Delete germantoenglish.py

germantoenglish.py

@@ -1,493 +0,0 @@
-# -*- coding: utf-8 -*-
-"""germanToEnglish.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1UI02YcWdG9ErJd18evuYmF1vNiqz7geo
-"""
-
-from torchtext.data.utils import get_tokenizer
-from torchtext.vocab import build_vocab_from_iterator
-from torchtext.datasets import multi30k, Multi30k
-from typing import Iterable, List
-
-
-# We need to modify the URLs for the dataset since the links to the original dataset are broken
-# Refer to https://github.com/pytorch/text/issues/1756#issuecomment-1163664163 for more info
-multi30k.URL["train"] = "https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz"
-multi30k.URL["valid"] = "https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz"
-
-SRC_LANGUAGE = 'de'
-TGT_LANGUAGE = 'en'
-
-# Place-holders
-token_transform = {}
-vocab_transform = {}
-
-from google.colab import drive
-drive.mount('/gdrive')
-
-#!pip install -U torchdata
-#!pip install -U spacy
-
-#!python -m spacy download en_core_web_sm
-#!python -m spacy download de_core_news_sm
-
-#!pip install portalocker>=2.0.0
-
-token_transform[SRC_LANGUAGE] = get_tokenizer('spacy', language='de_core_news_sm')
-token_transform[TGT_LANGUAGE] = get_tokenizer('spacy', language='en_core_web_sm')
-
-
-# helper function to yield list of tokens
-def yield_tokens(data_iter: Iterable, language: str) -> List[str]:
-    language_index = {SRC_LANGUAGE: 0, TGT_LANGUAGE: 1}
-
-    for data_sample in data_iter:
-        yield token_transform[language](data_sample[language_index[language]])
-
-# Define special symbols and indices
-UNK_IDX, PAD_IDX, BOS_IDX, EOS_IDX = 0, 1, 2, 3
-# Make sure the tokens are in order of their indices to properly insert them in vocab
-special_symbols = ['<unk>', '<pad>', '<bos>', '<eos>']
-
-for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:
-    # Training data Iterator
-    train_iter = Multi30k(split='train', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))
-    # Create torchtext's Vocab object
-    vocab_transform[ln] = build_vocab_from_iterator(yield_tokens(train_iter, ln),
-                                                    min_freq=1,
-                                                    specials=special_symbols,
-                                                    special_first=True)
-
-# Set ``UNK_IDX`` as the default index. This index is returned when the token is not found.
-# If not set, it throws ``RuntimeError`` when the queried token is not found in the Vocabulary.
-for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:
-  vocab_transform[ln].set_default_index(UNK_IDX)
-
-from torch import Tensor
-import torch
-import torch.nn as nn
-from torch.nn import Transformer
-import math
-DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
-class PositionalEncoding(nn.Module):
-    def __init__(self,
-                 emb_size: int,
-                 dropout: float,
-                 maxlen: int = 5000):
-        super(PositionalEncoding, self).__init__()
-        den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)
-        pos = torch.arange(0, maxlen).reshape(maxlen, 1)
-        pos_embedding = torch.zeros((maxlen, emb_size))
-        pos_embedding[:, 0::2] = torch.sin(pos * den)
-        pos_embedding[:, 1::2] = torch.cos(pos * den)
-        pos_embedding = pos_embedding.unsqueeze(-2)
-
-        self.dropout = nn.Dropout(dropout)
-        self.register_buffer('pos_embedding', pos_embedding)
-
-    def forward(self, token_embedding: Tensor):
-        return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])
-
-# helper Module to convert tensor of input indices into corresponding tensor of token embeddings
-class TokenEmbedding(nn.Module):
-    def __init__(self, vocab_size: int, emb_size):
-        super(TokenEmbedding, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, emb_size)
-        self.emb_size = emb_size
-
-    def forward(self, tokens: Tensor):
-        return self.embedding(tokens.long()) * math.sqrt(self.emb_size)
-
-# Seq2Seq Network
-class Seq2SeqTransformer(nn.Module):
-    def __init__(self,
-                 num_encoder_layers: int,
-                 num_decoder_layers: int,
-                 emb_size: int,
-                 nhead: int,
-                 src_vocab_size: int,
-                 tgt_vocab_size: int,
-                 dim_feedforward: int = 512,
-                 dropout: float = 0.1):
-        super(Seq2SeqTransformer, self).__init__()
-        self.transformer = Transformer(d_model=emb_size,
-                                       nhead=nhead,
-                                       num_encoder_layers=num_encoder_layers,
-                                       num_decoder_layers=num_decoder_layers,
-                                       dim_feedforward=dim_feedforward,
-                                       dropout=dropout)
-        self.generator = nn.Linear(emb_size, tgt_vocab_size)
-        self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
-        self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
-        self.positional_encoding = PositionalEncoding(
-            emb_size, dropout=dropout)
-
-    def forward(self,
-                src: Tensor,
-                trg: Tensor,
-                src_mask: Tensor,
-                tgt_mask: Tensor,
-                src_padding_mask: Tensor,
-                tgt_padding_mask: Tensor,
-                memory_key_padding_mask: Tensor):
-        src_emb = self.positional_encoding(self.src_tok_emb(src))
-        tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
-        outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,
-                                src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
-        return self.generator(outs)
-
-    def encode(self, src: Tensor, src_mask: Tensor):
-        return self.transformer.encoder(self.positional_encoding(
-                            self.src_tok_emb(src)), src_mask)
-
-    def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
-        return self.transformer.decoder(self.positional_encoding(
-                          self.tgt_tok_emb(tgt)), memory,
-                          tgt_mask)
-
-from torch import Tensor
-import torch
-import torch.nn as nn
-from torch.nn import Transformer
-import math
-DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
-class PositionalEncoding(nn.Module):
-    def __init__(self,
-                 emb_size: int,
-                 dropout: float,
-                 maxlen: int = 5000):
-        super(PositionalEncoding, self).__init__()
-        den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)
-        pos = torch.arange(0, maxlen).reshape(maxlen, 1)
-        pos_embedding = torch.zeros((maxlen, emb_size))
-        pos_embedding[:, 0::2] = torch.sin(pos * den)
-        pos_embedding[:, 1::2] = torch.cos(pos * den)
-        pos_embedding = pos_embedding.unsqueeze(-2)
-
-        self.dropout = nn.Dropout(dropout)
-        self.register_buffer('pos_embedding', pos_embedding)
-
-    def forward(self, token_embedding: Tensor):
-        return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])
-
-# helper Module to convert tensor of input indices into corresponding tensor of token embeddings
-class TokenEmbedding(nn.Module):
-    def __init__(self, vocab_size: int, emb_size):
-        super(TokenEmbedding, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, emb_size)
-        self.emb_size = emb_size
-
-    def forward(self, tokens: Tensor):
-        return self.embedding(tokens.long()) * math.sqrt(self.emb_size)
-
-# Seq2Seq Network
-class Seq2SeqTransformer(nn.Module):
-    def __init__(self,
-                 num_encoder_layers: int,
-                 num_decoder_layers: int,
-                 emb_size: int,
-                 nhead: int,
-                 src_vocab_size: int,
-                 tgt_vocab_size: int,
-                 dim_feedforward: int = 512,
-                 dropout: float = 0.1):
-        super(Seq2SeqTransformer, self).__init__()
-        self.transformer = Transformer(d_model=emb_size,
-                                       nhead=nhead,
-                                       num_encoder_layers=num_encoder_layers,
-                                       num_decoder_layers=num_decoder_layers,
-                                       dim_feedforward=dim_feedforward,
-                                       dropout=dropout)
-        self.generator = nn.Linear(emb_size, tgt_vocab_size)
-        self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
-        self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
-        self.positional_encoding = PositionalEncoding(
-            emb_size, dropout=dropout)
-
-    def forward(self,
-                src: Tensor,
-                trg: Tensor,
-                src_mask: Tensor,
-                tgt_mask: Tensor,
-                src_padding_mask: Tensor,
-                tgt_padding_mask: Tensor,
-                memory_key_padding_mask: Tensor):
-        src_emb = self.positional_encoding(self.src_tok_emb(src))
-        tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
-        outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,
-                                src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
-        return self.generator(outs)
-
-    def encode(self, src: Tensor, src_mask: Tensor):
-        return self.transformer.encoder(self.positional_encoding(
-                            self.src_tok_emb(src)), src_mask)
-
-    def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
-        return self.transformer.decoder(self.positional_encoding(
-                          self.tgt_tok_emb(tgt)), memory,
-                          tgt_mask)
-
-def generate_square_subsequent_mask(sz):
-    mask = (torch.triu(torch.ones((sz, sz), device=DEVICE)) == 1).transpose(0, 1)
-    mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
-    return mask
-
-
-def create_mask(src, tgt):
-    src_seq_len = src.shape[0]
-    tgt_seq_len = tgt.shape[0]
-
-    tgt_mask = generate_square_subsequent_mask(tgt_seq_len)
-    src_mask = torch.zeros((src_seq_len, src_seq_len),device=DEVICE).type(torch.bool)
-
-    src_padding_mask = (src == PAD_IDX).transpose(0, 1)
-    tgt_padding_mask = (tgt == PAD_IDX).transpose(0, 1)
-    return src_mask, tgt_mask, src_padding_mask, tgt_padding_mask
-
-torch.manual_seed(0)
-
-SRC_VOCAB_SIZE = len(vocab_transform[SRC_LANGUAGE])
-TGT_VOCAB_SIZE = len(vocab_transform[TGT_LANGUAGE])
-EMB_SIZE = 512
-NHEAD = 8
-FFN_HID_DIM = 512
-BATCH_SIZE = 128
-NUM_ENCODER_LAYERS = 3
-NUM_DECODER_LAYERS = 3
-
-transformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,
-                                 NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
-
-for p in transformer.parameters():
-    if p.dim() > 1:
-        nn.init.xavier_uniform_(p)
-
-transformer = transformer.to(DEVICE)
-
-loss_fn = torch.nn.CrossEntropyLoss(ignore_index=PAD_IDX)
-
-optimizer = torch.optim.Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)
-
-from torch.nn.utils.rnn import pad_sequence
-
-# helper function to club together sequential operations
-def sequential_transforms(*transforms):
-    def func(txt_input):
-        for transform in transforms:
-            txt_input = transform(txt_input)
-        return txt_input
-    return func
-
-# function to add BOS/EOS and create tensor for input sequence indices
-def tensor_transform(token_ids: List[int]):
-    return torch.cat((torch.tensor([BOS_IDX]),
-                      torch.tensor(token_ids),
-                      torch.tensor([EOS_IDX])))
-
-# ``src`` and ``tgt`` language text transforms to convert raw strings into tensors indices
-text_transform = {}
-for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:
-    text_transform[ln] = sequential_transforms(token_transform[ln], #Tokenization
-                                               vocab_transform[ln], #Numericalization
-                                               tensor_transform) # Add BOS/EOS and create tensor
-
-
-# function to collate data samples into batch tensors
-def collate_fn(batch):
-    src_batch, tgt_batch = [], []
-    for src_sample, tgt_sample in batch:
-        src_batch.append(text_transform[SRC_LANGUAGE](src_sample.rstrip("\n")))
-        tgt_batch.append(text_transform[TGT_LANGUAGE](tgt_sample.rstrip("\n")))
-
-    src_batch = pad_sequence(src_batch, padding_value=PAD_IDX)
-    tgt_batch = pad_sequence(tgt_batch, padding_value=PAD_IDX)
-    return src_batch, tgt_batch
-
-from torch.utils.data import DataLoader
-
-def train_epoch(model, optimizer):
-    model.train()
-    losses = 0
-    train_iter = Multi30k(split='train', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))
-    train_dataloader = DataLoader(train_iter, batch_size=BATCH_SIZE, collate_fn=collate_fn)
-
-    for src, tgt in train_dataloader:
-        src = src.to(DEVICE)
-        tgt = tgt.to(DEVICE)
-
-        tgt_input = tgt[:-1, :]
-
-        src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)
-
-        logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)
-
-        optimizer.zero_grad()
-
-        tgt_out = tgt[1:, :]
-        loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))
-        loss.backward()
-
-        optimizer.step()
-        losses += loss.item()
-
-    return losses / len(list(train_dataloader))
-
-def evaluate(model):
-    model.eval()
-    losses = 0
-
-    val_iter = Multi30k(split='valid', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))
-    val_dataloader = DataLoader(val_iter, batch_size=BATCH_SIZE, collate_fn=collate_fn)
-
-    for src, tgt in val_dataloader:
-        src = src.to(DEVICE)
-        tgt = tgt.to(DEVICE)
-
-        tgt_input = tgt[:-1, :]
-
-        src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)
-
-        logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)
-
-        tgt_out = tgt[1:, :]
-        loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))
-        losses += loss.item()
-
-    return losses / len(list(val_dataloader))
-
-from timeit import default_timer as timer
-NUM_EPOCHS = 10
-
-for epoch in range(1, NUM_EPOCHS+1):
-    start_time = timer()
-    train_loss = train_epoch(transformer, optimizer)
-    end_time = timer()
-    val_loss = evaluate(transformer)
-    print((f"Epoch: {epoch}, Train loss: {train_loss:.3f}, Val loss: {val_loss:.3f}, "f"Epoch time = {(end_time - start_time):.3f}s"))
-
-model =torch.save(transformer.state_dict(), '/gdrive/My Drive/transformer_model.pth')
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,
-                           NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
-model.load_state_dict(torch.load('/gdrive/My Drive/transformer_model.pth', map_location=device))
-model.to(device)
-model.eval()
-
-def greedy_decode(model,src, src_mask, max_len, start_symbol):
-    #DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    src = src.to(DEVICE)
-    src_mask = src_mask.to(DEVICE)
-    #model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE, NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
-
-    #model.load_state_dict(torch.load('/gdrive/My Drive/transformer_model.pth',map_location= DEVICE))
-
-    memory = model.encode(src, src_mask)
-    ys = torch.ones(1, 1).fill_(start_symbol).type(torch.long).to(DEVICE)
-    for i in range(max_len-1):
-        memory = memory.to(DEVICE)
-        tgt_mask = (generate_square_subsequent_mask(ys.size(0))
-                    .type(torch.bool)).to(DEVICE)
-        out = model.decode(ys, memory, tgt_mask)
-        out = out.transpose(0, 1)
-        prob = model.generator(out[:, -1])
-        _, next_word = torch.max(prob, dim=1)
-        next_word = next_word.item()
-
-        ys = torch.cat([ys,
-                        torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=0)
-        if next_word == EOS_IDX:
-            break
-    return ys
-
-# Load the saved model
-#loaded_model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,
-                                 # NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
-#loaded_model.load_state_dict(torch.load('/gdrive/My Drive/transformer_model.pth'))
-#loaded_model.eval()  # Make sure to set the model in evaluation mode
-
-def translate(src_sentence: str):
-    model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)
-    #DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model.load_state_dict(torch.load('/gdrive/My Drive/transformer_model.pth'))
-    model.to(DEVICE)
-    model.eval()
-    src = text_transform[SRC_LANGUAGE](src_sentence).view(-1, 1)
-    num_tokens = src.shape[0]
-    src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)
-    tgt_tokens = greedy_decode(
-        model,  src, src_mask, max_len=num_tokens + 5, start_symbol=BOS_IDX).flatten()
-    return " ".join(vocab_transform[TGT_LANGUAGE].lookup_tokens(list(tgt_tokens.cpu().numpy()))).replace("<bos>", "").replace("<eos>", "")
-
-print(translate("Eine Gruppe von Menschen steht vor einem Iglu ."))
-
-#!pip install transformers
-
-from transformers.modeling_utils import PreTrainedModel ,PretrainedConfig
-
-class Seq2SeqTransformer(PreTrainedModel):
-    def __init__(self,config):
-        super(Seq2SeqTransformer, self).__init__(config)
-        self.transformer = Transformer(d_model=config.emb_size,
-                                       nhead=config.nhead,
-                                       num_encoder_layers=config.num_encoder_layers,
-                                       num_decoder_layers=config.num_decoder_layers,
-                                       dim_feedforward=config.dim_feedforward,
-                                       dropout=config.dropout)
-        self.generator = nn.Linear(config.emb_size, config.tgt_vocab_size)
-        self.src_tok_emb = TokenEmbedding(config.src_vocab_size, config.emb_size)
-        self.tgt_tok_emb = TokenEmbedding(config.tgt_vocab_size, config.emb_size)
-        self.positional_encoding = PositionalEncoding(
-            config.emb_size, dropout=config.dropout)
-
-config = PretrainedConfig(
-      # Specify your vocabulary size
-    dim_feedforward =512,
-    dropout= 0.1,
-    emb_size= 512,
-    num_decoder_layers= 3,
-    num_encoder_layers= 3,
-    nhead= 8,
-    src_vocab_size= 19214,
-    tgt_vocab_size= 10837
-)
-
-model = Seq2SeqTransformer(config)
-model.to(DEVICE)
-
-
-model.save_pretrained('/gdrive/My Drive')
-
-#!pip install -q gradio==3.48.0
-
-import gradio as gr
-import torch
-from torchtext.data.utils import get_tokenizer
-from torchtext.vocab import build_vocab_from_iterator
-from torchtext.datasets import Multi30k
-from torch import Tensor
-from typing import Iterable, List
-
-if __name__ == "__main__":
-    # Create the Gradio interface
-    iface = gr.Interface(
-        fn=translate,  # Specify the translation function as the main function
-        inputs=[
-            gr.components.Textbox(label="Text")
-
-    ],
-        outputs=["text"],
-        cache_examples=False,  # Disable caching of examples
-        title="germanToenglish",  # Set the title of the interface
-        #description="This is a translator app for arabic and english. Currently supports only english to arabic."  # Add a description of the interface
-    )
-
-    # Launch the interface
-    iface.launch(share=True)