





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 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  @slow
  @require_torch
  def 							lowercase						(							self					:						Tuple	)				->		Any:
     __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(lowerCAmelCase_					:						Optional[int]	):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        __lowerCAmelCase				      =      tokenizer(batch['article']   ,    padding='max_length'   ,    truncation=lowerCAmelCase_   ,    max_length=5_1_2	)
        __lowerCAmelCase				      =      tokenizer(batch['highlights']   ,    padding='max_length'   ,    truncation=lowerCAmelCase_   ,    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(lowerCAmelCase_	) == 5_1_2 for x in inputs.input_ids	)
        assert all(len(lowerCAmelCase_	) == 1_2_8 for x in outputs.input_ids	)

        return batch

     def _compute_metrics(lowerCAmelCase_					:						List[str]	):
        __lowerCAmelCase				      =      pred.label_ids
        __lowerCAmelCase				      =      pred.predictions

        # all unnecessary tokens are removed
        __lowerCAmelCase				      =      tokenizer.batch_decode(lowerCAmelCase_   ,    skip_special_tokens=lowerCAmelCase_	)
        __lowerCAmelCase				      =      tokenizer.batch_decode(lowerCAmelCase_   ,    skip_special_tokens=lowerCAmelCase_	)

        __lowerCAmelCase				      =      sum([int(pred_str[i] == label_str[i]	) for i in range(len(lowerCAmelCase_	)	)]	) / len(lowerCAmelCase_	)

        return {"accuracy": accuracy}

     # map train dataset
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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']   ,    )

     __lowerCAmelCase				      =      self.get_auto_remove_tmp_dir()

     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      SeqaSeqTrainer(
         model=lowerCAmelCase_   ,    args=lowerCAmelCase_   ,    compute_metrics=_compute_metrics   ,    train_dataset=lowerCAmelCase_   ,    eval_dataset=lowerCAmelCase_   ,    tokenizer=lowerCAmelCase_   ,    )

     # start training
     trainer.train()









from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__a:     Union[str, Any]   					=					logging.get_logger(__name__)

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]