'''simple docstring'''


from typing import List, Optional

import numpy as np

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






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		'''EncodecFeatureExtractor'''
  lowerCamelCase_		:Optional[Any]						   =		('''T5Tokenizer''', '''T5TokenizerFast''')


  def __init__(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       super().__init__(snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       List[str]       =				self.feature_extractor
       UpperCAmelCase_							:       Union[str, Any]       =				False


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=True						):




       '''simple docstring'''

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


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




       '''simple docstring'''

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

       UpperCAmelCase_							:       Tuple       =				kwargs.pop('audio'				,		snake_case_						)
       UpperCAmelCase_							:       Tuple       =				kwargs.pop('sampling_rate'				,		snake_case_						)
       UpperCAmelCase_							:       Union[str, Any]       =				kwargs.pop('text'				,		snake_case_						)
       if len(snake_case_						) > 0:
            UpperCAmelCase_							:       int       =				args[0]
            UpperCAmelCase_							:       Any       =				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:
            UpperCAmelCase_							:       Dict       =				self.tokenizer(snake_case_				,		**snake_case_						)

       if audio is not None:
            UpperCAmelCase_							:       str       =				self.feature_extractor(snake_case_				,		*snake_case_				,		sampling_rate=snake_case_				,		**snake_case_						)

       if audio is None:
            return inputs

       elif text is None:
            return audio_inputs

       else:
            UpperCAmelCase_							:       List[str]       =				audio_inputs['input_values']
            if "padding_mask" in audio_inputs:
                 UpperCAmelCase_							:       Any       =				audio_inputs['padding_mask']
            return inputs


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				kwargs.pop('audio'				,		snake_case_						)
       UpperCAmelCase_							:       List[str]       =				kwargs.pop('padding_mask'				,		snake_case_						)

       if len(snake_case_						) > 0:
            UpperCAmelCase_							:       Optional[int]       =				args[0]
            UpperCAmelCase_							:       Tuple       =				args[1:]

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


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				to_numpy(snake_case_						)
       UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				audio_values.shape

       if padding_mask is None:
            return list(snake_case_						)

       UpperCAmelCase_							:       int       =				to_numpy(snake_case_						)

       # 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)
       UpperCAmelCase_							:       Optional[int]       =				seq_len - padding_mask.shape[-1]
       UpperCAmelCase_							:       str       =				1 - self.feature_extractor.padding_value
       UpperCAmelCase_							:       Any       =				np.pad(snake_case_				,		((0, 0), (0, difference))				,		'constant'				,		constant_values=snake_case_						)

       UpperCAmelCase_							:       Tuple       =				audio_values.tolist()
       for i in range(snake_case_						):
            UpperCAmelCase_							:       Any       =				np.asarray(audio_values[i]						)[
                padding_mask[i][None, :] != self.feature_extractor.padding_value
            ]
            UpperCAmelCase_							:       Optional[Any]       =				sliced_audio.reshape(snake_case_				,		-1						)

       return audio_values







'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:int
  lowerCamelCase_		:int







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       list[list[Edge]]       =				[[] for _ in range(snake_case_						)]
       UpperCAmelCase_							:       Any       =				size


  def __getitem__(			self				,		snake_case_						):




       '''simple docstring'''

       return iter(self._graph[vertex]						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._size


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if weight not in (0, 1):
            raise ValueError('Edge weight must be either 0 or 1.'						)

       if to_vertex < 0 or to_vertex >= self.size:
            raise ValueError('Vertex indexes must be in [0; size).'						)

       self._graph[from_vertex].append(Edge(snake_case_				,		snake_case_						)						)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				deque([start_vertex]						)
       UpperCAmelCase_							:       list[int | None]       =				[None] * self.size
       UpperCAmelCase_							:       str       =				0

       while queue:
            UpperCAmelCase_							:       Optional[int]       =				queue.popleft()
            UpperCAmelCase_							:       List[str]       =				distances[current_vertex]
            if current_distance is None:
                 continue

            for edge in self[current_vertex]:
                 UpperCAmelCase_							:       Union[str, Any]       =				current_distance + edge.weight
                 UpperCAmelCase_							:       Union[str, Any]       =				distances[edge.destination_vertex]
                 if (
                     isinstance(snake_case_				,		snake_case_						)
                     and new_distance >= dest_vertex_distance
                 ):
                      continue
                 UpperCAmelCase_							:       Optional[Any]       =				new_distance
                 if edge.weight == 0:
                      queue.appendleft(edge.destination_vertex						)
                 else:
                      queue.append(edge.destination_vertex						)

       if distances[finish_vertex] is None:
            raise ValueError('No path from start_vertex to finish_vertex.'						)

       return distances[finish_vertex]


if __name__ == "__main__":
 import doctest

 doctest.testmod()






