
'''simple docstring'''







import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

_lowercase				:						List[str]												= logging.get_logger(__name__)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				bytes   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						lowercase_			:  List[str]									= F'''{sampling_rate}'''
						lowercase_			:  Tuple									= '''1'''
						lowercase_			:  List[str]									= '''f32le'''
						lowercase_			:  str									= [
						    '''ffmpeg''',
						    '''-i''',
						    '''pipe:0''',
						    '''-ac''',
						    ac,
						    '''-ar''',
						    ar,
						    '''-f''',
						    format_for_conversion,
						    '''-hide_banner''',
						    '''-loglevel''',
						    '''quiet''',
						    '''pipe:1''',
						]

						try:
												lowercase_			:  Union[str, Any]									= subprocess.Popen(__SCREAMING_SNAKE_CASE   ,   stdin=subprocess.PIPE   ,   stdout=subprocess.PIPE							)
						except FileNotFoundError:
												raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''							)
						lowercase_			:  List[Any]									= ffmpeg_process.communicate(__SCREAMING_SNAKE_CASE							)
						lowercase_			:  Union[str, Any]									= output_stream[0]

						lowercase_			:  int									= np.frombuffer(__SCREAMING_SNAKE_CASE   ,   np.floataa							)
						if audio.shape[0] == 0:
												raise ValueError('''Malformed soundfile'''							)
						return audio






@add_end_docstrings(lowerCamelCase_  )
class 				lowerCAmelCase__     (						lowerCamelCase_  ):






					def __init__(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  List[str]									= 5
											super().__init__(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if self.framework != "pt":
																	raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''	)

											self.check_model_type(__SCREAMING_SNAKE_CASE	)






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


											"""simple docstring"""





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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Union[str, Any]									= {}
											if top_k is not None:
																	if top_k > self.model.config.num_labels:
																							lowercase_			:  Tuple									= self.model.config.num_labels
																	lowercase_			:  List[str]									= top_k
											return {}, {}, postprocess_params






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	if inputs.startswith('''http://'''	) or inputs.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_			:  Optional[Any]									= requests.get(__SCREAMING_SNAKE_CASE	).content
																	else:
																							with open(__SCREAMING_SNAKE_CASE       ,							'''rb'''	) as f:
																													lowercase_			:  Union[str, Any]									= f.read()

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= ffmpeg_read(__SCREAMING_SNAKE_CASE       ,							self.feature_extractor.sampling_rate	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	# Accepting `"array"` which is the key defined in `datasets` for
																	# better integration
																	if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																							raise ValueError(
																							    '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
																							    '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
																							    '''containing the sampling_rate associated with that array'''	)

																	lowercase_			:  Tuple									= inputs.pop('''raw'''       ,							__SCREAMING_SNAKE_CASE	)
																	if _inputs is None:
																							# Remove path which will not be used from `datasets`.
																							inputs.pop('''path'''       ,							__SCREAMING_SNAKE_CASE	)
																							lowercase_			:  str									= inputs.pop('''array'''       ,							__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  List[Any]									= inputs.pop('''sampling_rate'''	)
																	lowercase_			:  Any									= _inputs
																	if in_sampling_rate != self.feature_extractor.sampling_rate:
																							import torch

																							if is_torchaudio_available():
																													from torchaudio import functional as F
																							else:
																													raise ImportError(
																													    '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
																													    '''The torchaudio package can be installed through: `pip install torchaudio`.'''	)

																							lowercase_			:  Tuple									= F.resample(
																							    torch.from_numpy(__SCREAMING_SNAKE_CASE	)       ,							__SCREAMING_SNAKE_CASE       ,							self.feature_extractor.sampling_rate	).numpy()

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

											lowercase_			:  str									= self.feature_extractor(
											    __SCREAMING_SNAKE_CASE       ,							sampling_rate=self.feature_extractor.sampling_rate       ,							return_tensors='''pt'''	)
											return processed






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  str									= self.model(**__SCREAMING_SNAKE_CASE	)
											return model_outputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=5	):


											"""simple docstring"""





											lowercase_			:  Tuple									= model_outputs.logits[0].softmax(-1	)
											lowercase_		,				lowercase_			:  Tuple									= probs.topk(__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Any									= scores.tolist()
											lowercase_			:  Any									= ids.tolist()

											lowercase_			:  Optional[Any]									= [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)]

											return labels





'''simple docstring'''







from __future__ import annotations

from queue import Queue






class 				lowerCAmelCase__     :






					def __init__(     self	):


											"""simple docstring"""





											lowercase_			:  dict[int, list[int]]									= {}






					def 			_snake_case					(     self	):


											"""simple docstring"""





											for i in self.vertices:
																	print(__SCREAMING_SNAKE_CASE       ,							''' : '''       ,							''' -> '''.join([str(__SCREAMING_SNAKE_CASE	) for j in self.vertices[i]]	)	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if from_vertex in self.vertices:
																	self.vertices[from_vertex].append(__SCREAMING_SNAKE_CASE	)
											else:
																	lowercase_			:  Optional[Any]									= [to_vertex]






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= set()

											# create a first in first out queue to store all the vertices for BFS
											lowercase_			:  Queue									= Queue()

											# mark the source node as visited and enqueue it
											visited.add(__SCREAMING_SNAKE_CASE	)
											queue.put(__SCREAMING_SNAKE_CASE	)

											while not queue.empty():
																	lowercase_			:  List[Any]									= queue.get()

																	# loop through all adjacent vertex and enqueue it if not yet visited
																	for adjacent_vertex in self.vertices[vertex]:
																							if adjacent_vertex not in visited:
																													queue.put(__SCREAMING_SNAKE_CASE	)
																													visited.add(__SCREAMING_SNAKE_CASE	)
											return visited


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

							testmod(verbose=True)

							_lowercase				:						Optional[int]												= Graph()
							g.add_edge(0, 1)
							g.add_edge(0, 2)
							g.add_edge(1, 2)
							g.add_edge(2, 0)
							g.add_edge(2, 3)
							g.add_edge(3, 3)

							g.print_graph()
							# 0  :  1 -> 2
							# 1  :  2
							# 2  :  0 -> 3
							# 3  :  3

							assert sorted(g.bfs(2)) == [0, 1, 2, 3]



