



'''simple docstring'''



from ...processing_utils import ProcessorMixin





class _UpperCamelCase		(  SCREAMING_SNAKE_CASE_		):





				'''simple docstring'''





				lowerCAmelCase__            =     "SpeechT5FeatureExtractor"
				lowerCAmelCase__            =     "SpeechT5Tokenizer"

				def __init__(				self	:	Optional[int]	,							_lowerCAmelCase	:	List[str]	,							_lowerCAmelCase	:	Dict):







											'''simple docstring'''

											super().__init__(lowercase_	,							lowercase_)

				def __call__(				self	:	Optional[Any]	,							*_lowerCAmelCase	:	List[str]	,							**_lowerCAmelCase	:	List[Any]):







											'''simple docstring'''

											__lowercase													=kwargs.pop('audio'	,							lowercase_)
											__lowercase													=kwargs.pop('text'	,							lowercase_)
											__lowercase													=kwargs.pop('text_target'	,							lowercase_)
											__lowercase													=kwargs.pop('audio_target'	,							lowercase_)
											__lowercase													=kwargs.pop('sampling_rate'	,							lowercase_)

											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:
																		__lowercase													=self.feature_extractor(lowercase_	,							*lowercase_	,							sampling_rate=lowercase_	,							**lowercase_)
											elif text is not None:
																		__lowercase													=self.tokenizer(lowercase_	,							**lowercase_)
											else:
																		__lowercase													=None

											if audio_target is not None:
																		__lowercase													=self.feature_extractor(audio_target=lowercase_	,							*lowercase_	,							sampling_rate=lowercase_	,							**lowercase_)
																		__lowercase													=targets['input_values']
											elif text_target is not None:
																		__lowercase													=self.tokenizer(lowercase_	,							**lowercase_)
																		__lowercase													=targets['input_ids']
											else:
																		__lowercase													=None

											if inputs is None:
																		return targets

											if targets is not None:
																		__lowercase													=labels

																		__lowercase													=targets.get('attention_mask')
																		if decoder_attention_mask is not None:
																									__lowercase													=decoder_attention_mask

											return inputs

				def 					__lowerCamelCase  (				self	:	Dict	,							*_lowerCAmelCase	:	Tuple	,							**_lowerCAmelCase	:	Optional[int]):







											'''simple docstring'''

											__lowercase													=kwargs.pop('input_values'	,							lowercase_)
											__lowercase													=kwargs.pop('input_ids'	,							lowercase_)
											__lowercase													=kwargs.pop('labels'	,							lowercase_)

											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:
																		__lowercase													=self.feature_extractor.pad(lowercase_	,							*lowercase_	,							**lowercase_)
											elif input_ids is not None:
																		__lowercase													=self.tokenizer.pad(lowercase_	,							**lowercase_)
											else:
																		__lowercase													=None

											if labels is not None:
																		if "input_ids" in labels or (isinstance(lowercase_	,							lowercase_) and "input_ids" in labels[0]):
																									__lowercase													=self.tokenizer.pad(lowercase_	,							**lowercase_)
																									__lowercase													=targets['input_ids']
																		else:
																									__lowercase													=self.feature_extractor.feature_size
																									__lowercase													=self.feature_extractor.num_mel_bins
																									__lowercase													=self.feature_extractor.pad(lowercase_	,							*lowercase_	,							**lowercase_)
																									__lowercase													=feature_size_hack
																									__lowercase													=targets['input_values']
											else:
																		__lowercase													=None

											if inputs is None:
																		return targets

											if targets is not None:
																		__lowercase													=labels

																		__lowercase													=targets.get('attention_mask')
																		if decoder_attention_mask is not None:
																									__lowercase													=decoder_attention_mask

											return inputs

				def 					__lowerCamelCase  (				self	:	Dict	,							*_lowerCAmelCase	:	Any	,							**_lowerCAmelCase	:	int):







											'''simple docstring'''

											return self.tokenizer.batch_decode(*lowercase_	,							**lowercase_)

				def 					__lowerCamelCase  (				self	:	int	,							*_lowerCAmelCase	:	str	,							**_lowerCAmelCase	:	Optional[int]):







											'''simple docstring'''

											return self.tokenizer.decode(*lowercase_	,							**lowercase_)





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



