




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  ):






    """simple docstring"""





    @slow
    @require_torch
    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Tuple  =     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''  ,'''prajjwal1/bert-tiny'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

         UpperCAmelCase_     :				Optional[int]  =     bertabert.config.encoder.vocab_size
         UpperCAmelCase_     :				Any  =     tokenizer.sep_token_id
         UpperCAmelCase_     :				Optional[int]  =     tokenizer.cls_token_id
         UpperCAmelCase_     :				List[Any]  =     128

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

         UpperCAmelCase_     :				str  =     train_dataset.select(range(32    )    )
         UpperCAmelCase_     :				int  =     val_dataset.select(range(16    )    )

         UpperCAmelCase_     :				Optional[Any]  =     4

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

              UpperCAmelCase_     :				Optional[Any]  =     outputs.input_ids
              UpperCAmelCase_     :				Optional[int]  =     outputs.input_ids.copy()
              UpperCAmelCase_     :				Any  =     [
                  [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
              ]
              UpperCAmelCase_     :				Dict  =     outputs.attention_mask

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

              return batch

         def _compute_metrics(_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				List[str]  =     pred.label_ids
              UpperCAmelCase_     :				Dict  =     pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase_     :				List[str]  =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  ,skip_special_tokens=_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[str]  =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  ,skip_special_tokens=_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				str  =     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
         UpperCAmelCase_     :				Union[str, Any]  =     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
         UpperCAmelCase_     :				List[str]  =     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''']  ,)

         UpperCAmelCase_     :				Optional[int]  =     self.get_auto_remove_tmp_dir()

         UpperCAmelCase_     :				List[Any]  =     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
         UpperCAmelCase_     :				int  =     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()







import requests






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  {'Content-Type': 'application/json'}
   UpperCamelCase_						=  requests.post(__lowercase     ,   json={'text': message_body}     ,   headers=__lowercase)
   if response.status_code != 200:
      UpperCamelCase_						=  (
          'Request to slack returned an error '
          f"""{response.status_code}, the response is:\n{response.text}"""
      )
      raise ValueError(__lowercase)


if __name__ == "__main__":
 # Set the slack url to the one provided by Slack when you create the webhook at
 # https://my.slack.com/services/new/incoming-webhook/
 send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")





