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# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
__version__ = "2.5.1"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging
except ImportError:
pass
else:
absl.logging.set_verbosity("info")
absl.logging.set_stderrthreshold("info")
absl.logging._warn_preinit_stderr = False
import logging
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoConfig
from .configuration_bart import BartConfig
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
from .configuration_mmbt import MMBTConfig
from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
# Configurations
from .configuration_utils import PretrainedConfig
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
from .data import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadV1Processor,
SquadV2Processor,
xtreme_convert_examples_to_features,
xtreme_output_modes,
xtreme_processors,
xtreme_tasks_num_labels,
xglue_convert_examples_to_features,
xglue_output_modes,
xglue_processors,
xglue_tasks_num_labels,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
is_sklearn_available,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
# Files and general utilities
from .file_utils import (
CONFIG_NAME,
MODEL_CARD_NAME,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
add_end_docstrings,
add_start_docstrings,
cached_path,
is_tf_available,
is_torch_available,
)
# Model Cards
from .modelcard import ModelCard
# TF 2.0 <=> PyTorch conversion utilities
from .modeling_tf_pytorch_utils import (
convert_tf_weight_name_to_pt_weight_name,
load_pytorch_checkpoint_in_tf2_model,
load_pytorch_model_in_tf2_model,
load_pytorch_weights_in_tf2_model,
load_tf2_checkpoint_in_pytorch_model,
load_tf2_model_in_pytorch_model,
load_tf2_weights_in_pytorch_model,
)
# Pipelines
from .pipelines import (
CsvPipelineDataFormat,
FeatureExtractionPipeline,
FillMaskPipeline,
JsonPipelineDataFormat,
NerPipeline,
PipedPipelineDataFormat,
Pipeline,
PipelineDataFormat,
QuestionAnsweringPipeline,
TextClassificationPipeline,
TokenClassificationPipeline,
pipeline,
)
from .tokenization_albert import AlbertTokenizer
from .tokenization_auto import AutoTokenizer
from .tokenization_bart import BartTokenizer
from .tokenization_bert import BasicTokenizer, BertTokenizer, BertTokenizerFast, WordpieceTokenizer
from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
from .tokenization_camembert import CamembertTokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFast
from .tokenization_flaubert import FlaubertTokenizer
from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
from .tokenization_t5 import T5Tokenizer
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer, TransfoXLTokenizerFast
# Tokenizers
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_xlm import XLMTokenizer
from .tokenization_xlm_roberta import XLMRobertaTokenizer
from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
if is_sklearn_available():
from .data import glue_compute_metrics, xnli_compute_metrics, xglue_compute_metrics, xtreme_compute_metrics
# Modeling
if is_torch_available():
from .modeling_utils import PreTrainedModel, prune_layer, Conv1D, top_k_top_p_filtering
from .modeling_auto import (
AutoModel,
AutoModelForPreTraining,
AutoModelForSequenceClassification,
AutoModelForQuestionAnswering,
AutoModelWithLMHead,
AutoModelForTokenClassification,
ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_bert import (
BertPreTrainedModel,
BertModel,
BertForPreTraining,
BertForMaskedLM,
BertForNextSentencePrediction,
BertForMultiTaskSequenceClassification,
BertForSequenceClassification,
BertForMultipleChoice,
BertForTokenClassification,
BertForQuestionAnswering,
load_tf_weights_in_bert,
BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_openai import (
OpenAIGPTPreTrainedModel,
OpenAIGPTModel,
OpenAIGPTLMHeadModel,
OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt,
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_transfo_xl import (
TransfoXLPreTrainedModel,
TransfoXLModel,
TransfoXLLMHeadModel,
AdaptiveEmbedding,
load_tf_weights_in_transfo_xl,
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_gpt2 import (
GPT2PreTrainedModel,
GPT2Model,
GPT2LMHeadModel,
GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2,
GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_ctrl import CTRLPreTrainedModel, CTRLModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_xlnet import (
XLNetPreTrainedModel,
XLNetModel,
XLNetLMHeadModel,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetForMultipleChoice,
XLNetForQuestionAnsweringSimple,
XLNetForQuestionAnswering,
load_tf_weights_in_xlnet,
XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_xlm import (
XLMPreTrainedModel,
XLMModel,
XLMWithLMHeadModel,
XLMForSequenceClassification,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_bart import BartForSequenceClassification, BartModel, BartForMaskedLM
from .modeling_roberta import (
RobertaForMaskedLM,
RobertaModel,
RobertaForSequenceClassification,
RobertaForMultiTaskSequenceClassification,
RobertaForMultipleChoice,
RobertaForTokenClassification,
RobertaForQuestionAnswering,
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_camembert import (
CamembertForMaskedLM,
CamembertModel,
CamembertForSequenceClassification,
CamembertForTokenClassification,
CamembertForQuestionAnswering,
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_distilbert import (
DistilBertPreTrainedModel,
DistilBertForMaskedLM,
DistilBertModel,
DistilBertForSequenceClassification,
DistilBertForQuestionAnswering,
DistilBertForTokenClassification,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_camembert import (
CamembertForMaskedLM,
CamembertModel,
CamembertForSequenceClassification,
CamembertForMultipleChoice,
CamembertForTokenClassification,
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_encoder_decoder import PreTrainedEncoderDecoder
from .modeling_t5 import (
T5PreTrainedModel,
T5Model,
T5WithLMHeadModel,
load_tf_weights_in_t5,
T5_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_albert import (
AlbertPreTrainedModel,
AlbertModel,
AlbertForMaskedLM,
AlbertForSequenceClassification,
AlbertForQuestionAnswering,
AlbertForTokenClassification,
load_tf_weights_in_albert,
ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_xlm_roberta import (
XLMRobertaForMaskedLM,
XLMRobertaModel,
XLMRobertaForRetrieval,
XLMRobertaForMultipleChoice,
XLMRobertaForSequenceClassification,
XLMRobertaForSequenceClassificationStable,
XLMRobertaForSequenceClassificationConsistency,
XLMRobertaForMultiTaskSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaForTokenClassificationPoolingStable,
XLMRobertaForQuestionAnswering,
XLMRobertaForQuestionAnsweringStable,
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_mmbt import ModalEmbeddings, MMBTModel, MMBTForClassification
from .modeling_flaubert import (
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForSequenceClassification,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
# Optimization
from .optimization import (
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
)
# TensorFlow
if is_tf_available():
from .modeling_tf_utils import (
TFPreTrainedModel,
TFSharedEmbeddings,
TFSequenceSummary,
shape_list,
tf_top_k_top_p_filtering,
)
from .modeling_tf_auto import (
TFAutoModel,
TFAutoModelForPreTraining,
TFAutoModelForSequenceClassification,
TFAutoModelForQuestionAnswering,
TFAutoModelWithLMHead,
TFAutoModelForTokenClassification,
TF_ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_bert import (
TFBertPreTrainedModel,
TFBertMainLayer,
TFBertEmbeddings,
TFBertModel,
TFBertForPreTraining,
TFBertForMaskedLM,
TFBertForNextSentencePrediction,
TFBertForSequenceClassification,
TFBertForMultipleChoice,
TFBertForTokenClassification,
TFBertForQuestionAnswering,
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_gpt2 import (
TFGPT2PreTrainedModel,
TFGPT2MainLayer,
TFGPT2Model,
TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_openai import (
TFOpenAIGPTPreTrainedModel,
TFOpenAIGPTMainLayer,
TFOpenAIGPTModel,
TFOpenAIGPTLMHeadModel,
TFOpenAIGPTDoubleHeadsModel,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_transfo_xl import (
TFTransfoXLPreTrainedModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLLMHeadModel,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_xlnet import (
TFXLNetPreTrainedModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetForQuestionAnsweringSimple,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_xlm import (
TFXLMPreTrainedModel,
TFXLMMainLayer,
TFXLMModel,
TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_xlm_roberta import (
TFXLMRobertaForMaskedLM,
TFXLMRobertaModel,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_roberta import (
TFRobertaPreTrainedModel,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_camembert import (
TFCamembertModel,
TFCamembertForMaskedLM,
TFCamembertForSequenceClassification,
TFCamembertForTokenClassification,
TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_distilbert import (
TFDistilBertPreTrainedModel,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForQuestionAnswering,
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_ctrl import (
TFCTRLPreTrainedModel,
TFCTRLModel,
TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_albert import (
TFAlbertPreTrainedModel,
TFAlbertModel,
TFAlbertForMaskedLM,
TFAlbertForSequenceClassification,
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
)
from .modeling_tf_t5 import (
TFT5PreTrainedModel,
TFT5Model,
TFT5WithLMHeadModel,
TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP,
)
# Optimization
from .optimization_tf import WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator
if not is_tf_available() and not is_torch_available():
logger.warning(
"Neither PyTorch nor TensorFlow >= 2.0 have been found."
"Models won't be available and only tokenizers, configuration"
"and file/data utilities can be used."
)
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