






"""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       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	@slow
	@require_torch
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'  ,					'prajjwal1/bert-tiny'						)
					A__							 =       BertTokenizer.from_pretrained('bert-base-uncased'						)

					A__							 =       bertabert.config.encoder.vocab_size
					A__							 =       tokenizer.sep_token_id
					A__							 =       tokenizer.cls_token_id
					A__							 =       128

					A__							 =       datasets.load_dataset('cnn_dailymail'  ,					'3.0.0'  ,					split='train[:1%]'						)
					A__							 =       datasets.load_dataset('cnn_dailymail'  ,					'3.0.0'  ,					split='validation[:1%]'						)

					A__							 =       train_dataset.select(range(32						)						)
					A__							 =       val_dataset.select(range(16						)						)

					A__							 =       4

					def _map_to_encoder_decoder_inputs(__a						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									A__							 =       tokenizer(batch['article']  ,					padding='max_length'  ,					truncation=__a  ,					max_length=512						)
									A__							 =       tokenizer(batch['highlights']  ,					padding='max_length'  ,					truncation=__a  ,					max_length=128						)
									A__							 =       inputs.input_ids
									A__							 =       inputs.attention_mask

									A__							 =       outputs.input_ids
									A__							 =       outputs.input_ids.copy()
									A__							 =       [
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
									]
									A__							 =       outputs.attention_mask

									assert all(len(__a						) == 512 for x in inputs.input_ids						)
									assert all(len(__a						) == 128 for x in outputs.input_ids						)

									return batch

					def _compute_metrics(__a						):
									A__							 =       pred.label_ids
									A__							 =       pred.predictions

									# all unnecessary tokens are removed
									A__							 =       tokenizer.batch_decode(__a  ,					skip_special_tokens=__a						)
									A__							 =       tokenizer.batch_decode(__a  ,					skip_special_tokens=__a						)

									A__							 =       sum([int(pred_str[i] == label_str[i]						) for i in range(len(__a						)						)]						) / len(__a						)

									return {"accuracy": accuracy}

					# map train dataset
					A__							 =       train_dataset.map(
					    _map_to_encoder_decoder_inputs  ,					batched=__a  ,					batch_size=__a  ,					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__							 =       val_dataset.map(
					    _map_to_encoder_decoder_inputs  ,					batched=__a  ,					batch_size=__a  ,					remove_columns=['article', 'highlights']  ,					)
					val_dataset.set_format(
					    type='torch'  ,					columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']  ,					)

					A__							 =       self.get_auto_remove_tmp_dir()

					A__							 =       SeqaSeqTrainingArguments(
					    output_dir=__a  ,					per_device_train_batch_size=__a  ,					per_device_eval_batch_size=__a  ,					predict_with_generate=__a  ,					evaluation_strategy='steps'  ,					do_train=__a  ,					do_eval=__a  ,					warmup_steps=0  ,					eval_steps=2  ,					logging_steps=2  ,					)

					# instantiate trainer
					A__							 =       SeqaSeqTrainer(
					    model=__a  ,					args=__a  ,					compute_metrics=_compute_metrics  ,					train_dataset=__a  ,					eval_dataset=__a  ,					tokenizer=__a  ,					)

					# start training
					trainer.train()










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

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

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


