
'''simple docstring'''





_A     =  frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

_A     =  frozenset(["prompt", "negative_prompt"])

_A     =  frozenset([])

_A     =  frozenset(["image"])

_A     =  frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

_A     =  frozenset(["image"])

_A     =  frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

_A     =  frozenset(["prompt", "image", "negative_prompt"])

_A     =  frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

_A     =  frozenset(["prompt", "image", "mask_image", "negative_prompt"])

_A     =  frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

_A     =  frozenset(["image", "mask_image"])

_A     =  frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

_A     =  frozenset(["example_image", "image", "mask_image"])

_A     =  frozenset(["class_labels"])

_A     =  frozenset(["class_labels"])

_A     =  frozenset(["batch_size"])

_A     =  frozenset([])

_A     =  frozenset(["batch_size"])

_A     =  frozenset([])

_A     =  frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

_A     =  frozenset(["prompt", "negative_prompt"])
_A     =  frozenset(["input_tokens"])

_A     =  frozenset(["input_tokens"])





from ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			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:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

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

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			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:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							elif input_ids is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

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

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



