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
answer = oc.exec(pod_name='prometheus-k8s-0', namespace='openshift-monitoring', container='prometheus', command=f'echo "{test_message}"')
assert (answer == test_message) |
def test_collect_prometheus():
'\n Test that Prometheus data can be collected. TBD test that data is valid.\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
with tempfile.TemporaryDirectory() as dirname:
snapshot = PrometheusSnapshot(oc=oc, artifacts_path=dirname, verbose=True)
snapshot.prepare_for_snapshot(pre_wait_time=1)
time.sleep(10)
tarball = snapshot.retrieve_snapshot(post_wait_time=1)
assert tarfile.is_tarfile(tarball) | -6,243,749,812,123,490,000 | Test that Prometheus data can be collected. TBD test that data is valid.
:return: | tests/integration/benchmark_runner/common/oc/test_oc_without_operator.py | test_collect_prometheus | RobertKrawitz/benchmark-runner | python | def test_collect_prometheus():
'\n Test that Prometheus data can be collected. TBD test that data is valid.\n :return:\n '
oc = OC(kubeadmin_password=test_environment_variable['kubeadmin_password'])
oc.login()
with tempfile.TemporaryDirectory() as dirname:
snapshot = PrometheusSnapshot(oc=oc, artifacts_path=dirname, verbose=True)
snapshot.prepare_for_snapshot(pre_wait_time=1)
time.sleep(10)
tarball = snapshot.retrieve_snapshot(post_wait_time=1)
assert tarfile.is_tarfile(tarball) |
@property
def splits(self):
'Dictionary of split names and probabilities. Must sum to one.'
raise NotImplementedError() | 401,169,447,897,842,200 | Dictionary of split names and probabilities. Must sum to one. | magenta/models/score2perf/score2perf.py | splits | flyingleafe/magenta | python | @property
def splits(self):
raise NotImplementedError() |
@property
def min_hop_size_seconds(self):
'Minimum hop size in seconds at which to split input performances.'
raise NotImplementedError() | -1,182,965,727,413,683,200 | Minimum hop size in seconds at which to split input performances. | magenta/models/score2perf/score2perf.py | min_hop_size_seconds | flyingleafe/magenta | python | @property
def min_hop_size_seconds(self):
raise NotImplementedError() |
@property
def max_hop_size_seconds(self):
'Maximum hop size in seconds at which to split input performances.'
raise NotImplementedError() | -7,320,718,132,117,424,000 | Maximum hop size in seconds at which to split input performances. | magenta/models/score2perf/score2perf.py | max_hop_size_seconds | flyingleafe/magenta | python | @property
def max_hop_size_seconds(self):
raise NotImplementedError() |
@property
def num_replications(self):
'Number of times entire input performances will be split.'
return 1 | 6,038,184,881,289,907,000 | Number of times entire input performances will be split. | magenta/models/score2perf/score2perf.py | num_replications | flyingleafe/magenta | python | @property
def num_replications(self):
return 1 |
@property
def add_eos_symbol(self):
'Whether to append EOS to encoded performances.'
raise NotImplementedError() | -1,922,712,463,153,412,000 | Whether to append EOS to encoded performances. | magenta/models/score2perf/score2perf.py | add_eos_symbol | flyingleafe/magenta | python | @property
def add_eos_symbol(self):
raise NotImplementedError() |
@property
def absolute_timing(self):
'Whether or not score should use absolute (vs. tempo-relative) timing.'
return False | 8,370,973,809,132,255,000 | Whether or not score should use absolute (vs. tempo-relative) timing. | magenta/models/score2perf/score2perf.py | absolute_timing | flyingleafe/magenta | python | @property
def absolute_timing(self):
return False |
@property
def stretch_factors(self):
'Temporal stretch factors for data augmentation (in datagen).'
return [1.0] | 2,906,986,062,144,383,000 | Temporal stretch factors for data augmentation (in datagen). | magenta/models/score2perf/score2perf.py | stretch_factors | flyingleafe/magenta | python | @property
def stretch_factors(self):
return [1.0] |
@property
def transpose_amounts(self):
'Pitch transposition amounts for data augmentation (in datagen).'
return [0] | -979,399,267,056,224,400 | Pitch transposition amounts for data augmentation (in datagen). | magenta/models/score2perf/score2perf.py | transpose_amounts | flyingleafe/magenta | python | @property
def transpose_amounts(self):
return [0] |
@property
def random_crop_length_in_datagen(self):
'Randomly crop targets to this length in datagen.'
return None | 9,185,185,018,205,633,000 | Randomly crop targets to this length in datagen. | magenta/models/score2perf/score2perf.py | random_crop_length_in_datagen | flyingleafe/magenta | python | @property
def random_crop_length_in_datagen(self):
return None |
@property
def random_crop_in_train(self):
'Whether to randomly crop each training example when preprocessing.'
return False | -3,151,171,822,926,777,300 | Whether to randomly crop each training example when preprocessing. | magenta/models/score2perf/score2perf.py | random_crop_in_train | flyingleafe/magenta | python | @property
def random_crop_in_train(self):
return False |
@property
def split_in_eval(self):
'Whether to split each eval example when preprocessing.'
return False | -2,600,506,686,284,337,000 | Whether to split each eval example when preprocessing. | magenta/models/score2perf/score2perf.py | split_in_eval | flyingleafe/magenta | python | @property
def split_in_eval(self):
return False |
def performances_input_transform(self, tmp_dir):
'Input performances beam transform (or dictionary thereof) for datagen.'
raise NotImplementedError() | -5,446,088,655,176,826,000 | Input performances beam transform (or dictionary thereof) for datagen. | magenta/models/score2perf/score2perf.py | performances_input_transform | flyingleafe/magenta | python | def performances_input_transform(self, tmp_dir):
raise NotImplementedError() |
def performance_encoder(self):
'Encoder for target performances.'
return music_encoders.MidiPerformanceEncoder(steps_per_second=STEPS_PER_SECOND, num_velocity_bins=NUM_VELOCITY_BINS, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, add_eos=self.add_eos_symbol) | 7,870,267,202,908,675,000 | Encoder for target performances. | magenta/models/score2perf/score2perf.py | performance_encoder | flyingleafe/magenta | python | def performance_encoder(self):
return music_encoders.MidiPerformanceEncoder(steps_per_second=STEPS_PER_SECOND, num_velocity_bins=NUM_VELOCITY_BINS, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, add_eos=self.add_eos_symbol) |
def score_encoders(self):
'List of (name, encoder) tuples for input score components.'
return [] | 5,118,624,544,231,853,000 | List of (name, encoder) tuples for input score components. | magenta/models/score2perf/score2perf.py | score_encoders | flyingleafe/magenta | python | def score_encoders(self):
return [] |
def augment_note_sequence(ns, stretch_factor, transpose_amount):
'Augment a NoteSequence by time stretch and pitch transposition.'
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns | 2,368,470,625,032,840,000 | Augment a NoteSequence by time stretch and pitch transposition. | magenta/models/score2perf/score2perf.py | augment_note_sequence | flyingleafe/magenta | python | def augment_note_sequence(ns, stretch_factor, transpose_amount):
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns |
def augment_note_sequence(ns, stretch_factor, transpose_amount):
'Augment a NoteSequence by time stretch and pitch transposition.'
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns | 2,368,470,625,032,840,000 | Augment a NoteSequence by time stretch and pitch transposition. | magenta/models/score2perf/score2perf.py | augment_note_sequence | flyingleafe/magenta | python | def augment_note_sequence(ns, stretch_factor, transpose_amount):
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns |
def augment_note_sequence(ns, stretch_factor, transpose_amount):
'Augment a NoteSequence by time stretch and pitch transposition.'
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns | 2,368,470,625,032,840,000 | Augment a NoteSequence by time stretch and pitch transposition. | magenta/models/score2perf/score2perf.py | augment_note_sequence | flyingleafe/magenta | python | def augment_note_sequence(ns, stretch_factor, transpose_amount):
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns |
def augment_note_sequence(ns, stretch_factor, transpose_amount):
'Augment a NoteSequence by time stretch and pitch transposition.'
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns | 2,368,470,625,032,840,000 | Augment a NoteSequence by time stretch and pitch transposition. | magenta/models/score2perf/score2perf.py | augment_note_sequence | flyingleafe/magenta | python | def augment_note_sequence(ns, stretch_factor, transpose_amount):
augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False)
try:
(_, num_deleted_notes) = sequences_lib.transpose_note_sequence(augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True)
except chord_symbols_lib.ChordSymbolError:
raise datagen_beam.DataAugmentationError('Transposition of chord symbol(s) failed.')
if num_deleted_notes:
raise datagen_beam.DataAugmentationError('Transposition caused out-of-range pitch(es).')
return augmented_ns |
def __init__(self, trainer):
'\n Generates a path for saving model which can also be used for resuming\n from a checkpoint.\n '
self.trainer = trainer
self.config = self.trainer.config
self.save_dir = self.config.training_parameters.save_dir
self.model_name = self.config.model
self.ckpt_foldername = ckpt_name_from_core_args(self.config)
self.ckpt_foldername += foldername_from_config_override(self.trainer.args)
self.device = registry.get('current_device')
self.ckpt_prefix = ''
if hasattr(self.trainer.model, 'get_ckpt_name'):
self.ckpt_prefix = (self.trainer.model.get_ckpt_name() + '_')
self.config['log_foldername'] = self.ckpt_foldername
self.ckpt_foldername = os.path.join(self.save_dir, self.ckpt_foldername)
self.pth_filepath = os.path.join(self.ckpt_foldername, (((self.ckpt_prefix + self.model_name) + getattr(self.config.model_attributes, self.model_name).code_name) + '_final.pth'))
self.models_foldername = os.path.join(self.ckpt_foldername, 'models')
if (not os.path.exists(self.models_foldername)):
os.makedirs(self.models_foldername)
self.save_config()
self.repo_path = updir(os.path.abspath(__file__), n=3) | 1,764,111,408,306,437,600 | Generates a path for saving model which can also be used for resuming
from a checkpoint. | pythia/utils/checkpoint.py | __init__ | likenneth/mmgnn_textvqa | python | def __init__(self, trainer):
'\n Generates a path for saving model which can also be used for resuming\n from a checkpoint.\n '
self.trainer = trainer
self.config = self.trainer.config
self.save_dir = self.config.training_parameters.save_dir
self.model_name = self.config.model
self.ckpt_foldername = ckpt_name_from_core_args(self.config)
self.ckpt_foldername += foldername_from_config_override(self.trainer.args)
self.device = registry.get('current_device')
self.ckpt_prefix =
if hasattr(self.trainer.model, 'get_ckpt_name'):
self.ckpt_prefix = (self.trainer.model.get_ckpt_name() + '_')
self.config['log_foldername'] = self.ckpt_foldername
self.ckpt_foldername = os.path.join(self.save_dir, self.ckpt_foldername)
self.pth_filepath = os.path.join(self.ckpt_foldername, (((self.ckpt_prefix + self.model_name) + getattr(self.config.model_attributes, self.model_name).code_name) + '_final.pth'))
self.models_foldername = os.path.join(self.ckpt_foldername, 'models')
if (not os.path.exists(self.models_foldername)):
os.makedirs(self.models_foldername)
self.save_config()
self.repo_path = updir(os.path.abspath(__file__), n=3) |
def create_user(self, email, password=None, **extra_fields):
'Create and saves a new user'
if (not email):
raise ValueError('Users must have email address')
user = self.model(email=self.normalize_email(email), **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user | -6,611,066,487,681,690,000 | Create and saves a new user | app/core/models.py | create_user | shadow-smoke/recipe-app-api | python | def create_user(self, email, password=None, **extra_fields):
if (not email):
raise ValueError('Users must have email address')
user = self.model(email=self.normalize_email(email), **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user |
def transaction_exists(self, pkglist):
'\n checks the package list to see if any packages are\n involved in an incomplete transaction\n '
conflicts = []
if (not transaction_helpers):
return conflicts
pkglist_nvreas = (splitFilename(pkg) for pkg in pkglist)
unfinished_transactions = find_unfinished_transactions()
for trans in unfinished_transactions:
steps = find_ts_remaining(trans)
for step in steps:
(action, step_spec) = step
(n, v, r, e, a) = splitFilename(step_spec)
for pkg in pkglist_nvreas:
label = ('%s-%s' % (n, a))
if ((n == pkg[0]) and (a == pkg[4])):
if (label not in conflicts):
conflicts.append(('%s-%s' % (n, a)))
break
return conflicts | 3,814,851,130,299,122,000 | checks the package list to see if any packages are
involved in an incomplete transaction | venv/lib/python2.7/site-packages/ansible/modules/packaging/os/yum.py | transaction_exists | aburan28/ansible-devops-pipeline | python | def transaction_exists(self, pkglist):
'\n checks the package list to see if any packages are\n involved in an incomplete transaction\n '
conflicts = []
if (not transaction_helpers):
return conflicts
pkglist_nvreas = (splitFilename(pkg) for pkg in pkglist)
unfinished_transactions = find_unfinished_transactions()
for trans in unfinished_transactions:
steps = find_ts_remaining(trans)
for step in steps:
(action, step_spec) = step
(n, v, r, e, a) = splitFilename(step_spec)
for pkg in pkglist_nvreas:
label = ('%s-%s' % (n, a))
if ((n == pkg[0]) and (a == pkg[4])):
if (label not in conflicts):
conflicts.append(('%s-%s' % (n, a)))
break
return conflicts |
def local_envra(self, path):
'return envra of a local rpm passed in'
ts = rpm.TransactionSet()
ts.setVSFlags(rpm._RPMVSF_NOSIGNATURES)
fd = os.open(path, os.O_RDONLY)
try:
header = ts.hdrFromFdno(fd)
except rpm.error as e:
return None
finally:
os.close(fd)
return ('%s:%s-%s-%s.%s' % ((header[rpm.RPMTAG_EPOCH] or '0'), header[rpm.RPMTAG_NAME], header[rpm.RPMTAG_VERSION], header[rpm.RPMTAG_RELEASE], header[rpm.RPMTAG_ARCH])) | -6,192,923,276,369,877,000 | return envra of a local rpm passed in | venv/lib/python2.7/site-packages/ansible/modules/packaging/os/yum.py | local_envra | aburan28/ansible-devops-pipeline | python | def local_envra(self, path):
ts = rpm.TransactionSet()
ts.setVSFlags(rpm._RPMVSF_NOSIGNATURES)
fd = os.open(path, os.O_RDONLY)
try:
header = ts.hdrFromFdno(fd)
except rpm.error as e:
return None
finally:
os.close(fd)
return ('%s:%s-%s-%s.%s' % ((header[rpm.RPMTAG_EPOCH] or '0'), header[rpm.RPMTAG_NAME], header[rpm.RPMTAG_VERSION], header[rpm.RPMTAG_RELEASE], header[rpm.RPMTAG_ARCH])) |
def run(self):
'\n actually execute the module code backend\n '
error_msgs = []
if (not HAS_RPM_PYTHON):
error_msgs.append('The Python 2 bindings for rpm are needed for this module. If you require Python 3 support use the `dnf` Ansible module instead.')
if (not HAS_YUM_PYTHON):
error_msgs.append('The Python 2 yum module is needed for this module. If you require Python 3 support use the `dnf` Ansible module instead.')
if (self.disable_excludes and (yum.__version_info__ < (3, 4))):
self.module.fail_json(msg="'disable_includes' is available in yum version 3.4 and onwards.")
if error_msgs:
self.module.fail_json(msg='. '.join(error_msgs))
if self.module.get_bin_path('yum-deprecated'):
yumbin = self.module.get_bin_path('yum-deprecated')
else:
yumbin = self.module.get_bin_path('yum')
self.yum_basecmd = [yumbin, '-d', '2', '-y']
repoquerybin = self.module.get_bin_path('repoquery', required=False)
if (self.install_repoquery and (not repoquerybin) and (not self.module.check_mode)):
yum_path = self.module.get_bin_path('yum')
if yum_path:
self.module.run_command(('%s -y install yum-utils' % yum_path))
repoquerybin = self.module.get_bin_path('repoquery', required=False)
if self.list:
if (not repoquerybin):
self.module.fail_json(msg='repoquery is required to use list= with this module. Please install the yum-utils package.')
results = {'results': self.list_stuff(repoquerybin, self.list)}
else:
my = self.yum_base()
my.conf
repoquery = None
try:
yum_plugins = my.plugins._plugins
except AttributeError:
pass
else:
if ('rhnplugin' in yum_plugins):
if repoquerybin:
repoquery = [repoquerybin, '--show-duplicates', '--plugins', '--quiet']
if (self.installroot != '/'):
repoquery.extend(['--installroot', self.installroot])
results = self.ensure(repoquery)
if repoquery:
results['msg'] = ('%s %s' % (results.get('msg', ''), 'Warning: Due to potential bad behaviour with rhnplugin and certificates, used slower repoquery calls instead of Yum API.'))
self.module.exit_json(**results) | 6,903,917,648,374,279,000 | actually execute the module code backend | venv/lib/python2.7/site-packages/ansible/modules/packaging/os/yum.py | run | aburan28/ansible-devops-pipeline | python | def run(self):
'\n \n '
error_msgs = []
if (not HAS_RPM_PYTHON):
error_msgs.append('The Python 2 bindings for rpm are needed for this module. If you require Python 3 support use the `dnf` Ansible module instead.')
if (not HAS_YUM_PYTHON):
error_msgs.append('The Python 2 yum module is needed for this module. If you require Python 3 support use the `dnf` Ansible module instead.')
if (self.disable_excludes and (yum.__version_info__ < (3, 4))):
self.module.fail_json(msg="'disable_includes' is available in yum version 3.4 and onwards.")
if error_msgs:
self.module.fail_json(msg='. '.join(error_msgs))
if self.module.get_bin_path('yum-deprecated'):
yumbin = self.module.get_bin_path('yum-deprecated')
else:
yumbin = self.module.get_bin_path('yum')
self.yum_basecmd = [yumbin, '-d', '2', '-y']
repoquerybin = self.module.get_bin_path('repoquery', required=False)
if (self.install_repoquery and (not repoquerybin) and (not self.module.check_mode)):
yum_path = self.module.get_bin_path('yum')
if yum_path:
self.module.run_command(('%s -y install yum-utils' % yum_path))
repoquerybin = self.module.get_bin_path('repoquery', required=False)
if self.list:
if (not repoquerybin):
self.module.fail_json(msg='repoquery is required to use list= with this module. Please install the yum-utils package.')
results = {'results': self.list_stuff(repoquerybin, self.list)}
else:
my = self.yum_base()
my.conf
repoquery = None
try:
yum_plugins = my.plugins._plugins
except AttributeError:
pass
else:
if ('rhnplugin' in yum_plugins):
if repoquerybin:
repoquery = [repoquerybin, '--show-duplicates', '--plugins', '--quiet']
if (self.installroot != '/'):
repoquery.extend(['--installroot', self.installroot])
results = self.ensure(repoquery)
if repoquery:
results['msg'] = ('%s %s' % (results.get('msg', ), 'Warning: Due to potential bad behaviour with rhnplugin and certificates, used slower repoquery calls instead of Yum API.'))
self.module.exit_json(**results) |
def graph_degree(A):
'\n Returns the degree for the nodes (rows) of a symmetric \n graph in sparse CSR or CSC format, or a qobj.\n \n Parameters\n ----------\n A : qobj, csr_matrix, csc_matrix\n Input quantum object or csr_matrix.\n \n Returns\n -------\n degree : array\n Array of integers giving the degree for each node (row).\n \n '
if (A.__class__.__name__ == 'Qobj'):
return _node_degrees(A.data.indices, A.data.indptr, A.shape[0])
else:
return _node_degrees(A.indices, A.indptr, A.shape[0]) | 8,779,110,006,112,680,000 | Returns the degree for the nodes (rows) of a symmetric
graph in sparse CSR or CSC format, or a qobj.
Parameters
----------
A : qobj, csr_matrix, csc_matrix
Input quantum object or csr_matrix.
Returns
-------
degree : array
Array of integers giving the degree for each node (row). | qutip/graph.py | graph_degree | trxw/qutip | python | def graph_degree(A):
'\n Returns the degree for the nodes (rows) of a symmetric \n graph in sparse CSR or CSC format, or a qobj.\n \n Parameters\n ----------\n A : qobj, csr_matrix, csc_matrix\n Input quantum object or csr_matrix.\n \n Returns\n -------\n degree : array\n Array of integers giving the degree for each node (row).\n \n '
if (A.__class__.__name__ == 'Qobj'):
return _node_degrees(A.data.indices, A.data.indptr, A.shape[0])
else:
return _node_degrees(A.indices, A.indptr, A.shape[0]) |
def breadth_first_search(A, start):
'\n Breadth-First-Search (BFS) of a graph in CSR or CSC matrix format starting\n from a given node (row). Takes Qobjs and CSR or CSC matrices as inputs.\n \n This function requires a matrix with symmetric structure.\n Use A+trans(A) if original matrix is not symmetric or not sure.\n \n Parameters\n ----------\n A : qobj, csr_matrix\n Input graph in CSR matrix form\n \n start : int\n Staring node for BFS traversal.\n \n Returns\n -------\n order : array\n Order in which nodes are traversed from starting node.\n \n levels : array\n Level of the nodes in the order that they are traversed.\n \n '
if (A.__class__.__name__ == 'Qobj'):
A = A.data
num_rows = A.shape[0]
start = int(start)
(order, levels) = _breadth_first_search(A.indices, A.indptr, num_rows, start)
return (order[(order != (- 1))], levels[(levels != (- 1))]) | -5,681,492,159,195,273,000 | Breadth-First-Search (BFS) of a graph in CSR or CSC matrix format starting
from a given node (row). Takes Qobjs and CSR or CSC matrices as inputs.
This function requires a matrix with symmetric structure.
Use A+trans(A) if original matrix is not symmetric or not sure.
Parameters
----------
A : qobj, csr_matrix
Input graph in CSR matrix form
start : int
Staring node for BFS traversal.
Returns
-------
order : array
Order in which nodes are traversed from starting node.
levels : array
Level of the nodes in the order that they are traversed. | qutip/graph.py | breadth_first_search | trxw/qutip | python | def breadth_first_search(A, start):
'\n Breadth-First-Search (BFS) of a graph in CSR or CSC matrix format starting\n from a given node (row). Takes Qobjs and CSR or CSC matrices as inputs.\n \n This function requires a matrix with symmetric structure.\n Use A+trans(A) if original matrix is not symmetric or not sure.\n \n Parameters\n ----------\n A : qobj, csr_matrix\n Input graph in CSR matrix form\n \n start : int\n Staring node for BFS traversal.\n \n Returns\n -------\n order : array\n Order in which nodes are traversed from starting node.\n \n levels : array\n Level of the nodes in the order that they are traversed.\n \n '
if (A.__class__.__name__ == 'Qobj'):
A = A.data
num_rows = A.shape[0]
start = int(start)
(order, levels) = _breadth_first_search(A.indices, A.indptr, num_rows, start)
return (order[(order != (- 1))], levels[(levels != (- 1))]) |
def symrcm(A, sym=False):
'\n Returns the permutation array that orders a sparse CSR or CSC matrix or Qobj\n in Reverse-Cuthill McKee ordering. Since the input matrix must be symmetric,\n this routine works on the matrix A+Trans(A) if the sym flag is set to False (Default).\n \n It is assumed by default (*sym=False*) that the input matrix is not symmetric. This\n is because it is faster to do A+Trans(A) than it is to check for symmetry for \n a generic matrix. If you are guaranteed that the matrix is symmetric in structure\n (values of matrix element do not matter) then set *sym=True*\n \n Parameters\n ----------\n A : csr_matrix, qobj\n Input sparse csr_matrix or Qobj.\n \n sym : bool {False, True}\n Flag to set whether input matrix is symmetric.\n \n Returns\n -------\n perm : array\n Array of permuted row and column indices.\n \n Notes\n -----\n This routine is used primarily for internal reordering of Lindblad super-operators\n for use in iterative solver routines.\n \n References\n ----------\n E. Cuthill and J. McKee, "Reducing the Bandwidth of Sparse Symmetric Matrices",\n ACM \'69 Proceedings of the 1969 24th national conference, (1969).\n \n '
nrows = A.shape[0]
if (A.__class__.__name__ == 'Qobj'):
if (not sym):
A = (A.data + A.data.transpose())
return _rcm(A.indices, A.indptr, nrows)
else:
return _rcm(A.data.indices, A.data.indptr, nrows)
else:
if (not sym):
A = (A + A.transpose())
return _rcm(A.indices, A.indptr, nrows) | -2,374,158,014,856,256,000 | Returns the permutation array that orders a sparse CSR or CSC matrix or Qobj
in Reverse-Cuthill McKee ordering. Since the input matrix must be symmetric,
this routine works on the matrix A+Trans(A) if the sym flag is set to False (Default).
It is assumed by default (*sym=False*) that the input matrix is not symmetric. This
is because it is faster to do A+Trans(A) than it is to check for symmetry for
a generic matrix. If you are guaranteed that the matrix is symmetric in structure
(values of matrix element do not matter) then set *sym=True*
Parameters
----------
A : csr_matrix, qobj
Input sparse csr_matrix or Qobj.
sym : bool {False, True}
Flag to set whether input matrix is symmetric.
Returns
-------
perm : array
Array of permuted row and column indices.
Notes
-----
This routine is used primarily for internal reordering of Lindblad super-operators
for use in iterative solver routines.
References
----------
E. Cuthill and J. McKee, "Reducing the Bandwidth of Sparse Symmetric Matrices",
ACM '69 Proceedings of the 1969 24th national conference, (1969). | qutip/graph.py | symrcm | trxw/qutip | python | def symrcm(A, sym=False):
'\n Returns the permutation array that orders a sparse CSR or CSC matrix or Qobj\n in Reverse-Cuthill McKee ordering. Since the input matrix must be symmetric,\n this routine works on the matrix A+Trans(A) if the sym flag is set to False (Default).\n \n It is assumed by default (*sym=False*) that the input matrix is not symmetric. This\n is because it is faster to do A+Trans(A) than it is to check for symmetry for \n a generic matrix. If you are guaranteed that the matrix is symmetric in structure\n (values of matrix element do not matter) then set *sym=True*\n \n Parameters\n ----------\n A : csr_matrix, qobj\n Input sparse csr_matrix or Qobj.\n \n sym : bool {False, True}\n Flag to set whether input matrix is symmetric.\n \n Returns\n -------\n perm : array\n Array of permuted row and column indices.\n \n Notes\n -----\n This routine is used primarily for internal reordering of Lindblad super-operators\n for use in iterative solver routines.\n \n References\n ----------\n E. Cuthill and J. McKee, "Reducing the Bandwidth of Sparse Symmetric Matrices",\n ACM \'69 Proceedings of the 1969 24th national conference, (1969).\n \n '
nrows = A.shape[0]
if (A.__class__.__name__ == 'Qobj'):
if (not sym):
A = (A.data + A.data.transpose())
return _rcm(A.indices, A.indptr, nrows)
else:
return _rcm(A.data.indices, A.data.indptr, nrows)
else:
if (not sym):
A = (A + A.transpose())
return _rcm(A.indices, A.indptr, nrows) |
def bfs_matching(A):
'\n Returns an array of row permutations that removes nonzero elements\n from the diagonal of a nonsingular square CSC sparse matrix. Such\n a permutation is always possible provided that the matrix is \n nonsingular.\n \n This function looks at the structure of the matrix only.\n \n Parameters\n ----------\n A : csc_matrix\n Input matrix\n \n Returns\n -------\n perm : array\n Array of row permutations.\n \n Notes\n -----\n This function relies on a maximum cardinality bipartite matching algorithm\n based on a breadth-first search (BFS) of the underlying graph[1]_.\n \n References\n ----------\n .. [1] I. S. Duff, K. Kaya, and B. Ucar, "Design, Implementation, and \n Analysis of Maximum Transversal Algorithms", ACM Trans. Math. Softw.\n 38, no. 2, (2011).\n \n '
nrows = A.shape[0]
if (A.shape[0] != A.shape[1]):
raise ValueError('bfs_matching requires a square matrix.')
if (A.__class__.__name__ == 'Qobj'):
A = A.data.tocsc()
elif (not sp.isspmatrix_csc(A)):
A = sp.csc_matrix(A)
warn('bfs_matching requires CSC matrix format.', sp.SparseEfficiencyWarning)
perm = _bfs_matching(A.indices, A.indptr, nrows)
if np.any((perm == (- 1))):
raise Exception('Possibly singular input matrix.')
return perm | 3,940,556,777,090,186,000 | Returns an array of row permutations that removes nonzero elements
from the diagonal of a nonsingular square CSC sparse matrix. Such
a permutation is always possible provided that the matrix is
nonsingular.
This function looks at the structure of the matrix only.
Parameters
----------
A : csc_matrix
Input matrix
Returns
-------
perm : array
Array of row permutations.
Notes
-----
This function relies on a maximum cardinality bipartite matching algorithm
based on a breadth-first search (BFS) of the underlying graph[1]_.
References
----------
.. [1] I. S. Duff, K. Kaya, and B. Ucar, "Design, Implementation, and
Analysis of Maximum Transversal Algorithms", ACM Trans. Math. Softw.
38, no. 2, (2011). | qutip/graph.py | bfs_matching | trxw/qutip | python | def bfs_matching(A):
'\n Returns an array of row permutations that removes nonzero elements\n from the diagonal of a nonsingular square CSC sparse matrix. Such\n a permutation is always possible provided that the matrix is \n nonsingular.\n \n This function looks at the structure of the matrix only.\n \n Parameters\n ----------\n A : csc_matrix\n Input matrix\n \n Returns\n -------\n perm : array\n Array of row permutations.\n \n Notes\n -----\n This function relies on a maximum cardinality bipartite matching algorithm\n based on a breadth-first search (BFS) of the underlying graph[1]_.\n \n References\n ----------\n .. [1] I. S. Duff, K. Kaya, and B. Ucar, "Design, Implementation, and \n Analysis of Maximum Transversal Algorithms", ACM Trans. Math. Softw.\n 38, no. 2, (2011).\n \n '
nrows = A.shape[0]
if (A.shape[0] != A.shape[1]):
raise ValueError('bfs_matching requires a square matrix.')
if (A.__class__.__name__ == 'Qobj'):
A = A.data.tocsc()
elif (not sp.isspmatrix_csc(A)):
A = sp.csc_matrix(A)
warn('bfs_matching requires CSC matrix format.', sp.SparseEfficiencyWarning)
perm = _bfs_matching(A.indices, A.indptr, nrows)
if np.any((perm == (- 1))):
raise Exception('Possibly singular input matrix.')
return perm |
def weighted_bfs_matching(A):
'\n Returns an array of row permutations that attempts to maximize\n the product of the ABS values of the diagonal elements in \n a nonsingular square CSC sparse matrix. Such a permutation is \n always possible provided that the matrix is nonsingular.\n \n This function looks at both the structure and ABS values of the \n underlying matrix.\n \n Parameters\n ----------\n A : csc_matrix\n Input matrix\n \n Returns\n -------\n perm : array\n Array of row permutations.\n \n Notes\n -----\n This function uses a weighted maximum cardinality bipartite matching \n algorithm based on breadth-first search (BFS). The columns are weighted\n according to the element of max ABS value in the associated rows and \n are traversed in descending order by weight. When performing the BFS \n traversal, the row associated to a given column is the one with maximum \n weight. Unlike other techniques[1]_, this algorithm does not guarantee the \n product of the diagonal is maximized. However, this limitation is offset\n by the substantially faster runtime of this method. \n \n References\n ----------\n .. [1] I. S. Duff and J. Koster, "The design and use of algorithms for \n permuting large entries to the diagonal of sparse matrices", SIAM J. \n Matrix Anal. and Applics. 20, no. 4, 889 (1997).\n \n '
nrows = A.shape[0]
if (A.shape[0] != A.shape[1]):
raise ValueError('weighted_bfs_matching requires a square matrix.')
if (A.__class__.__name__ == 'Qobj'):
A = A.data.tocsc()
elif (not sp.isspmatrix_csc(A)):
A = sp.csc_matrix(A)
warn('weighted_bfs_matching requires CSC matrix format', sp.SparseEfficiencyWarning)
perm = _weighted_bfs_matching(np.asarray(np.abs(A.data), dtype=float), A.indices, A.indptr, nrows)
if np.any((perm == (- 1))):
raise Exception('Possibly singular input matrix.')
return perm | -5,521,932,354,056,884,000 | Returns an array of row permutations that attempts to maximize
the product of the ABS values of the diagonal elements in
a nonsingular square CSC sparse matrix. Such a permutation is
always possible provided that the matrix is nonsingular.
This function looks at both the structure and ABS values of the
underlying matrix.
Parameters
----------
A : csc_matrix
Input matrix
Returns
-------
perm : array
Array of row permutations.
Notes
-----
This function uses a weighted maximum cardinality bipartite matching
algorithm based on breadth-first search (BFS). The columns are weighted
according to the element of max ABS value in the associated rows and
are traversed in descending order by weight. When performing the BFS
traversal, the row associated to a given column is the one with maximum
weight. Unlike other techniques[1]_, this algorithm does not guarantee the
product of the diagonal is maximized. However, this limitation is offset
by the substantially faster runtime of this method.
References
----------
.. [1] I. S. Duff and J. Koster, "The design and use of algorithms for
permuting large entries to the diagonal of sparse matrices", SIAM J.
Matrix Anal. and Applics. 20, no. 4, 889 (1997). | qutip/graph.py | weighted_bfs_matching | trxw/qutip | python | def weighted_bfs_matching(A):
'\n Returns an array of row permutations that attempts to maximize\n the product of the ABS values of the diagonal elements in \n a nonsingular square CSC sparse matrix. Such a permutation is \n always possible provided that the matrix is nonsingular.\n \n This function looks at both the structure and ABS values of the \n underlying matrix.\n \n Parameters\n ----------\n A : csc_matrix\n Input matrix\n \n Returns\n -------\n perm : array\n Array of row permutations.\n \n Notes\n -----\n This function uses a weighted maximum cardinality bipartite matching \n algorithm based on breadth-first search (BFS). The columns are weighted\n according to the element of max ABS value in the associated rows and \n are traversed in descending order by weight. When performing the BFS \n traversal, the row associated to a given column is the one with maximum \n weight. Unlike other techniques[1]_, this algorithm does not guarantee the \n product of the diagonal is maximized. However, this limitation is offset\n by the substantially faster runtime of this method. \n \n References\n ----------\n .. [1] I. S. Duff and J. Koster, "The design and use of algorithms for \n permuting large entries to the diagonal of sparse matrices", SIAM J. \n Matrix Anal. and Applics. 20, no. 4, 889 (1997).\n \n '
nrows = A.shape[0]
if (A.shape[0] != A.shape[1]):
raise ValueError('weighted_bfs_matching requires a square matrix.')
if (A.__class__.__name__ == 'Qobj'):
A = A.data.tocsc()
elif (not sp.isspmatrix_csc(A)):
A = sp.csc_matrix(A)
warn('weighted_bfs_matching requires CSC matrix format', sp.SparseEfficiencyWarning)
perm = _weighted_bfs_matching(np.asarray(np.abs(A.data), dtype=float), A.indices, A.indptr, nrows)
if np.any((perm == (- 1))):
raise Exception('Possibly singular input matrix.')
return perm |
def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
'A better wrapper over request for deferred signing'
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method, params, headers, body)
return self.fetch(request['url'], request['method'], request['headers'], request['body']) | -5,809,463,524,355,869,000 | A better wrapper over request for deferred signing | python/ccxt/base/exchange.py | fetch2 | newdime/ccxt | python | def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method, params, headers, body)
return self.fetch(request['url'], request['method'], request['headers'], request['body']) |
def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
'Exchange.request is the entry point for all generated methods'
return self.fetch2(path, api, method, params, headers, body) | 6,673,804,092,993,897,000 | Exchange.request is the entry point for all generated methods | python/ccxt/base/exchange.py | request | newdime/ccxt | python | def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
return self.fetch2(path, api, method, params, headers, body) |
def find_broadly_matched_key(self, broad, string):
'A helper method for matching error strings exactly vs broadly'
keys = list(broad.keys())
for i in range(0, len(keys)):
key = keys[i]
if (string.find(key) >= 0):
return key
return None | 1,118,882,194,763,658,900 | A helper method for matching error strings exactly vs broadly | python/ccxt/base/exchange.py | find_broadly_matched_key | newdime/ccxt | python | def find_broadly_matched_key(self, broad, string):
keys = list(broad.keys())
for i in range(0, len(keys)):
key = keys[i]
if (string.find(key) >= 0):
return key
return None |
def fetch(self, url, method='GET', headers=None, body=None):
'Perform a HTTP request and return decoded JSON data'
request_headers = self.prepare_request_headers(headers)
url = (self.proxy + url)
if self.verbose:
print('\nRequest:', method, url, request_headers, body)
self.logger.debug('%s %s, Request: %s %s', method, url, request_headers, body)
if body:
body = body.encode()
self.session.cookies.clear()
response = None
http_response = None
json_response = None
try:
response = self.session.request(method, url, data=body, headers=request_headers, timeout=int((self.timeout / 1000)), proxies=self.proxies)
http_response = response.text
json_response = (self.parse_json(http_response) if self.is_json_encoded_object(http_response) else None)
headers = response.headers
if self.enableLastHttpResponse:
self.last_http_response = http_response
if self.enableLastJsonResponse:
self.last_json_response = json_response
if self.enableLastResponseHeaders:
self.last_response_headers = headers
if self.verbose:
print('\nResponse:', method, url, response.status_code, headers, http_response)
self.logger.debug('%s %s, Response: %s %s %s', method, url, response.status_code, headers, http_response)
response.raise_for_status()
except Timeout as e:
self.raise_error(RequestTimeout, method, url, e)
except TooManyRedirects as e:
self.raise_error(ExchangeError, url, method, e)
except SSLError as e:
self.raise_error(ExchangeError, url, method, e)
except HTTPError as e:
self.handle_errors(response.status_code, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_errors(e, response.status_code, http_response, url, method)
self.raise_error(ExchangeError, url, method, e, http_response)
except RequestException as e:
error_string = str(e)
if (('ECONNRESET' in error_string) or ('Connection aborted.' in error_string)):
self.raise_error(NetworkError, url, method, e)
else:
self.raise_error(ExchangeError, url, method, e)
self.handle_errors(response.status_code, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_response(http_response, json_response, url, method, headers, body)
if (json_response is not None):
return json_response
return http_response | -7,195,045,384,639,707,000 | Perform a HTTP request and return decoded JSON data | python/ccxt/base/exchange.py | fetch | newdime/ccxt | python | def fetch(self, url, method='GET', headers=None, body=None):
request_headers = self.prepare_request_headers(headers)
url = (self.proxy + url)
if self.verbose:
print('\nRequest:', method, url, request_headers, body)
self.logger.debug('%s %s, Request: %s %s', method, url, request_headers, body)
if body:
body = body.encode()
self.session.cookies.clear()
response = None
http_response = None
json_response = None
try:
response = self.session.request(method, url, data=body, headers=request_headers, timeout=int((self.timeout / 1000)), proxies=self.proxies)
http_response = response.text
json_response = (self.parse_json(http_response) if self.is_json_encoded_object(http_response) else None)
headers = response.headers
if self.enableLastHttpResponse:
self.last_http_response = http_response
if self.enableLastJsonResponse:
self.last_json_response = json_response
if self.enableLastResponseHeaders:
self.last_response_headers = headers
if self.verbose:
print('\nResponse:', method, url, response.status_code, headers, http_response)
self.logger.debug('%s %s, Response: %s %s %s', method, url, response.status_code, headers, http_response)
response.raise_for_status()
except Timeout as e:
self.raise_error(RequestTimeout, method, url, e)
except TooManyRedirects as e:
self.raise_error(ExchangeError, url, method, e)
except SSLError as e:
self.raise_error(ExchangeError, url, method, e)
except HTTPError as e:
self.handle_errors(response.status_code, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_errors(e, response.status_code, http_response, url, method)
self.raise_error(ExchangeError, url, method, e, http_response)
except RequestException as e:
error_string = str(e)
if (('ECONNRESET' in error_string) or ('Connection aborted.' in error_string)):
self.raise_error(NetworkError, url, method, e)
else:
self.raise_error(ExchangeError, url, method, e)
self.handle_errors(response.status_code, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_response(http_response, json_response, url, method, headers, body)
if (json_response is not None):
return json_response
return http_response |
@staticmethod
def safe_either(method, dictionary, key1, key2, default_value=None):
'A helper-wrapper for the safe_value_2() family.'
value = method(dictionary, key1)
return (value if (value is not None) else method(dictionary, key2, default_value)) | -2,371,737,021,285,098,500 | A helper-wrapper for the safe_value_2() family. | python/ccxt/base/exchange.py | safe_either | newdime/ccxt | python | @staticmethod
def safe_either(method, dictionary, key1, key2, default_value=None):
value = method(dictionary, key1)
return (value if (value is not None) else method(dictionary, key2, default_value)) |
@staticmethod
def truncate(num, precision=0):
'Deprecated, use decimal_to_precision instead'
if (precision > 0):
decimal_precision = math.pow(10, precision)
return (math.trunc((num * decimal_precision)) / decimal_precision)
return int(Exchange.truncate_to_string(num, precision)) | 5,881,430,384,757,220,000 | Deprecated, use decimal_to_precision instead | python/ccxt/base/exchange.py | truncate | newdime/ccxt | python | @staticmethod
def truncate(num, precision=0):
if (precision > 0):
decimal_precision = math.pow(10, precision)
return (math.trunc((num * decimal_precision)) / decimal_precision)
return int(Exchange.truncate_to_string(num, precision)) |
@staticmethod
def truncate_to_string(num, precision=0):
'Deprecated, todo: remove references from subclasses'
if (precision > 0):
parts = ('{0:.%df}' % precision).format(Decimal(num)).split('.')
decimal_digits = parts[1][:precision].rstrip('0')
decimal_digits = (decimal_digits if len(decimal_digits) else '0')
return ((parts[0] + '.') + decimal_digits)
return ('%d' % num) | -3,156,627,279,850,857,000 | Deprecated, todo: remove references from subclasses | python/ccxt/base/exchange.py | truncate_to_string | newdime/ccxt | python | @staticmethod
def truncate_to_string(num, precision=0):
if (precision > 0):
parts = ('{0:.%df}' % precision).format(Decimal(num)).split('.')
decimal_digits = parts[1][:precision].rstrip('0')
decimal_digits = (decimal_digits if len(decimal_digits) else '0')
return ((parts[0] + '.') + decimal_digits)
return ('%d' % num) |
def check_address(self, address):
'Checks an address is not the same character repeated or an empty sequence'
if (address is None):
self.raise_error(InvalidAddress, details='address is None')
if (all(((letter == address[0]) for letter in address)) or (len(address) < self.minFundingAddressLength) or (' ' in address)):
self.raise_error(InvalidAddress, details=(((('address is invalid or has less than ' + str(self.minFundingAddressLength)) + ' characters: "') + str(address)) + '"'))
return address | -2,909,175,738,945,414,700 | Checks an address is not the same character repeated or an empty sequence | python/ccxt/base/exchange.py | check_address | newdime/ccxt | python | def check_address(self, address):
if (address is None):
self.raise_error(InvalidAddress, details='address is None')
if (all(((letter == address[0]) for letter in address)) or (len(address) < self.minFundingAddressLength) or (' ' in address)):
self.raise_error(InvalidAddress, details=(((('address is invalid or has less than ' + str(self.minFundingAddressLength)) + ' characters: "') + str(address)) + '"'))
return address |
@functools.wraps(entry)
def inner(_self, params=None):
'\n Inner is called when a generated method (publicGetX) is called.\n _self is a reference to self created by function.__get__(exchange, type(exchange))\n https://en.wikipedia.org/wiki/Closure_(computer_programming) equivalent to functools.partial\n '
inner_kwargs = dict(outer_kwargs)
if (params is not None):
inner_kwargs['params'] = params
return entry(_self, **inner_kwargs) | 3,173,901,515,913,682,400 | Inner is called when a generated method (publicGetX) is called.
_self is a reference to self created by function.__get__(exchange, type(exchange))
https://en.wikipedia.org/wiki/Closure_(computer_programming) equivalent to functools.partial | python/ccxt/base/exchange.py | inner | newdime/ccxt | python | @functools.wraps(entry)
def inner(_self, params=None):
'\n Inner is called when a generated method (publicGetX) is called.\n _self is a reference to self created by function.__get__(exchange, type(exchange))\n https://en.wikipedia.org/wiki/Closure_(computer_programming) equivalent to functools.partial\n '
inner_kwargs = dict(outer_kwargs)
if (params is not None):
inner_kwargs['params'] = params
return entry(_self, **inner_kwargs) |
def get_core_directory(paths: Optional[Union[(Text, List[Text])]]) -> Text:
'Recursively collects all Core training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Path to temporary directory containing all found Core training files.\n '
(core_files, _) = get_core_nlu_files(paths)
return _copy_files_to_new_dir(core_files) | -2,413,637,754,033,914,000 | Recursively collects all Core training files from a list of paths.
Args:
paths: List of paths to training files or folders containing them.
Returns:
Path to temporary directory containing all found Core training files. | rasa/data.py | get_core_directory | Amirali-Shirkh/rasa-for-botfront | python | def get_core_directory(paths: Optional[Union[(Text, List[Text])]]) -> Text:
'Recursively collects all Core training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Path to temporary directory containing all found Core training files.\n '
(core_files, _) = get_core_nlu_files(paths)
return _copy_files_to_new_dir(core_files) |
def get_nlu_directory(paths: Optional[Union[(Text, List[Text])]]) -> Text:
'Recursively collects all NLU training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Path to temporary directory containing all found NLU training files.\n '
(_, nlu_files) = get_core_nlu_files(paths)
return _copy_files_to_new_dir(nlu_files) | 871,682,756,566,041,500 | Recursively collects all NLU training files from a list of paths.
Args:
paths: List of paths to training files or folders containing them.
Returns:
Path to temporary directory containing all found NLU training files. | rasa/data.py | get_nlu_directory | Amirali-Shirkh/rasa-for-botfront | python | def get_nlu_directory(paths: Optional[Union[(Text, List[Text])]]) -> Text:
'Recursively collects all NLU training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Path to temporary directory containing all found NLU training files.\n '
(_, nlu_files) = get_core_nlu_files(paths)
return _copy_files_to_new_dir(nlu_files) |
def get_core_nlu_directories(paths: Optional[Union[(Text, List[Text])]]) -> Tuple[(Text, Text)]:
'Recursively collects all training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Path to directory containing the Core files and path to directory\n containing the NLU training files.\n '
(story_files, nlu_data_files) = get_core_nlu_files(paths)
story_directory = _copy_files_to_new_dir(story_files)
nlu_directory = _copy_files_to_new_dir(nlu_data_files)
return (story_directory, nlu_directory) | 4,776,967,156,037,344,000 | Recursively collects all training files from a list of paths.
Args:
paths: List of paths to training files or folders containing them.
Returns:
Path to directory containing the Core files and path to directory
containing the NLU training files. | rasa/data.py | get_core_nlu_directories | Amirali-Shirkh/rasa-for-botfront | python | def get_core_nlu_directories(paths: Optional[Union[(Text, List[Text])]]) -> Tuple[(Text, Text)]:
'Recursively collects all training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Path to directory containing the Core files and path to directory\n containing the NLU training files.\n '
(story_files, nlu_data_files) = get_core_nlu_files(paths)
story_directory = _copy_files_to_new_dir(story_files)
nlu_directory = _copy_files_to_new_dir(nlu_data_files)
return (story_directory, nlu_directory) |
def get_core_nlu_files(paths: Optional[Union[(Text, List[Text])]]) -> Tuple[(List[Text], List[Text])]:
'Recursively collects all training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Tuple of paths to story and NLU files.\n '
story_files = set()
nlu_data_files = set()
if (paths is None):
paths = []
elif isinstance(paths, str):
paths = [paths]
for path in set(paths):
if (not path):
continue
if _is_valid_filetype(path):
if is_nlu_file(path):
nlu_data_files.add(os.path.abspath(path))
elif is_story_file(path):
story_files.add(os.path.abspath(path))
else:
(new_story_files, new_nlu_data_files) = _find_core_nlu_files_in_directory(path)
story_files.update(new_story_files)
nlu_data_files.update(new_nlu_data_files)
return (sorted(story_files), sorted(nlu_data_files)) | 3,364,048,093,809,867,300 | Recursively collects all training files from a list of paths.
Args:
paths: List of paths to training files or folders containing them.
Returns:
Tuple of paths to story and NLU files. | rasa/data.py | get_core_nlu_files | Amirali-Shirkh/rasa-for-botfront | python | def get_core_nlu_files(paths: Optional[Union[(Text, List[Text])]]) -> Tuple[(List[Text], List[Text])]:
'Recursively collects all training files from a list of paths.\n\n Args:\n paths: List of paths to training files or folders containing them.\n\n Returns:\n Tuple of paths to story and NLU files.\n '
story_files = set()
nlu_data_files = set()
if (paths is None):
paths = []
elif isinstance(paths, str):
paths = [paths]
for path in set(paths):
if (not path):
continue
if _is_valid_filetype(path):
if is_nlu_file(path):
nlu_data_files.add(os.path.abspath(path))
elif is_story_file(path):
story_files.add(os.path.abspath(path))
else:
(new_story_files, new_nlu_data_files) = _find_core_nlu_files_in_directory(path)
story_files.update(new_story_files)
nlu_data_files.update(new_nlu_data_files)
return (sorted(story_files), sorted(nlu_data_files)) |
def is_nlu_file(file_path: Text) -> bool:
"Checks if a file is a Rasa compatible nlu file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a nlu file, otherwise `False`.\n "
return (loading.guess_format(file_path) != loading.UNK) | -8,459,099,074,937,874,000 | Checks if a file is a Rasa compatible nlu file.
Args:
file_path: Path of the file which should be checked.
Returns:
`True` if it's a nlu file, otherwise `False`. | rasa/data.py | is_nlu_file | Amirali-Shirkh/rasa-for-botfront | python | def is_nlu_file(file_path: Text) -> bool:
"Checks if a file is a Rasa compatible nlu file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a nlu file, otherwise `False`.\n "
return (loading.guess_format(file_path) != loading.UNK) |
def is_story_file(file_path: Text) -> bool:
"Checks if a file is a Rasa story file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a story file, otherwise `False`.\n "
if (not file_path.endswith('.md')):
return False
try:
with open(file_path, encoding=DEFAULT_ENCODING, errors='surrogateescape') as lines:
return any((_contains_story_pattern(line) for line in lines))
except Exception as e:
logger.error(f"Tried to check if '{file_path}' is a story file, but failed to read it. If this file contains story data, you should investigate this error, otherwise it is probably best to move the file to a different location. Error: {e}")
return False | -1,701,745,109,258,489,300 | Checks if a file is a Rasa story file.
Args:
file_path: Path of the file which should be checked.
Returns:
`True` if it's a story file, otherwise `False`. | rasa/data.py | is_story_file | Amirali-Shirkh/rasa-for-botfront | python | def is_story_file(file_path: Text) -> bool:
"Checks if a file is a Rasa story file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a story file, otherwise `False`.\n "
if (not file_path.endswith('.md')):
return False
try:
with open(file_path, encoding=DEFAULT_ENCODING, errors='surrogateescape') as lines:
return any((_contains_story_pattern(line) for line in lines))
except Exception as e:
logger.error(f"Tried to check if '{file_path}' is a story file, but failed to read it. If this file contains story data, you should investigate this error, otherwise it is probably best to move the file to a different location. Error: {e}")
return False |
def is_domain_file(file_path: Text) -> bool:
"Checks whether the given file path is a Rasa domain file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a domain file, otherwise `False`.\n "
file_name = os.path.basename(file_path)
return (file_name in ['domain.yml', 'domain.yaml']) | -5,027,564,738,567,654,000 | Checks whether the given file path is a Rasa domain file.
Args:
file_path: Path of the file which should be checked.
Returns:
`True` if it's a domain file, otherwise `False`. | rasa/data.py | is_domain_file | Amirali-Shirkh/rasa-for-botfront | python | def is_domain_file(file_path: Text) -> bool:
"Checks whether the given file path is a Rasa domain file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a domain file, otherwise `False`.\n "
file_name = os.path.basename(file_path)
return (file_name in ['domain.yml', 'domain.yaml']) |
def is_config_file(file_path: Text) -> bool:
"Checks whether the given file path is a Rasa config file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a Rasa config file, otherwise `False`.\n "
file_name = os.path.basename(file_path)
return (file_name in ['config.yml', 'config.yaml']) | -499,820,486,625,838,900 | Checks whether the given file path is a Rasa config file.
Args:
file_path: Path of the file which should be checked.
Returns:
`True` if it's a Rasa config file, otherwise `False`. | rasa/data.py | is_config_file | Amirali-Shirkh/rasa-for-botfront | python | def is_config_file(file_path: Text) -> bool:
"Checks whether the given file path is a Rasa config file.\n\n Args:\n file_path: Path of the file which should be checked.\n\n Returns:\n `True` if it's a Rasa config file, otherwise `False`.\n "
file_name = os.path.basename(file_path)
return (file_name in ['config.yml', 'config.yaml']) |
def login(self, vmanage_ip, username, password):
'Login to vmanage'
base_url_str = ('https://%s:8443/' % vmanage_ip)
login_action = 'j_security_check'
login_data = {'j_username': username, 'j_password': password}
login_url = (base_url_str + login_action)
url = (base_url_str + login_url)
sess = requests.session()
login_response = sess.post(url=login_url, data=login_data, verify=False)
if (b'<html>' in login_response.content):
print('Login Failed')
sys.exit(0)
self.session[vmanage_ip] = sess | 8,070,638,483,843,328,000 | Login to vmanage | app/Http/Controllers/Dashboard/Wan_edge_Health.py | login | victornguyen98/luanvan2020 | python | def login(self, vmanage_ip, username, password):
base_url_str = ('https://%s:8443/' % vmanage_ip)
login_action = 'j_security_check'
login_data = {'j_username': username, 'j_password': password}
login_url = (base_url_str + login_action)
url = (base_url_str + login_url)
sess = requests.session()
login_response = sess.post(url=login_url, data=login_data, verify=False)
if (b'<html>' in login_response.content):
print('Login Failed')
sys.exit(0)
self.session[vmanage_ip] = sess |
@distributed_trace
def list(self, **kwargs: Any) -> AsyncIterable['_models.OperationListResult']:
'Lists all of the available Microsoft.Resources REST API operations.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either OperationListResult or the result of cls(response)\n :rtype:\n ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.resource.resources.v2019_08_01.models.OperationListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
api_version = kwargs.pop('api_version', '2019-08-01')
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_operations_list_request(api_version=api_version, template_url=self.list.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_operations_list_request(template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('OperationListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), AsyncList(list_of_elem))
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data) | -4,370,259,222,565,901,300 | Lists all of the available Microsoft.Resources REST API operations.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either OperationListResult or the result of cls(response)
:rtype:
~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.resource.resources.v2019_08_01.models.OperationListResult]
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | list | AikoBB/azure-sdk-for-python | python | @distributed_trace
def list(self, **kwargs: Any) -> AsyncIterable['_models.OperationListResult']:
'Lists all of the available Microsoft.Resources REST API operations.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either OperationListResult or the result of cls(response)\n :rtype:\n ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.resource.resources.v2019_08_01.models.OperationListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
api_version = kwargs.pop('api_version', '2019-08-01')
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_operations_list_request(api_version=api_version, template_url=self.list.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_operations_list_request(template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('OperationListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), AsyncList(list_of_elem))
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data) |
@distributed_trace_async
async def begin_delete_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> AsyncLROPoller[None]:
'Deletes a deployment from the deployment history.\n\n A template deployment that is currently running cannot be deleted. Deleting a template\n deployment removes the associated deployment operations. This is an asynchronous operation that\n returns a status of 202 until the template deployment is successfully deleted. The Location\n response header contains the URI that is used to obtain the status of the process. While the\n process is running, a call to the URI in the Location header returns a status of 202. When the\n process finishes, the URI in the Location header returns a status of 204 on success. If the\n asynchronous request failed, the URI in the Location header returns an error-level status code.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for\n this operation to not poll, or pass in your own initialized polling object for a personal\n polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[None]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
api_version = kwargs.pop('api_version', '2019-08-01')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._delete_at_scope_initial(scope=scope, deployment_name=deployment_name, api_version=api_version, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) | 4,929,680,912,637,127,000 | Deletes a deployment from the deployment history.
A template deployment that is currently running cannot be deleted. Deleting a template
deployment removes the associated deployment operations. This is an asynchronous operation that
returns a status of 202 until the template deployment is successfully deleted. The Location
response header contains the URI that is used to obtain the status of the process. While the
process is running, a call to the URI in the Location header returns a status of 202. When the
process finishes, the URI in the Location header returns a status of 204 on success. If the
asynchronous request failed, the URI in the Location header returns an error-level status code.
:param scope: The scope of a deployment.
:type scope: str
:param deployment_name: The name of the deployment.
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | begin_delete_at_scope | AikoBB/azure-sdk-for-python | python | @distributed_trace_async
async def begin_delete_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> AsyncLROPoller[None]:
'Deletes a deployment from the deployment history.\n\n A template deployment that is currently running cannot be deleted. Deleting a template\n deployment removes the associated deployment operations. This is an asynchronous operation that\n returns a status of 202 until the template deployment is successfully deleted. The Location\n response header contains the URI that is used to obtain the status of the process. While the\n process is running, a call to the URI in the Location header returns a status of 202. When the\n process finishes, the URI in the Location header returns a status of 204 on success. If the\n asynchronous request failed, the URI in the Location header returns an error-level status code.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for\n this operation to not poll, or pass in your own initialized polling object for a personal\n polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[None]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
api_version = kwargs.pop('api_version', '2019-08-01')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._delete_at_scope_initial(scope=scope, deployment_name=deployment_name, api_version=api_version, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) |
@distributed_trace_async
async def check_existence_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> bool:
'Checks whether the deployment exists.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: bool, or the result of cls(response)\n :rtype: bool\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
request = build_deployments_check_existence_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, template_url=self.check_existence_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [204, 404]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
return (200 <= response.status_code <= 299) | -7,118,669,282,943,952,000 | Checks whether the deployment exists.
:param scope: The scope of a deployment.
:type scope: str
:param deployment_name: The name of the deployment.
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | check_existence_at_scope | AikoBB/azure-sdk-for-python | python | @distributed_trace_async
async def check_existence_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> bool:
'Checks whether the deployment exists.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: bool, or the result of cls(response)\n :rtype: bool\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
request = build_deployments_check_existence_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, template_url=self.check_existence_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [204, 404]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
return (200 <= response.status_code <= 299) |
@distributed_trace_async
async def begin_create_or_update_at_scope(self, scope: str, deployment_name: str, parameters: '_models.Deployment', **kwargs: Any) -> AsyncLROPoller['_models.DeploymentExtended']:
'Deploys resources at a given scope.\n\n You can provide the template and parameters directly in the request or link to JSON files.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :param parameters: Additional parameters supplied to the operation.\n :type parameters: ~azure.mgmt.resource.resources.v2019_08_01.models.Deployment\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for\n this operation to not poll, or pass in your own initialized polling object for a personal\n polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either DeploymentExtended or the result of\n cls(response)\n :rtype:\n ~azure.core.polling.AsyncLROPoller[~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentExtended]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
api_version = kwargs.pop('api_version', '2019-08-01')
content_type = kwargs.pop('content_type', 'application/json')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._create_or_update_at_scope_initial(scope=scope, deployment_name=deployment_name, parameters=parameters, api_version=api_version, content_type=content_type, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('DeploymentExtended', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) | 3,362,381,667,887,774,700 | Deploys resources at a given scope.
You can provide the template and parameters directly in the request or link to JSON files.
:param scope: The scope of a deployment.
:type scope: str
:param deployment_name: The name of the deployment.
:type deployment_name: str
:param parameters: Additional parameters supplied to the operation.
:type parameters: ~azure.mgmt.resource.resources.v2019_08_01.models.Deployment
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either DeploymentExtended or the result of
cls(response)
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentExtended]
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | begin_create_or_update_at_scope | AikoBB/azure-sdk-for-python | python | @distributed_trace_async
async def begin_create_or_update_at_scope(self, scope: str, deployment_name: str, parameters: '_models.Deployment', **kwargs: Any) -> AsyncLROPoller['_models.DeploymentExtended']:
'Deploys resources at a given scope.\n\n You can provide the template and parameters directly in the request or link to JSON files.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :param parameters: Additional parameters supplied to the operation.\n :type parameters: ~azure.mgmt.resource.resources.v2019_08_01.models.Deployment\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for\n this operation to not poll, or pass in your own initialized polling object for a personal\n polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either DeploymentExtended or the result of\n cls(response)\n :rtype:\n ~azure.core.polling.AsyncLROPoller[~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentExtended]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
api_version = kwargs.pop('api_version', '2019-08-01')
content_type = kwargs.pop('content_type', 'application/json')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._create_or_update_at_scope_initial(scope=scope, deployment_name=deployment_name, parameters=parameters, api_version=api_version, content_type=content_type, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('DeploymentExtended', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) |
@distributed_trace_async
async def get_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> '_models.DeploymentExtended':
'Gets a deployment.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: DeploymentExtended, or the result of cls(response)\n :rtype: ~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentExtended\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
request = build_deployments_get_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, template_url=self.get_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('DeploymentExtended', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | -2,818,057,004,809,382,000 | Gets a deployment.
:param scope: The scope of a deployment.
:type scope: str
:param deployment_name: The name of the deployment.
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: DeploymentExtended, or the result of cls(response)
:rtype: ~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentExtended
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | get_at_scope | AikoBB/azure-sdk-for-python | python | @distributed_trace_async
async def get_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> '_models.DeploymentExtended':
'Gets a deployment.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: DeploymentExtended, or the result of cls(response)\n :rtype: ~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentExtended\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
request = build_deployments_get_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, template_url=self.get_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('DeploymentExtended', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized |
@distributed_trace_async
async def cancel_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> None:
'Cancels a currently running template deployment.\n\n You can cancel a deployment only if the provisioningState is Accepted or Running. After the\n deployment is canceled, the provisioningState is set to Canceled. Canceling a template\n deployment stops the currently running template deployment and leaves the resources partially\n deployed.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
request = build_deployments_cancel_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, template_url=self.cancel_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [204]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) | 5,202,299,697,922,900,000 | Cancels a currently running template deployment.
You can cancel a deployment only if the provisioningState is Accepted or Running. After the
deployment is canceled, the provisioningState is set to Canceled. Canceling a template
deployment stops the currently running template deployment and leaves the resources partially
deployed.
:param scope: The scope of a deployment.
:type scope: str
:param deployment_name: The name of the deployment.
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | cancel_at_scope | AikoBB/azure-sdk-for-python | python | @distributed_trace_async
async def cancel_at_scope(self, scope: str, deployment_name: str, **kwargs: Any) -> None:
'Cancels a currently running template deployment.\n\n You can cancel a deployment only if the provisioningState is Accepted or Running. After the\n deployment is canceled, the provisioningState is set to Canceled. Canceling a template\n deployment stops the currently running template deployment and leaves the resources partially\n deployed.\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
request = build_deployments_cancel_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, template_url=self.cancel_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [204]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) |
@distributed_trace_async
async def validate_at_scope(self, scope: str, deployment_name: str, parameters: '_models.Deployment', **kwargs: Any) -> '_models.DeploymentValidateResult':
'Validates whether the specified template is syntactically correct and will be accepted by Azure\n Resource Manager..\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :param parameters: Parameters to validate.\n :type parameters: ~azure.mgmt.resource.resources.v2019_08_01.models.Deployment\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: DeploymentValidateResult, or the result of cls(response)\n :rtype: ~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentValidateResult\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
content_type = kwargs.pop('content_type', 'application/json')
_json = self._serialize.body(parameters, 'Deployment')
request = build_deployments_validate_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, content_type=content_type, json=_json, template_url=self.validate_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200, 400]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if (response.status_code == 200):
deserialized = self._deserialize('DeploymentValidateResult', pipeline_response)
if (response.status_code == 400):
deserialized = self._deserialize('DeploymentValidateResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | -5,833,968,429,805,891,000 | Validates whether the specified template is syntactically correct and will be accepted by Azure
Resource Manager..
:param scope: The scope of a deployment.
:type scope: str
:param deployment_name: The name of the deployment.
:type deployment_name: str
:param parameters: Parameters to validate.
:type parameters: ~azure.mgmt.resource.resources.v2019_08_01.models.Deployment
:keyword callable cls: A custom type or function that will be passed the direct response
:return: DeploymentValidateResult, or the result of cls(response)
:rtype: ~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentValidateResult
:raises: ~azure.core.exceptions.HttpResponseError | sdk/resources/azure-mgmt-resource/azure/mgmt/resource/resources/v2019_08_01/aio/operations/_operations.py | validate_at_scope | AikoBB/azure-sdk-for-python | python | @distributed_trace_async
async def validate_at_scope(self, scope: str, deployment_name: str, parameters: '_models.Deployment', **kwargs: Any) -> '_models.DeploymentValidateResult':
'Validates whether the specified template is syntactically correct and will be accepted by Azure\n Resource Manager..\n\n :param scope: The scope of a deployment.\n :type scope: str\n :param deployment_name: The name of the deployment.\n :type deployment_name: str\n :param parameters: Parameters to validate.\n :type parameters: ~azure.mgmt.resource.resources.v2019_08_01.models.Deployment\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: DeploymentValidateResult, or the result of cls(response)\n :rtype: ~azure.mgmt.resource.resources.v2019_08_01.models.DeploymentValidateResult\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', '2019-08-01')
content_type = kwargs.pop('content_type', 'application/json')
_json = self._serialize.body(parameters, 'Deployment')
request = build_deployments_validate_at_scope_request(scope=scope, deployment_name=deployment_name, api_version=api_version, content_type=content_type, json=_json, template_url=self.validate_at_scope.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200, 400]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if (response.status_code == 200):
deserialized = self._deserialize('DeploymentValidateResult', pipeline_response)
if (response.status_code == 400):
deserialized = self._deserialize('DeploymentValidateResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized |
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