



def        lowercase_							(A : Optional[int]      ):
       return [
           {
               0: [1, 2],
               1: [0, 2],
               2: [0, 1, 3, 5],
               3: [2, 4],
               4: [3],
               5: [2, 6, 8],
               6: [5, 7],
               7: [6, 8],
               8: [5, 7],
           },
           {
               0: [6],
               1: [9],
               2: [4, 5],
               3: [4],
               4: [2, 3],
               5: [2],
               6: [0, 7],
               7: [6],
               8: [],
               9: [1],
           },
           {
               0: [4],
               1: [6],
               2: [],
               3: [5, 6, 7],
               4: [0, 6],
               5: [3, 8, 9],
               6: [1, 3, 4, 7],
               7: [3, 6, 8, 9],
               8: [5, 7],
               9: [5, 7],
           },
           {
               0: [1, 3],
               1: [0, 2, 4],
               2: [1, 3, 4],
               3: [0, 2, 4],
               4: [1, 2, 3],
           },
       ][index]
def        lowercase_							(A : dict[int, list[int]]      ):
       snake_case__							:	Optional[Any]   	=	0
       snake_case__							:	Union[str, Any]   	=	len(A      )  # No of vertices in graph
       snake_case__							:	Any   	=	[0] * n
       snake_case__							:	Optional[int]   	=	[False] * n

       def dfs(A : Dict		,					A : Optional[int]		,					A : Tuple		,					A : str      ):
              snake_case__							:	Optional[Any]   	=	True
              snake_case__							:	int   	=	id_
              id_ += 1
              for to in graph[at]:
                     if to == parent:
                            pass
                     elif not visited[to]:
                            dfs(A		,					A		,					A		,					id_      )
                            snake_case__							:	Any   	=	min(low[at]		,					low[to]      )
                            if id_ <= low[to]:
                                   bridges.append((at, to) if at < to else (to, at)      )
                     else:
                            # This edge is a back edge and cannot be a bridge
                            snake_case__							:	List[str]   	=	min(low[at]		,					low[to]      )

       snake_case__							:	list[tuple[int, int]]   	=	[]
       for i in range(A      ):
              if not visited[i]:
                     dfs(A		,					-1		,					A		,					id_      )
       return bridges


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


a_   :List[Any]        =      logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], **_snake_case  :  str					)   ->Dict:
            super().__init__(**_snake_case					)

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






     def __call__(       self  :  Union[str, Any], _snake_case  :  Union[np.ndarray, bytes, str], **_snake_case  :  Tuple					)   ->Dict:
            return super().__call__(_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, **_snake_case  :  Any					)   ->Union[str, Any]:
            snake_case__							:	str   	=	{}
            if "candidate_labels" in kwargs:
                   snake_case__							:	str   	=	kwargs['candidate_labels']
            if "hypothesis_template" in kwargs:
                   snake_case__							:	str   	=	kwargs['hypothesis_template']

            return preprocess_params, {}, {}






     def    lowercase_   (       self  :  Dict, _snake_case  :  str, _snake_case  :  Optional[int]=None, _snake_case  :  List[str]="This is a sound of {}."					)   ->int:
            if isinstance(_snake_case, _snake_case					):
                   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
                          snake_case__							:	List[Any]   	=	requests.get(_snake_case					).content
                   else:
                          with open(_snake_case, 'rb'					) as f:
                                 snake_case__							:	Union[str, Any]   	=	f.read()

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	List[Any]   	=	ffmpeg_read(_snake_case, self.feature_extractor.sampling_rate					)

            if not isinstance(_snake_case, 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'					)

            snake_case__							:	Tuple   	=	self.feature_extractor(
                [audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='pt'					)
            snake_case__							:	int   	=	candidate_labels
            snake_case__							:	int   	=	[hypothesis_template.format(_snake_case					) for x in candidate_labels]
            snake_case__							:	Optional[int]   	=	self.tokenizer(_snake_case, return_tensors=self.framework, padding=_snake_case					)
            snake_case__							:	List[Any]   	=	[text_inputs]
            return inputs






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any]					)   ->int:
            snake_case__							:	Optional[int]   	=	model_inputs.pop('candidate_labels'					)
            snake_case__							:	str   	=	model_inputs.pop('text_inputs'					)
            if isinstance(text_inputs[0], _snake_case					):
                   snake_case__							:	Optional[Any]   	=	text_inputs[0]
            else:
                   # Batching case.
                   snake_case__							:	int   	=	text_inputs[0][0]

            snake_case__							:	Any   	=	self.model(**_snake_case, **_snake_case					)

            snake_case__							:	List[Any]   	=	{
                'candidate_labels': candidate_labels,
                'logits': outputs.logits_per_audio,
            }
            return model_outputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str					)   ->List[str]:
            snake_case__							:	int   	=	model_outputs.pop('candidate_labels'					)
            snake_case__							:	List[Any]   	=	model_outputs['logits'][0]

            if self.framework == "pt":
                   snake_case__							:	Tuple   	=	logits.softmax(dim=0					)
                   snake_case__							:	Union[str, Any]   	=	probs.tolist()
            else:
                   raise ValueError('`tf` framework not supported.'					)

            snake_case__							:	Union[str, Any]   	=	[
                {'score': score, 'label': candidate_label}
                for score, candidate_label in sorted(zip(_snake_case, _snake_case					), key=lambda _snake_case					: -x[0]					)
            ]
            return result






