


"""simple docstring"""

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy





class  lowerCamelCase__    (		_a     ):
      a			:  Dict           =		"""EncodecFeatureExtractor"""
      a			:  str           =		("""T5Tokenizer""", """T5TokenizerFast""")
      def __init__(		self						:						List[Any]   ,							A_						:						List[str]   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       super().__init__(A_   ,							A_				)
       __lowercase						=						self.feature_extractor
       __lowercase						=						False
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						str=None   ,							A_						:						Union[str, Any]=None   ,							A_						:						str=True				):






       '''simple docstring'''


       return self.tokenizer.get_decoder_prompt_ids(task=A_   ,							language=A_   ,							no_timestamps=A_				)
      def __call__(		self						:						Any   ,							*A_						:						int   ,							**A_						:						Optional[Any]				):






       '''simple docstring'''


       if self._in_target_context_manager:
        return self.current_processor(*A_   ,							**A_				)

       __lowercase						=						kwargs.pop("""audio"""   ,							A_				)
       __lowercase						=						kwargs.pop("""sampling_rate"""   ,							A_				)
       __lowercase						=						kwargs.pop("""text"""   ,							A_				)
       if len(A_				) > 0:
        __lowercase						=						args[0]
        __lowercase						=						args[1:]

       if audio is None and text is None:
        raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

       if text is not None:
        __lowercase						=						self.tokenizer(A_   ,							**A_				)

       if audio is not None:
        __lowercase						=						self.feature_extractor(A_   ,							*A_   ,							sampling_rate=A_   ,							**A_				)

       if audio is None:
        return inputs

       elif text is None:
        return audio_inputs

       else:
        __lowercase						=						audio_inputs["""input_values"""]
        if "padding_mask" in audio_inputs:
         __lowercase						=						audio_inputs["""padding_mask"""]
        return inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							*A_						:						Optional[Any]   ,							**A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						kwargs.pop("""audio"""   ,							A_				)
       __lowercase						=						kwargs.pop("""padding_mask"""   ,							A_				)

       if len(A_				) > 0:
        __lowercase						=						args[0]
        __lowercase						=						args[1:]

       if audio_values is not None:
        return self._decode_audio(A_   ,							padding_mask=A_				)
       else:
        return self.tokenizer.batch_decode(*A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							*A_						:						List[str]   ,							**A_						:						List[Any]				):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]   ,							A_						:						int   ,							A_						:						Optional = None				):






       '''simple docstring'''


       __lowercase						=						to_numpy(A_				)
       __lowercase				,		__lowercase				,		__lowercase						=						audio_values.shape

       if padding_mask is None:
        return list(A_				)

       __lowercase						=						to_numpy(A_				)

       # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
       # token (so that the generated audio values are **not** treated as padded tokens)
       __lowercase						=						seq_len - padding_mask.shape[-1]
       __lowercase						=						1 - self.feature_extractor.padding_value
       __lowercase						=						np.pad(A_   ,							((0, 0), (0, difference))   ,							"""constant"""   ,							constant_values=A_				)

       __lowercase						=						audio_values.tolist()
       for i in range(A_				):
        __lowercase						=						np.asarray(audio_values[i]				)[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        __lowercase						=						sliced_audio.reshape(A_   ,							-1				)

       return audio_values




"""simple docstring"""

from PIL import Image


def lowerCAmelCase_   (					UpperCamelCase__						:			Image					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						(259 * (level + 255)) / (255 * (259 - level))

 def contrast(UpperCamelCase__						:			int			) -> int:
  return int(128 + factor * (c - 128)			)

 return img.point(UpperCamelCase__			)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change contrast to 170
              UpperCAmelCase__					=change_contrast(img, 170)
              cont_img.save("image_data/lena_high_contrast.png", format="png")
