"""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		(			__a      ):

      @slow
      @require_torch
      def 							A__			(		self		):







            """simple docstring"""



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

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

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

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

            lowercase								=      4

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

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

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

                  return batch

            def _compute_metrics(__lowerCAmelCase		):
                  lowercase								=      pred.label_ids
                  lowercase								=      pred.predictions

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

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

                  return {"accuracy": accuracy}

            # map train dataset
            lowercase								=      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
            lowercase								=      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"""]						,			)

            lowercase								=      self.get_auto_remove_tmp_dir()

            lowercase								=      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
            lowercase								=      SeqaSeqTrainer(
                model=_lowerCamelCase						,			args=_lowerCamelCase						,			compute_metrics=_compute_metrics						,			train_dataset=_lowerCamelCase						,			eval_dataset=_lowerCamelCase						,			tokenizer=_lowerCamelCase						,			)

            # start training
            trainer.train()





"""simple docstring"""






def      lowerCAmelCase__			(		__magic_name__ = 1_0      )						->str:
			if not isinstance(__magic_name__			,   __magic_name__      ) or n < 0:
						raise ValueError("Invalid input"      )
			__lowercase       =       1_0**n
			__lowercase       =       2_8_4_3_3 * (pow(2			,   7_8_3_0_4_5_7			,   __magic_name__      )) + 1
			return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"{solution(10) = }")





