

from __future__ import annotations

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





@dataclass
class 					__lowerCAmelCase	:
  _a		     =    42
  _a		     =    42





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=[[] for _ in range(lowerCAmelCase   )]
        _lowercase      			=size




  def __getitem__(							self				,  lowerCAmelCase   )    ->					Iterator[Edge]:
        '''simple docstring'''






        return iter(self._graph[vertex]   )




  @property
  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        return self._size




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Any:
        '''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(lowerCAmelCase				,  lowerCAmelCase   )   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					int | None:
        '''simple docstring'''






        _lowercase      			=deque([start_vertex]   )
        _lowercase      			=[None] * self.size
        _lowercase      			=0

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

              for edge in self[current_vertex]:
                    _lowercase      			=current_distance + edge.weight
                    _lowercase      			=distances[edge.destination_vertex]
                    if (
                        isinstance(lowerCAmelCase				,  lowerCAmelCase   )
                        and new_distance >= dest_vertex_distance
                    ):
                          continue
                    _lowercase      			=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 collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowercase_   							=  logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  **lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        if self.framework != "pt":
              raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''   )
        # No specific FOR_XXX available yet




  def __call__(							self				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        return super().__call__(lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			={}
        if "candidate_labels" in kwargs:
              _lowercase      			=kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
              _lowercase      			=kwargs['hypothesis_template']

        return preprocess_params, {}, {}




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase="This is a sound of {}."   )    ->					Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              if audio.startswith('http://'   ) or audio.startswith('https://'   ):
                    # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                    # like http_huggingface_co.png
                    _lowercase      			=requests.get(lowerCAmelCase   ).content
              else:
                    with open(lowerCAmelCase				,  'rb'   ) as f:
                          _lowercase      			=f.read()

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=ffmpeg_read(lowerCAmelCase				,  self.feature_extractor.sampling_rate   )

        if not isinstance(lowerCAmelCase				,  np.ndarray   ):
              raise ValueError('We expect a numpy ndarray as input'   )
        if len(audio.shape   ) != 1:
              raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'   )

        _lowercase      			=self.feature_extractor(
            [audio]				,  sampling_rate=self.feature_extractor.sampling_rate				,  return_tensors='pt'   )
        _lowercase      			=candidate_labels
        _lowercase      			=[hypothesis_template.format(lowerCAmelCase   ) for x in candidate_labels]
        _lowercase      			=self.tokenizer(lowerCAmelCase				,  return_tensors=self.framework				,  padding=lowerCAmelCase   )
        _lowercase      			=[text_inputs]
        return inputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=model_inputs.pop('candidate_labels'   )
        _lowercase      			=model_inputs.pop('text_inputs'   )
        if isinstance(text_inputs[0]				,  lowerCAmelCase   ):
              _lowercase      			=text_inputs[0]
        else:
              # Batching case.
              _lowercase      			=text_inputs[0][0]

        _lowercase      			=self.model(**lowerCAmelCase				,  **lowerCAmelCase   )

        _lowercase      			={
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_audio,
        }
        return model_outputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=model_outputs.pop('candidate_labels'   )
        _lowercase      			=model_outputs['logits'][0]

        if self.framework == "pt":
              _lowercase      			=logits.softmax(dim=0   )
              _lowercase      			=probs.tolist()
        else:
              raise ValueError('`tf` framework not supported.'   )

        _lowercase      			=[
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase				,  lowerCAmelCase   )				,  key=lambda lowerCAmelCase   : -x[0]   )
        ]
        return result


