"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)
@add_end_docstrings(__A     )
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      **snake_case_							):







										'''simple docstring'''


										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   ,      snake_case_   ,      **snake_case_							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     Any								=			{}
										if "candidate_labels" in kwargs:
																	A__       :     Dict								=			kwargs["""candidate_labels"""]
										if "hypothesis_template" in kwargs:
																	A__       :     Optional[Any]								=			kwargs["""hypothesis_template"""]

										return preprocess_params, {}, {}





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=None   ,      snake_case_="This is a sound of {}."							):







										'''simple docstring'''


										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
																								A__       :     List[str]								=			requests.get(snake_case_							).content
																	else:
																								with open(snake_case_   ,      """rb"""							) as f:
																															A__       :     Tuple								=			f.read()

										if isinstance(snake_case_   ,      snake_case_							):
																	A__       :     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"""							)

										A__       :     str								=			self.feature_extractor(
										    [audio]   ,      sampling_rate=self.feature_extractor.sampling_rate   ,      return_tensors="""pt"""							)
										A__       :     str								=			candidate_labels
										A__       :     Any								=			[hypothesis_template.format(snake_case_							) for x in candidate_labels]
										A__       :     int								=			self.tokenizer(snake_case_   ,      return_tensors=self.framework   ,      padding=snake_case_							)
										A__       :     Dict								=			[text_inputs]
										return inputs





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[Any]								=			model_inputs.pop("""candidate_labels"""							)
										A__       :     str								=			model_inputs.pop("""text_inputs"""							)
										if isinstance(text_inputs[0]   ,      snake_case_							):
																	A__       :     str								=			text_inputs[0]
										else:
																	# Batching case.
																	A__       :     List[Any]								=			text_inputs[0][0]

										A__       :     Union[str, Any]								=			self.model(**snake_case_   ,      **snake_case_							)

										A__       :     Optional[int]								=			{
										    """candidate_labels""": candidate_labels,
										    """logits""": outputs.logits_per_audio,
										}
										return model_outputs





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			model_outputs.pop("""candidate_labels"""							)
										A__       :     Any								=			model_outputs["""logits"""][0]

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

										A__       :     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






"""simple docstring"""



from __future__ import annotations

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) == 0:
														return []
							A__				,							A__       :     Dict								=			min(lowerCAmelCase							), max(lowerCAmelCase							)
							A__       :     List[Any]								=			int(max_value - min_value							) + 1
							A__       :     list[list]								=			[[] for _ in range(lowerCAmelCase							)]

							for i in my_list:
														buckets[int(i - min_value							)].append(lowerCAmelCase							)

							return [v for bucket in buckets for v in sorted(lowerCAmelCase							)]


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

				testmod()
				assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





