
'''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


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

@add_end_docstrings(_lowercase    )
class 	__lowercase				(  _lowercase    ):

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

								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      ):
								return super().__call__(A			,   **A      )

			def        UpperCAmelCase__ (self			,   **A      ):
								lowerCamelCase_					:      Tuple     =		{}
								if "candidate_labels" in kwargs:
													lowerCamelCase_					:      Optional[int]     =		kwargs['''candidate_labels''']
								if "hypothesis_template" in kwargs:
													lowerCamelCase_					:      Dict     =		kwargs['''hypothesis_template''']

								return preprocess_params, {}, {}

			def        UpperCAmelCase__ (self			,   A			,   A=None			,   A="This is a sound of {}."      ):
								if isinstance(A			,   A      ):
													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_					:      str     =		requests.get(A      ).content
													else:
																		with open(A			,   '''rb'''      ) as f:
																							lowerCamelCase_					:      Optional[Any]     =		f.read()

								if isinstance(A			,   A      ):
													lowerCamelCase_					:      Any     =		ffmpeg_read(A			,   self.feature_extractor.sampling_rate      )

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

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Dict     =		model_inputs.pop('''candidate_labels'''      )
								lowerCamelCase_					:      List[str]     =		model_inputs.pop('''text_inputs'''      )
								if isinstance(text_inputs[0]			,   A      ):
													lowerCamelCase_					:      Optional[int]     =		text_inputs[0]
								else:
													# Batching case.
													lowerCamelCase_					:      Dict     =		text_inputs[0][0]

								lowerCamelCase_					:      Dict     =		self.model(**A			,   **A      )

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






			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Tuple     =		model_outputs.pop('''candidate_labels'''      )
								lowerCamelCase_					:      int     =		model_outputs['''logits'''][0]

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

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




'''simple docstring'''


from __future__ import annotations

from math import pi, sqrt




def lowercase_	(   _lowercase			,       _lowercase				)     ->							tuple:
					'''simple docstring'''

					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''				)

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''				)

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance				)))				),
										)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


