






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(lowerCAmelCase_				)
class  UpperCamelCase_      (			lowerCAmelCase_				):



	'''simple docstring'''




	def __init__(       self       ,			**a					)       ->    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       ,			a       ,			**a					)       ->    Dict:
								return super().__call__(_snake_case       ,			**_snake_case					)




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    Union[str, Any]:
								snake_case_				=							{}
								if "candidate_labels" in kwargs:
															snake_case_				=							kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
															snake_case_				=							kwargs['hypothesis_template']

								return preprocess_params, {}, {}




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None       ,			a="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_				=							requests.get(_snake_case					).content
															else:
																						with open(_snake_case       ,			'rb'					) as f:
																													snake_case_				=							f.read()

								if isinstance(_snake_case       ,			_snake_case					):
															snake_case_				=							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_				=							self.feature_extractor(
								    [audio]       ,			sampling_rate=self.feature_extractor.sampling_rate       ,			return_tensors='pt'					)
								snake_case_				=							candidate_labels
								snake_case_				=							[hypothesis_template.format(_snake_case					) for x in candidate_labels]
								snake_case_				=							self.tokenizer(_snake_case       ,			return_tensors=self.framework       ,			padding=_snake_case					)
								snake_case_				=							[text_inputs]
								return inputs




	def 							_UpperCamelCase			(       self       ,			a					)       ->    int:
								snake_case_				=							model_inputs.pop('candidate_labels'					)
								snake_case_				=							model_inputs.pop('text_inputs'					)
								if isinstance(text_inputs[0]       ,			_snake_case					):
															snake_case_				=							text_inputs[0]
								else:
															# Batching case.
															snake_case_				=							text_inputs[0][0]

								snake_case_				=							self.model(**_snake_case       ,			**_snake_case					)

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






	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
								snake_case_				=							model_outputs.pop('candidate_labels'					)
								snake_case_				=							model_outputs['logits'][0]

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

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










from math import factorial
def        lowercase_							(A : int		,					A : int		,					A : float      ):
       if successes > trials:
              raise ValueError('successes must be lower or equal to trials'      )
       if trials < 0 or successes < 0:
              raise ValueError('the function is defined for non-negative integers'      )
       if not isinstance(A		,					A      ) or not isinstance(A		,					A      ):
              raise ValueError('the function is defined for non-negative integers'      )
       if not 0 < prob < 1:
              raise ValueError('prob has to be in range of 1 - 0'      )
       snake_case__							:	List[Any]   	=	(prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case__							:	List[str]   	=	float(factorial(A      )      )
       coefficient /= factorial(A      ) * factorial(trials - successes      )
       return probability * coefficient


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

 testmod()
 print("Probability of 2 successes out of 4 trails")
 print("with probability of 0.75 is:", end=" ")
 print(binomial_distribution(2, 4, 0.75))






