
"""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


lowerCamelCase__ :       Optional[Any]						     =			logging.get_logger(__name__)




@add_end_docstrings(_UpperCAmelCase   )
class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :Optional[int]		, **lowerCamelCase_    :str					)							->			Optional[int]:







			'''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    :str		, lowerCamelCase_    :Union[np.ndarray, bytes, str]		, **lowerCamelCase_    :List[str]					)							->			Dict:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Dict		, **lowerCamelCase_    :Tuple					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= {}
			if "candidate_labels" in kwargs:
					SCREAMING_SNAKE_CASE       :     Any				= kwargs['''candidate_labels''']
			if "hypothesis_template" in kwargs:
					SCREAMING_SNAKE_CASE       :     Any				= kwargs['''hypothesis_template''']

			return preprocess_params, {}, {}





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :Any="This is a sound of {}."					)							->			Union[str, Any]:







			'''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
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= requests.get(lowerCamelCase_					).content
					else:
							with open(lowerCamelCase_		, '''rb'''					) as f:
									SCREAMING_SNAKE_CASE       :     Any				= f.read()

			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     str				= 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'''					)

			SCREAMING_SNAKE_CASE       :     Any				= self.feature_extractor(
			    [audio]		, sampling_rate=self.feature_extractor.sampling_rate		, return_tensors='''pt'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= candidate_labels
			SCREAMING_SNAKE_CASE       :     Any				= [hypothesis_template.format(lowerCamelCase_					) for x in candidate_labels]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.tokenizer(lowerCamelCase_		, return_tensors=self.framework		, padding=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= [text_inputs]
			return inputs





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Dict					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_inputs.pop('''candidate_labels'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_inputs.pop('''text_inputs'''					)
			if isinstance(text_inputs[0]		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= text_inputs[0]
			else:
					# Batching case.
					SCREAMING_SNAKE_CASE       :     List[str]				= text_inputs[0][0]

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.model(**lowerCamelCase_		, **lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {
			    '''candidate_labels''': candidate_labels,
			    '''logits''': outputs.logits_per_audio,
			}
			return model_outputs







	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Optional[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= model_outputs.pop('''candidate_labels'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= model_outputs['''logits'''][0]

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

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







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


if __name__ == "__main__":
					print(solution())





