





'''simple docstring'''






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = "x"    ,	_lowerCamelCase = 10**-10    ,	_lowerCamelCase = 1    ,	) ->				complex:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     symbols(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     lambdify(_lowerCamelCase    ,	_lowerCamelCase )
			__snake_case					:      List[Any]							      =     lambdify(_lowerCamelCase    ,	diff(_lowerCamelCase    ,	_lowerCamelCase ) )

			__snake_case					:      List[str]							      =     starting_point

			while True:
						if diff_function(_lowerCamelCase ) != 0:
									__snake_case					:      Optional[int]							      =     prev_guess - multiplicity * func(_lowerCamelCase ) / diff_function(
									    _lowerCamelCase )
						else:
									raise ZeroDivisionError("""Could not find root""" ) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess ) < precision:
									return next_guess

						__snake_case					:      str							      =     next_guess


# Let's Execute
if __name__ == "__main__":
	# Find root of trigonometric function
	# Find value of pi
	print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

	# Find root of polynomial
	# Find fourth Root of 5
	print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

	# Find value of e
	print(
	    "The root of log(y) - 1 = 0 is ",
	    f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
	)

	# Exponential Roots
	print(
	    "The root of exp(x) - 1 = 0 is",
	    f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}""",
	)

	# Find root of cos(x)
	print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")





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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  **lowerCAmelCase   )    ->					Tuple:
        '''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				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






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




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			={}
        if "candidate_labels" in kwargs:
              _lowercase      			=kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
              _lowercase      			=kwargs['hypothesis_template']

        return preprocess_params, {}, {}




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase="This is a sound of {}."   )    ->					Tuple:
        '''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
                    _lowercase      			=requests.get(lowerCAmelCase   ).content
              else:
                    with open(lowerCAmelCase				,  'rb'   ) as f:
                          _lowercase      			=f.read()

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=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'   )

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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=model_inputs.pop('candidate_labels'   )
        _lowercase      			=model_inputs.pop('text_inputs'   )
        if isinstance(text_inputs[0]				,  lowerCAmelCase   ):
              _lowercase      			=text_inputs[0]
        else:
              # Batching case.
              _lowercase      			=text_inputs[0][0]

        _lowercase      			=self.model(**lowerCAmelCase				,  **lowerCAmelCase   )

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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=model_outputs.pop('candidate_labels'   )
        _lowercase      			=model_outputs['logits'][0]

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

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


