



'''simple docstring'''
from ...processing_utils import ProcessorMixin






class 			__lowercase		(						a__						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						"SpeechT5FeatureExtractor"
     SCREAMING_SNAKE_CASE__		   =						"SpeechT5Tokenizer"







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(lowerCamelCase_     ,			lowerCamelCase_    )







     def __call__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('audio'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	int		    =  kwargs.pop('text'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('text_target'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('audio_target'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	str		    =  kwargs.pop('sampling_rate'     ,			lowerCamelCase_    )

         if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'    )
         if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'    )
         if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'    )

         if audio is not None:
             __UpperCamelCase	:	str		    =  self.feature_extractor(lowerCamelCase_     ,			*lowerCamelCase_     ,			sampling_rate=lowerCamelCase_     ,			**lowerCamelCase_    )
         elif text is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer(lowerCamelCase_     ,			**lowerCamelCase_    )
         else:
             __UpperCamelCase	:	Dict		    =  None

         if audio_target is not None:
             __UpperCamelCase	:	Optional[Any]		    =  self.feature_extractor(audio_target=lowerCamelCase_     ,			*lowerCamelCase_     ,			sampling_rate=lowerCamelCase_     ,			**lowerCamelCase_    )
             __UpperCamelCase	:	int		    =  targets['input_values']
         elif text_target is not None:
             __UpperCamelCase	:	Optional[int]		    =  self.tokenizer(lowerCamelCase_     ,			**lowerCamelCase_    )
             __UpperCamelCase	:	Dict		    =  targets['input_ids']
         else:
             __UpperCamelCase	:	Optional[Any]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Dict		    =  labels

             __UpperCamelCase	:	Any		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Optional[Any]		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('input_values'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('input_ids'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('labels'     ,			lowerCamelCase_    )

         if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'    )
         if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'    )

         if input_values is not None:
             __UpperCamelCase	:	Tuple		    =  self.feature_extractor.pad(lowerCamelCase_     ,			*lowerCamelCase_     ,			**lowerCamelCase_    )
         elif input_ids is not None:
             __UpperCamelCase	:	Any		    =  self.tokenizer.pad(lowerCamelCase_     ,			**lowerCamelCase_    )
         else:
             __UpperCamelCase	:	int		    =  None

         if labels is not None:
             if "input_ids" in labels or (isinstance(lowerCamelCase_     ,			lowerCamelCase_    ) and "input_ids" in labels[0]):
                 __UpperCamelCase	:	List[str]		    =  self.tokenizer.pad(lowerCamelCase_     ,			**lowerCamelCase_    )
                 __UpperCamelCase	:	Any		    =  targets['input_ids']
             else:
                 __UpperCamelCase	:	str		    =  self.feature_extractor.feature_size
                 __UpperCamelCase	:	Dict		    =  self.feature_extractor.num_mel_bins
                 __UpperCamelCase	:	Optional[int]		    =  self.feature_extractor.pad(lowerCamelCase_     ,			*lowerCamelCase_     ,			**lowerCamelCase_    )
                 __UpperCamelCase	:	List[Any]		    =  feature_size_hack
                 __UpperCamelCase	:	Dict		    =  targets['input_values']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Union[str, Any]		    =  labels

             __UpperCamelCase	:	Dict		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Tuple		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*lowerCamelCase_     ,			**lowerCamelCase_    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*lowerCamelCase_     ,			**lowerCamelCase_    )




'''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @slow
     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,			'prajjwal1/bert-tiny'    )
         __UpperCamelCase	:	Optional[int]		    =  BertTokenizer.from_pretrained('bert-base-uncased'    )

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

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

         __UpperCamelCase	:	Dict		    =  train_dataset.select(range(3_2    )    )
         __UpperCamelCase	:	List[Any]		    =  val_dataset.select(range(1_6    )    )

         __UpperCamelCase	:	Optional[int]		    =  4

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

             __UpperCamelCase	:	Optional[Any]		    =  outputs.input_ids
             __UpperCamelCase	:	List[Any]		    =  outputs.input_ids.copy()
             __UpperCamelCase	:	Optional[Any]		    =  [
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
             ]
             __UpperCamelCase	:	Union[str, Any]		    =  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    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

             # all unnecessary tokens are removed
             __UpperCamelCase	:	Tuple		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

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

             return {"accuracy": accuracy}

         # map train dataset
         __UpperCamelCase	:	str		    =  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	:	Any		    =  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	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir()

         __UpperCamelCase	:	List[Any]		    =  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	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()



