





from ...processing_utils import ProcessorMixin






class        _a(    a__    ):
		lowerCamelCase__     :int   				=		"SpeechT5FeatureExtractor"
		lowerCamelCase__     :Optional[int]   				=		"SpeechT5Tokenizer"







		def __init__(    self ,						__snake_case ,						__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							super().__init__(lowerCamelCase_ ,						lowerCamelCase_				)







		def __call__(    self ,						*__snake_case ,						**__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   kwargs.pop("audio" ,						lowerCamelCase_				)
							_snake_case       :					Tuple				       =   kwargs.pop("text" ,						lowerCamelCase_				)
							_snake_case       :					int				       =   kwargs.pop("text_target" ,						lowerCamelCase_				)
							_snake_case       :					int				       =   kwargs.pop("audio_target" ,						lowerCamelCase_				)
							_snake_case       :					Optional[Any]				       =   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:
												_snake_case       :					Optional[Any]				       =   self.feature_extractor(lowerCamelCase_ ,						*lowerCamelCase_ ,						sampling_rate=lowerCamelCase_ ,						**lowerCamelCase_				)
							elif text is not None:
												_snake_case       :					List[Any]				       =   self.tokenizer(lowerCamelCase_ ,						**lowerCamelCase_				)
							else:
												_snake_case       :					Optional[Any]				       =   None

							if audio_target is not None:
												_snake_case       :					Tuple				       =   self.feature_extractor(audio_target=lowerCamelCase_ ,						*lowerCamelCase_ ,						sampling_rate=lowerCamelCase_ ,						**lowerCamelCase_				)
												_snake_case       :					Union[str, Any]				       =   targets["input_values"]
							elif text_target is not None:
												_snake_case       :					List[Any]				       =   self.tokenizer(lowerCamelCase_ ,						**lowerCamelCase_				)
												_snake_case       :					str				       =   targets["input_ids"]
							else:
												_snake_case       :					Tuple				       =   None

							if inputs is None:
												return targets

							if targets is not None:
												_snake_case       :					List[Any]				       =   labels

												_snake_case       :					Any				       =   targets.get("attention_mask"				)
												if decoder_attention_mask is not None:
																	_snake_case       :					List[Any]				       =   decoder_attention_mask

							return inputs







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   kwargs.pop("input_values" ,						lowerCamelCase_				)
							_snake_case       :					Union[str, Any]				       =   kwargs.pop("input_ids" ,						lowerCamelCase_				)
							_snake_case       :					Optional[int]				       =   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:
												_snake_case       :					Optional[int]				       =   self.feature_extractor.pad(lowerCamelCase_ ,						*lowerCamelCase_ ,						**lowerCamelCase_				)
							elif input_ids is not None:
												_snake_case       :					List[str]				       =   self.tokenizer.pad(lowerCamelCase_ ,						**lowerCamelCase_				)
							else:
												_snake_case       :					Tuple				       =   None

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

							if inputs is None:
												return targets

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

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

							return inputs







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      Dict:


							'''simple docstring'''

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







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)

__lowerCAmelCase :int     =    {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}






class        _a(    __A    ):
		lowerCamelCase__     :Optional[Any]   				=		'timesformer'







		def __init__(    self ,						__snake_case=2_2_4 ,						__snake_case=1_6 ,						__snake_case=3 ,						__snake_case=8 ,						__snake_case=7_6_8 ,						__snake_case=1_2 ,						__snake_case=1_2 ,						__snake_case=3_0_7_2 ,						__snake_case="gelu" ,						__snake_case=0.0 ,						__snake_case=0.0 ,						__snake_case=0.02 ,						__snake_case=1E-6 ,						__snake_case=True ,						__snake_case="divided_space_time" ,						__snake_case=0 ,						**__snake_case ,						)      ->      str:


							'''simple docstring'''

							super().__init__(**__snake_case				)

							_snake_case       :					Optional[Any]				       =   image_size
							_snake_case       :					Optional[int]				       =   patch_size
							_snake_case       :					str				       =   num_channels
							_snake_case       :					Tuple				       =   num_frames

							_snake_case       :					Union[str, Any]				       =   hidden_size
							_snake_case       :					Optional[int]				       =   num_hidden_layers
							_snake_case       :					List[Any]				       =   num_attention_heads
							_snake_case       :					Optional[int]				       =   intermediate_size
							_snake_case       :					List[str]				       =   hidden_act
							_snake_case       :					Optional[int]				       =   hidden_dropout_prob
							_snake_case       :					Optional[Any]				       =   attention_probs_dropout_prob
							_snake_case       :					Dict				       =   initializer_range
							_snake_case       :					Optional[int]				       =   layer_norm_eps
							_snake_case       :					str				       =   qkv_bias

							_snake_case       :					List[str]				       =   attention_type
							_snake_case       :					Optional[int]				       =   drop_path_rate