from ...processing_utils import ProcessorMixin
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """SpeechT5FeatureExtractor"""
  _SCREAMING_SNAKE_CASE     					=      """SpeechT5Tokenizer"""

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   super().__init__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def __call__(		self			:   Tuple  ,				*__SCREAMING_SNAKE_CASE			:   Tuple  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	kwargs.pop("audio"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("text"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("text_target"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("audio_target"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("sampling_rate"  ,				__SCREAMING_SNAKE_CASE							)

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

   if audio_target is not None:
    __a							=	self.feature_extractor(audio_target=__SCREAMING_SNAKE_CASE  ,				*__SCREAMING_SNAKE_CASE  ,				sampling_rate=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
    __a							=	targets["input_values"]
   elif text_target is not None:
    __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
    __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 		_UpperCAmelCase (		self			:   Tuple  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   __a							=	kwargs.pop("input_values"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("input_ids"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("labels"  ,				__SCREAMING_SNAKE_CASE							)

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

   if labels is not None:
    if "input_ids" in labels or (isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) and "input_ids" in labels[0]):
     __a							=	self.tokenizer.pad(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
     __a							=	targets["input_ids"]
    else:
     __a							=	self.feature_extractor.feature_size
     __a							=	self.feature_extractor.num_mel_bins
     __a							=	self.feature_extractor.pad(__SCREAMING_SNAKE_CASE  ,				*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
     __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 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Any							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Tuple  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	None
   if "feature_extractor" in kwargs:
    warnings.warn(
        "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
        " instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
    __a							=	kwargs.pop("feature_extractor"							)

   __a							=	image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("You need to specify an `image_processor`."							)
   if tokenizer is None:
    raise ValueError("You need to specify a `tokenizer`."							)

   super().__init__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):

   if text is None and images is None:
    raise ValueError("You have to specify either text or images. Both cannot be none."							)

   if text is not None:
    __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if text is not None and images is not None:
    __a							=	image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE							)  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.tokenizer.model_input_names
   __a							=	self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
   return self.image_processor_class


