'''simple docstring'''







from ...processing_utils import ProcessorMixin




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Optional[Any]     		= 'SpeechT5FeatureExtractor'
						A_			:							Tuple     		= 'SpeechT5Tokenizer'




						def __init__(    self :       Tuple							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       str      )     ->				Optional[Any]:
													super().__init__(UpperCamelCase_							,							UpperCamelCase_      )




						def __call__(    self :       int							,							*UpperCamelCase_ :       List[Any]							,							**UpperCamelCase_ :       int      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :str						    = kwargs.pop('audio'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = kwargs.pop('text'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = kwargs.pop('text_target'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = kwargs.pop('audio_target'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = kwargs.pop('sampling_rate'							,							UpperCamelCase_      )

													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:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.feature_extractor(UpperCamelCase_							,							*UpperCamelCase_							,							sampling_rate=UpperCamelCase_							,							**UpperCamelCase_      )
													elif text is not None:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = self.tokenizer(UpperCamelCase_							,							**UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = None

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

													if inputs is None:
																				return targets

													if targets is not None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = labels

																				SCREAMING_SNAKE_CASE__     :Dict						    = targets.get('attention_mask'      )
																				if decoder_attention_mask is not None:
																											SCREAMING_SNAKE_CASE__     :int						    = decoder_attention_mask

													return inputs




						def      __lowerCamelCase       (    self :       Optional[int]							,							*UpperCamelCase_ :       str							,							**UpperCamelCase_ :       Tuple      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = kwargs.pop('input_values'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = kwargs.pop('input_ids'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = kwargs.pop('labels'							,							UpperCamelCase_      )

													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:
																				SCREAMING_SNAKE_CASE__     :Any						    = self.feature_extractor.pad(UpperCamelCase_							,							*UpperCamelCase_							,							**UpperCamelCase_      )
													elif input_ids is not None:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.tokenizer.pad(UpperCamelCase_							,							**UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = None

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

													if inputs is None:
																				return targets

													if targets is not None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = labels

																				SCREAMING_SNAKE_CASE__     :str						    = targets.get('attention_mask'      )
																				if decoder_attention_mask is not None:
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = decoder_attention_mask

													return inputs




						def      __lowerCamelCase       (    self :       Any							,							*UpperCamelCase_ :       Tuple							,							**UpperCamelCase_ :       Dict      )     ->				Optional[int]:
													return self.tokenizer.batch_decode(*UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple							,							*UpperCamelCase_ :       int							,							**UpperCamelCase_ :       Dict      )     ->				Optional[Any]:
													return self.tokenizer.decode(*UpperCamelCase_							,							**UpperCamelCase_      )







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_      	=	{
    '''configuration_blenderbot_small''': [
        '''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotSmallConfig''',
        '''BlenderbotSmallOnnxConfig''',
    ],
    '''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BlenderbotSmallTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlenderbotSmallForCausalLM''',
		    '''BlenderbotSmallForConditionalGeneration''',
		    '''BlenderbotSmallModel''',
		    '''BlenderbotSmallPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TFBlenderbotSmallForConditionalGeneration''',
		    '''TFBlenderbotSmallModel''',
		    '''TFBlenderbotSmallPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''FlaxBlenderbotSmallForConditionalGeneration''',
		    '''FlaxBlenderbotSmallModel''',
		    '''FlaxBlenderbotSmallPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

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






