



from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "EncodecFeatureExtractor"
						a__   =  ("T5Tokenizer", "T5TokenizerFast")







						def __init__(	self      :			Dict	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[Any] )      ->				Optional[int]:
									super().__init__(__lowerCamelCase	,	__lowerCamelCase )
									A							:					int              =						self.feature_extractor
									A							:					List[Any]              =						False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			Dict=True )      ->				Union[str, Any]:
									return self.tokenizer.get_decoder_prompt_ids(task=__lowerCamelCase	,	language=__lowerCamelCase	,	no_timestamps=__lowerCamelCase )







						def __call__(	self      :			Dict	,	*__lowerCamelCase      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Optional[Any]:
									# For backward compatibility
									if self._in_target_context_manager:
												return self.current_processor(*__lowerCamelCase	,	**__lowerCamelCase )

									A							:					Any              =						kwargs.pop("audio"	,	__lowerCamelCase )
									A							:					int              =						kwargs.pop("sampling_rate"	,	__lowerCamelCase )
									A							:					Any              =						kwargs.pop("text"	,	__lowerCamelCase )
									if len(__lowerCamelCase ) > 0:
												A							:					Optional[Any]              =						args[0]
												A							:					Dict              =						args[1:]

									if audio is None and text is None:
												raise ValueError("You need to specify either an `audio` or `text` input to process." )

									if text is not None:
												A							:					Union[str, Any]              =						self.tokenizer(__lowerCamelCase	,	**__lowerCamelCase )

									if audio is not None:
												A							:					Dict              =						self.feature_extractor(__lowerCamelCase	,	*__lowerCamelCase	,	sampling_rate=__lowerCamelCase	,	**__lowerCamelCase )

									if audio is None:
												return inputs

									elif text is None:
												return audio_inputs

									else:
												A							:					int              =						audio_inputs["input_values"]
												if "padding_mask" in audio_inputs:
															A							:					Dict              =						audio_inputs["padding_mask"]
												return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			int	,	**__lowerCamelCase      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					Optional[Any]              =						kwargs.pop("audio"	,	__lowerCamelCase )
									A							:					Tuple              =						kwargs.pop("padding_mask"	,	__lowerCamelCase )

									if len(__lowerCamelCase ) > 0:
												A							:					Any              =						args[0]
												A							:					Tuple              =						args[1:]

									if audio_values is not None:
												return self._decode_audio(__lowerCamelCase	,	padding_mask=__lowerCamelCase )
									else:
												return self.tokenizer.batch_decode(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	*__lowerCamelCase      :			Tuple	,	**__lowerCamelCase      :			Optional[Any] )      ->				Optional[int]:
									return self.tokenizer.decode(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Optional = None )      ->				List[np.ndarray]:
									A							:					List[str]              =						to_numpy(__lowerCamelCase )
									A       , A       , A							:					List[str]              =						audio_values.shape

									if padding_mask is None:
												return list(__lowerCamelCase )

									A							:					List[Any]              =						to_numpy(__lowerCamelCase )

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									A							:					Optional[int]              =						seq_len - padding_mask.shape[-1]
									A							:					Union[str, Any]              =						1 - self.feature_extractor.padding_value
									A							:					Any              =						np.pad(__lowerCamelCase	,	((0, 0), (0, difference))	,	"constant"	,	constant_values=__lowerCamelCase )

									A							:					List[Any]              =						audio_values.tolist()
									for i in range(__lowerCamelCase ):
												A							:					Tuple              =						np.asarray(audio_values[i] )[
												    padding_mask[i][None, :] != self.feature_extractor.padding_value
												]
												A							:					Union[str, Any]              =						sliced_audio.reshape(__lowerCamelCase	,	-1 )

									return audio_values




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						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

						A							:					int              =						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=0.005)}""",
							)

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