


'''simple docstring'''

from __future__ import annotations

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





@dataclass
class 	snake_case       :





  """simple docstring"""




  _lowerCamelCase						       =    42
  _lowerCamelCase						       =    42





class 	snake_case       :





  """simple docstring"""




  def __init__(       self     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    [[] for _ in range(A_	)]
   lowerCamelCase_						=    size






  def __getitem__(       self     ,    UpperCamelCase	):







   """simple docstring"""
   return iter(self._graph[vertex]	)






  @property
  def     snake_case						(       self	):







   """simple docstring"""
   return self._size






  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase	):







   """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(A_     ,    A_	)	)







  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    deque([start_vertex]	)
   lowerCamelCase_						=    [None] * self.size
   lowerCamelCase_						=    0

   while queue:
    lowerCamelCase_						=    queue.popleft()
    lowerCamelCase_						=    distances[current_vertex]
    if current_distance is None:
     continue

    for edge in self[current_vertex]:
     lowerCamelCase_						=    current_distance + edge.weight
     lowerCamelCase_						=    distances[edge.destination_vertex]
     if (
         isinstance(A_     ,    A_	)
         and new_distance >= dest_vertex_distance
     ):
      continue
     lowerCamelCase_						=    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()










from typing import List, Optional

import numpy as np

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

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=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__						=self.tokenizer(A_,		**A_		)

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

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=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  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=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)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

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

    return audio_values



