



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(SCREAMING_SNAKE_CASE				)
class 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    int				,	**snake_case_							:    Union[str, Any])->							Optional[Any]:
						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							:    Optional[Any]				,	snake_case_							:    Union[np.ndarray, bytes, str]				,	**snake_case_							:    Optional[int])->							Any:
						return super().__call__(snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    List[str]				,	**snake_case_							:    List[Any])->							Tuple:
						__lowerCAmelCase	   ={}
						if "candidate_labels" in kwargs:
											__lowerCAmelCase	   =kwargs["""candidate_labels"""]
						if "hypothesis_template" in kwargs:
											__lowerCAmelCase	   =kwargs["""hypothesis_template"""]

						return preprocess_params, {}, {}



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Any=None				,	snake_case_							:    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
																__lowerCAmelCase	   =requests.get(snake_case_).content
											else:
																with open(snake_case_				,	"""rb""") as f:
																					__lowerCAmelCase	   =f.read()

						if isinstance(snake_case_				,	snake_case_):
											__lowerCAmelCase	   =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""")

						__lowerCAmelCase	   =self.feature_extractor(
						    [audio]				,	sampling_rate=self.feature_extractor.sampling_rate				,	return_tensors="""pt""")
						__lowerCAmelCase	   =candidate_labels
						__lowerCAmelCase	   =[hypothesis_template.format(snake_case_) for x in candidate_labels]
						__lowerCAmelCase	   =self.tokenizer(snake_case_				,	return_tensors=self.framework				,	padding=snake_case_)
						__lowerCAmelCase	   =[text_inputs]
						return inputs



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    Union[str, Any])->							List[str]:
						__lowerCAmelCase	   =model_inputs.pop("""candidate_labels""")
						__lowerCAmelCase	   =model_inputs.pop("""text_inputs""")
						if isinstance(text_inputs[0]				,	snake_case_):
											__lowerCAmelCase	   =text_inputs[0]
						else:
											# Batching case.
											__lowerCAmelCase	   =text_inputs[0][0]

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

						__lowerCAmelCase	   ={
						    """candidate_labels""": candidate_labels,
						    """logits""": outputs.logits_per_audio,
						}
						return model_outputs



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    int)->							int:
						__lowerCAmelCase	   =model_outputs.pop("""candidate_labels""")
						__lowerCAmelCase	   =model_outputs["""logits"""][0]

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

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









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


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

				testmod()




