"""simple docstring"""






from typing import List, Optional

import numpy as np

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





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[int]													=			'EncodecFeatureExtractor'
      snake_case__    :     List[str]													=			('T5Tokenizer', 'T5TokenizerFast')

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            super().__init__(__lowerCAmelCase	, __lowerCAmelCase		)
            lowercase														=			self.feature_extractor
            lowercase														=			False

      def        A__ (		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=True		):







            """simple docstring"""



            return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase	, language=__lowerCAmelCase	, no_timestamps=__lowerCAmelCase		)

      def __call__(		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



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

            lowercase														=			kwargs.pop("""audio"""	, __lowerCAmelCase		)
            lowercase														=			kwargs.pop("""sampling_rate"""	, __lowerCAmelCase		)
            lowercase														=			kwargs.pop("""text"""	, __lowerCAmelCase		)
            if len(__lowerCAmelCase		) > 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(__lowerCAmelCase	, **__lowerCAmelCase		)

            if audio is not None:
                  lowercase														=			self.feature_extractor(__lowerCAmelCase	, *__lowerCAmelCase	, sampling_rate=__lowerCAmelCase	, **__lowerCAmelCase		)

            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        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.pop("""audio"""	, __lowerCAmelCase		)
            lowercase														=			kwargs.pop("""padding_mask"""	, __lowerCAmelCase		)

            if len(__lowerCAmelCase		) > 0:
                  lowercase														=			args[0]
                  lowercase														=			args[1:]

            if audio_values is not None:
                  return self._decode_audio(__lowerCAmelCase	, padding_mask=__lowerCAmelCase		)
            else:
                  return self.tokenizer.batch_decode(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



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







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			to_numpy(__lowerCAmelCase		)
            lowercase							,							lowercase							,							lowercase														=			audio_values.shape

            if padding_mask is None:
                  return list(__lowerCAmelCase		)

            lowercase														=			to_numpy(__lowerCAmelCase		)

            # 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(__lowerCAmelCase	, ((0, 0), (0, difference))	, """constant"""	, constant_values=__lowerCAmelCase		)

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

            return audio_values




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  lowercase														=			words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # add the word
                        aligned_words_list.append(line[i]	)
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """	)
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]	)
                  # join the aligned words list to form a justified line
                  return "".join(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


if __name__ == "__main__":
       from doctest import testmod

       testmod()



