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     lowerCAmelCase__     (				__snake_case			):


		'''simple docstring'''






		@slow
		@require_torch
		def   _snake_case       (       self     :     Tuple		)      ->	str:
				_lowerCamelCase				   =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'		)
				_lowerCamelCase				   =  BertTokenizer.from_pretrained('bert-base-uncased'		)

				_lowerCamelCase				   =  bertabert.config.encoder.vocab_size
				_lowerCamelCase				   =  tokenizer.sep_token_id
				_lowerCamelCase				   =  tokenizer.cls_token_id
				_lowerCamelCase				   =  1_2_8

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

				_lowerCamelCase				   =  train_dataset.select(range(3_2		)		)
				_lowerCamelCase				   =  val_dataset.select(range(1_6		)		)

				_lowerCamelCase				   =  4

				def _map_to_encoder_decoder_inputs(snake_case__     :     Tuple		):
						# Tokenizer will automatically set [BOS] <text> [EOS]
						_lowerCamelCase				   =  tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=_SCREAMING_SNAKE_CASE     ,  max_length=5_1_2		)
						_lowerCamelCase				   =  tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=_SCREAMING_SNAKE_CASE     ,  max_length=1_2_8		)
						_lowerCamelCase				   =  inputs.input_ids
						_lowerCamelCase				   =  inputs.attention_mask

						_lowerCamelCase				   =  outputs.input_ids
						_lowerCamelCase				   =  outputs.input_ids.copy()
						_lowerCamelCase				   =  [
						    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
						]
						_lowerCamelCase				   =  outputs.attention_mask

						assert all(len(_SCREAMING_SNAKE_CASE		) == 5_1_2 for x in inputs.input_ids		)
						assert all(len(_SCREAMING_SNAKE_CASE		) == 1_2_8 for x in outputs.input_ids		)

						return batch

				def _compute_metrics(snake_case__     :     str		):
						_lowerCamelCase				   =  pred.label_ids
						_lowerCamelCase				   =  pred.predictions

						# all unnecessary tokens are removed
						_lowerCamelCase				   =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     ,  skip_special_tokens=_SCREAMING_SNAKE_CASE		)
						_lowerCamelCase				   =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE     ,  skip_special_tokens=_SCREAMING_SNAKE_CASE		)

						_lowerCamelCase				   =  sum([int(pred_str[i] == label_str[i]		) for i in range(len(_SCREAMING_SNAKE_CASE		)		)]		) / len(_SCREAMING_SNAKE_CASE		)

						return {"accuracy": accuracy}

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

				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()

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

				# instantiate trainer
				_lowerCamelCase				   =  SeqaSeqTrainer(
				    model=_SCREAMING_SNAKE_CASE     ,  args=_SCREAMING_SNAKE_CASE     ,  compute_metrics=_compute_metrics     ,  train_dataset=_SCREAMING_SNAKE_CASE     ,  eval_dataset=_SCREAMING_SNAKE_CASE     ,  tokenizer=_SCREAMING_SNAKE_CASE     ,  )

				# start training
				trainer.train()







from collections.abc import Sequence
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase))
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      UpperCamelCase					       =  0.0
      for coeff in reversed(_UpperCamelCase):
            UpperCamelCase					       =  result * x + coeff
      return result


if __name__ == "__main__":
       __magic_name__   :  Union[str, Any]        =							(0.0, 0.0, 5.0, 9.3, 7.0)
       __magic_name__   :  List[str]        =							10.0
       print(evaluate_poly(poly, x))
       print(horner(poly, x))



