



'''simple docstring'''






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
					import datasets

class 							a__( UpperCamelCase__     ):







				@slow
				@require_torch
				def       lowercase_	(				self			:  Dict     ):
						a						: List[Any]					       =					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'						,      'prajjwal1/bert-tiny'     )
						a						: int					       =					BertTokenizer.from_pretrained('bert-base-uncased'     )

						a						: Tuple					       =					bertabert.config.encoder.vocab_size
						a						: Tuple					       =					tokenizer.sep_token_id
						a						: Optional[int]					       =					tokenizer.cls_token_id
						a						: Union[str, Any]					       =					1_28

						a						: Union[str, Any]					       =					datasets.load_dataset('cnn_dailymail'						,      '3.0.0'						,      split='train[:1%]'     )
						a						: Optional[int]					       =					datasets.load_dataset('cnn_dailymail'						,      '3.0.0'						,      split='validation[:1%]'     )

						a						: List[str]					       =					train_dataset.select(range(32     )     )
						a						: Any					       =					val_dataset.select(range(16     )     )

						a						: Any					       =					4

						def _map_to_encoder_decoder_inputs(__snake_case			:  Optional[Any]     ):
								# Tokenizer will automatically set [BOS] <text> [EOS]
								a						: Any					       =					tokenizer(batch['article']						,      padding='max_length'						,      truncation=__lowerCamelCase						,      max_length=5_12     )
								a						: List[Any]					       =					tokenizer(batch['highlights']						,      padding='max_length'						,      truncation=__lowerCamelCase						,      max_length=1_28     )
								a						: str					       =					inputs.input_ids
								a						: Tuple					       =					inputs.attention_mask

								a						: Optional[int]					       =					outputs.input_ids
								a						: Dict					       =					outputs.input_ids.copy()
								a						: List[str]					       =					[
								    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
								]
								a						: int					       =					outputs.attention_mask

								assert all(len(__lowerCamelCase     ) == 5_12 for x in inputs.input_ids     )
								assert all(len(__lowerCamelCase     ) == 1_28 for x in outputs.input_ids     )

								return batch

						def _compute_metrics(__snake_case			:  str     ):
								a						: List[str]					       =					pred.label_ids
								a						: str					       =					pred.predictions

								# all unnecessary tokens are removed
								a						: Tuple					       =					tokenizer.batch_decode(__lowerCamelCase						,      skip_special_tokens=__lowerCamelCase     )
								a						: Union[str, Any]					       =					tokenizer.batch_decode(__lowerCamelCase						,      skip_special_tokens=__lowerCamelCase     )

								a						: Dict					       =					sum([int(pred_str[i] == label_str[i]     ) for i in range(len(__lowerCamelCase     )     )]     ) / len(__lowerCamelCase     )

								return {"accuracy": accuracy}

						# map train dataset
						a						: List[str]					       =					train_dataset.map(
						    _map_to_encoder_decoder_inputs						,      batched=__lowerCamelCase						,      batch_size=__lowerCamelCase						,      remove_columns=['article', 'highlights']						,      )
						train_dataset.set_format(
						    type='torch'						,      columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,      )

						# same for validation dataset
						a						: str					       =					val_dataset.map(
						    _map_to_encoder_decoder_inputs						,      batched=__lowerCamelCase						,      batch_size=__lowerCamelCase						,      remove_columns=['article', 'highlights']						,      )
						val_dataset.set_format(
						    type='torch'						,      columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,      )

						a						: int					       =					self.get_auto_remove_tmp_dir()

						a						: Optional[Any]					       =					SeqaSeqTrainingArguments(
						    output_dir=__lowerCamelCase						,      per_device_train_batch_size=__lowerCamelCase						,      per_device_eval_batch_size=__lowerCamelCase						,      predict_with_generate=__lowerCamelCase						,      evaluation_strategy='steps'						,      do_train=__lowerCamelCase						,      do_eval=__lowerCamelCase						,      warmup_steps=0						,      eval_steps=2						,      logging_steps=2						,      )

						# instantiate trainer
						a						: Any					       =					SeqaSeqTrainer(
						    model=__lowerCamelCase						,      args=__lowerCamelCase						,      compute_metrics=_compute_metrics						,      train_dataset=__lowerCamelCase						,      eval_dataset=__lowerCamelCase						,      tokenizer=__lowerCamelCase						,      )

						# start training
						trainer.train()




'''simple docstring'''






import torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

        if p_starts is not None:
          a						: List[Any]					       =					torch.vstack((p_starts, p_start)     )
          a						: List[Any]					       =					torch.vstack((p_ends, p_end)     )
        else:
          a						: List[Any]					       =					p_start
          a						: Optional[int]					       =					p_end

      return p_starts, p_ends