"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				'Wav2Vec2FeatureExtractor'
    _snake_case  :	Any  				=				'AutoTokenizer'


    def __init__(       self						:	List[str] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[Any]       ):
      super().__init__(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.feature_extractor
      _UpperCAmelCase    			=   False


    @classmethod
    def   lowerCAmelCase_	(       cls						:	int ,					__lowerCAmelCase						:	Dict ,					**__lowerCAmelCase						:	str       ):
      try:
        return super().from_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )
      except OSError:
        warnings.warn(
            f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
            """ include a `tokenizer_class` attribute is deprecated and will be """
            """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
            """ attribute to either your `config.json` or `tokenizer_config.json` """
            """file to suppress this warning: """ ,					__lowerCAmelCase ,					)

        _UpperCAmelCase    			=   WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )
        _UpperCAmelCase    			=   WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )

        return cls(feature_extractor=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase       )


    def __call__(       self						:	Dict ,					*__lowerCAmelCase						:	int ,					**__lowerCAmelCase						:	Any       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*__lowerCAmelCase ,					**__lowerCAmelCase       )

      if "raw_speech" in kwargs:
        warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""       )
        _UpperCAmelCase    			=   kwargs.pop("""raw_speech"""       )
      else:
        _UpperCAmelCase    			=   kwargs.pop("""audio""" ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   kwargs.pop("""sampling_rate""" ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   kwargs.pop("""text""" ,					__lowerCAmelCase       )
      if len(__lowerCAmelCase       ) > 0:
        _UpperCAmelCase    			=   args[0]
        _UpperCAmelCase    			=   args[1:]

      if audio is None and text is None:
        raise ValueError("""You need to specify either an `audio` or `text` input to process."""       )

      if audio is not None:
        _UpperCAmelCase    			=   self.feature_extractor(__lowerCAmelCase ,					*__lowerCAmelCase ,					sampling_rate=__lowerCAmelCase ,					**__lowerCAmelCase       )
      if text is not None:
        _UpperCAmelCase    			=   self.tokenizer(__lowerCAmelCase ,					**__lowerCAmelCase       )

      if text is None:
        return inputs
      elif audio is None:
        return encodings
      else:
        _UpperCAmelCase    			=   encodings["""input_ids"""]
        return inputs


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					*__lowerCAmelCase						:	int ,					**__lowerCAmelCase						:	Optional[Any]       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor.pad(*__lowerCAmelCase ,					**__lowerCAmelCase       )

      _UpperCAmelCase    			=   kwargs.pop("""input_features""" ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   kwargs.pop("""labels""" ,					__lowerCAmelCase       )
      if len(__lowerCAmelCase       ) > 0:
        _UpperCAmelCase    			=   args[0]
        _UpperCAmelCase    			=   args[1:]

      if input_features is not None:
        _UpperCAmelCase    			=   self.feature_extractor.pad(__lowerCAmelCase ,					*__lowerCAmelCase ,					**__lowerCAmelCase       )
      if labels is not None:
        _UpperCAmelCase    			=   self.tokenizer.pad(__lowerCAmelCase ,					**__lowerCAmelCase       )

      if labels is None:
        return input_features
      elif input_features is None:
        return labels
      else:
        _UpperCAmelCase    			=   labels["""input_ids"""]
        return input_features


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					*__lowerCAmelCase						:	Dict ,					**__lowerCAmelCase						:	Tuple       ):
      return self.tokenizer.batch_decode(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					*__lowerCAmelCase						:	Any ,					**__lowerCAmelCase						:	Optional[int]       ):
      return self.tokenizer.decode(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    @contextmanager
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      warnings.warn(
          """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
          """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
          """your audio inputs, or in a separate call."""       )
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   self.tokenizer
      yield
      _UpperCAmelCase    			=   self.feature_extractor
      _UpperCAmelCase    			=   False

"""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					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

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

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

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

      _UpperCAmelCase    			=   4

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

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   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						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

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

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

        return {"accuracy": accuracy}

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

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

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

      # start training
      trainer.train()
