



'''simple docstring'''




from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = False    ):
			'''simple docstring'''





			if radian_mode:
						return [magnitude * cos(lowerCamelCase__    ), magnitude * sin(lowerCamelCase__    )]
			return [magnitude * cos(radians(lowerCamelCase__    )    ), magnitude * sin(radians(lowerCamelCase__    )    )]



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = 10**-1    ):
			'''simple docstring'''





			A_	:						NDArray[floataa]	=     cross(lowerCamelCase__						,      lowerCamelCase__    )
			A_	:						float	=     sum(lowerCamelCase__    )
			return abs(lowerCamelCase__    ) < eps


if __name__ == "__main__":
						# Test to check if it works
						lowerCamelCase							:List[str]				    =					array(
						    [
						        polar_force(718.4, 1_8_0 - 3_0),
						        polar_force(879.54, 4_5),
						        polar_force(1_0_0, -9_0),
						    ]
						)

						lowerCamelCase							:NDArray[floataa]				    =					array([[0, 0], [0, 0], [0, 0]])

						assert in_static_equilibrium(forces, location)

						# Problem 1 in image_data/2D_problems.jpg
						lowerCamelCase							:Union[str, Any]				    =					array(
						    [
						        polar_force(3_0 * 9.81, 1_5),
						        polar_force(2_1_5, 1_8_0 - 4_5),
						        polar_force(2_6_4, 9_0 - 3_0),
						    ]
						)

						lowerCamelCase							:List[str]				    =					array([[0, 0], [0, 0], [0, 0]])

						assert in_static_equilibrium(forces, location)

						# Problem in image_data/2D_problems_1.jpg
						lowerCamelCase							:Tuple				    =					array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

						lowerCamelCase							:Union[str, Any]				    =					array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

						assert in_static_equilibrium(forces, location)

						import doctest

						doctest.testmod()




'''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							:Union[str, Any]				    =					logging.get_logger(__name__)







@add_end_docstrings(__UpperCAmelCase )
class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      **lowercase							):
				super().__init__(**lowercase							)

				if self.framework != "pt":
							raise ValueError(F'The {self.__class__} is only available in PyTorch.'							)
				# No specific FOR_XXX available yet


	def __call__(self       ,      lowercase       ,      **lowercase							):
				return super().__call__(lowercase       ,      **lowercase							)


	def     _a	(self       ,      **lowercase							):
				A_	:						Tuple	=     {}
				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     _a	(self       ,      lowercase       ,      lowercase=None       ,      lowercase="This is a sound of {}."							):
				if isinstance(lowercase       ,      lowercase							):
							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_	:						Dict	=     requests.get(lowercase							).content
							else:
										with open(lowercase       ,      """rb"""							) as f:
													A_	:						List[str]	=     f.read()

				if isinstance(lowercase       ,      lowercase							):
							A_	:						List[Any]	=     ffmpeg_read(lowercase       ,      self.feature_extractor.sampling_rate							)

				if not isinstance(lowercase       ,      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_	:						int	=     self.feature_extractor(
				    [audio]       ,      sampling_rate=self.feature_extractor.sampling_rate       ,      return_tensors="""pt"""							)
				A_	:						List[Any]	=     candidate_labels
				A_	:						str	=     [hypothesis_template.format(lowercase							) for x in candidate_labels]
				A_	:						Optional[Any]	=     self.tokenizer(lowercase       ,      return_tensors=self.framework       ,      padding=lowercase							)
				A_	:						Optional[Any]	=     [text_inputs]
				return inputs


	def     _a	(self       ,      lowercase							):
				A_	:						Union[str, Any]	=     model_inputs.pop("""candidate_labels"""							)
				A_	:						List[Any]	=     model_inputs.pop("""text_inputs"""							)
				if isinstance(text_inputs[0]       ,      lowercase							):
							A_	:						Union[str, Any]	=     text_inputs[0]
				else:
							# Batching case.
							A_	:						Optional[int]	=     text_inputs[0][0]

				A_	:						str	=     self.model(**lowercase       ,      **lowercase							)

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


	def     _a	(self       ,      lowercase							):
				A_	:						Union[str, Any]	=     model_outputs.pop("""candidate_labels"""							)
				A_	:						List[Any]	=     model_outputs["""logits"""][0]

				if self.framework == "pt":
							A_	:						Optional[Any]	=     logits.softmax(dim=0							)
							A_	:						str	=     probs.tolist()
				else:
							raise ValueError("""`tf` framework not supported."""							)

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