






'''simple docstring'''

from ...processing_utils import ProcessorMixin






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """SpeechT5FeatureExtractor"""
  lowercase  	=   """SpeechT5Tokenizer"""

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        super().__init__(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def __call__(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      kwargs.pop("""audio"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      kwargs.pop("""text"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      kwargs.pop("""text_target"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      kwargs.pop("""audio_target"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      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:
              UpperCamelCase       						=      self.feature_extractor(_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE    ,	sampling_rate=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        elif text is not None:
              UpperCamelCase       						=      self.tokenizer(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      None

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

        if inputs is None:
              return targets

        if targets is not None:
              UpperCamelCase       						=      labels

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

        return inputs

  def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      kwargs.pop("""input_values"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      kwargs.pop("""input_ids"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      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:
              UpperCamelCase       						=      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        elif input_ids is not None:
              UpperCamelCase       						=      self.tokenizer.pad(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      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]):
                    UpperCamelCase       						=      self.tokenizer.pad(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      targets["""input_ids"""]
              else:
                    UpperCamelCase       						=      self.feature_extractor.feature_size
                    UpperCamelCase       						=      self.feature_extractor.num_mel_bins
                    UpperCamelCase       						=      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      feature_size_hack
                    UpperCamelCase       						=      targets["""input_values"""]
        else:
              UpperCamelCase       						=      None

        if inputs is None:
              return targets

        if targets is not None:
              UpperCamelCase       						=      labels

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

        return inputs

  def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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


  def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






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








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)
