from ...processing_utils import ProcessorMixin


class 		a__				(  _UpperCamelCase						):
			A__							:  Optional[int]														=     "SpeechT5FeatureExtractor"
			A__							:  str														=     "SpeechT5Tokenizer"



			def __init__(		self ,		UpperCAmelCase ,		UpperCAmelCase       )					->       Optional[int]:
								super().__init__(__a ,		__a       )



			def __call__(		self ,		*UpperCAmelCase ,		**UpperCAmelCase       )					->       str:
								__a						   =  kwargs.pop('audio' ,		__a       )
								__a						   =  kwargs.pop('text' ,		__a       )
								__a						   =  kwargs.pop('text_target' ,		__a       )
								__a						   =  kwargs.pop('audio_target' ,		__a       )
								__a						   =  kwargs.pop('sampling_rate' ,		__a       )

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

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

								if inputs is None:
													return targets

								if targets is not None:
													__a						   =  labels

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

								return inputs



			def __SCREAMING_SNAKE_CASE				(		self ,		*UpperCAmelCase ,		**UpperCAmelCase       )					->       List[Any]:
								__a						   =  kwargs.pop('input_values' ,		__a       )
								__a						   =  kwargs.pop('input_ids' ,		__a       )
								__a						   =  kwargs.pop('labels' ,		__a       )

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

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

								if inputs is None:
													return targets

								if targets is not None:
													__a						   =  labels

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

								return inputs



			def __SCREAMING_SNAKE_CASE				(		self ,		*UpperCAmelCase ,		**UpperCAmelCase       )					->       Optional[int]:
								return self.tokenizer.batch_decode(*__a ,		**__a       )







			def __SCREAMING_SNAKE_CASE				(		self ,		*UpperCAmelCase ,		**UpperCAmelCase       )					->       Dict:
								return self.tokenizer.decode(*__a ,		**__a       )











"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:int						  =       "mobilenet_v1"

  def __init__(		self :							Optional[int]       , __a :							List[str]=3       , __a :							Union[str, Any]=224       , __a :							Tuple=1.0       , __a :							List[Any]=8       , __a :							Union[str, Any]="relu6"       , __a :							Dict=True       , __a :							Tuple=0.9_99       , __a :							Dict=0.02       , __a :							Any=0.0_01       , **__a :							Any       , )		->					int:
        super().__init__(**__a						)

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero."						)

        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							depth_multiplier
        _UpperCamelCase   :						int    =							min_depth
        _UpperCamelCase   :						List[str]    =							hidden_act
        _UpperCamelCase   :						Union[str, Any]    =							tf_padding
        _UpperCamelCase   :						int    =							classifier_dropout_prob
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Union[str, Any]    =							layer_norm_eps





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       version.parse("1.11" )

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})]						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})]						)
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					float:
        return 1e-4
