



"""simple docstring"""






from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   float | Decimal							,		__lowerCamelCase    :   float = 10**-10     ):
			"""simple docstring"""
			lowerCamelCase__		:		str		     =a
			while True:
						lowerCamelCase__		:		Optional[int]		     =Decimal(__lowerCamelCase     ) - (
						    Decimal(eval(__lowerCamelCase     )     ) / Decimal(eval(str(diff(__lowerCamelCase     )     )     )     )  # noqa: S307
						)
						# This number dictates the accuracy of the answer
						if abs(eval(__lowerCamelCase     )     ) < precision:  # noqa: S307
									return float(__lowerCamelCase     )


# 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
		print(f'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
		# Find Square Root of 5
		print(f'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
		# Exponential Roots
		print(f'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')









"""simple docstring"""






from ...processing_utils import ProcessorMixin

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'SpeechT5FeatureExtractor'
					_a             =							'SpeechT5Tokenizer'



					def __init__(	self							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str					)-> Any:
								super().__init__(lowerCamelCase,  lowerCamelCase					)



					def __call__(	self							:  Tuple,  *lowerCamelCase							:  List[str],  **lowerCamelCase							:  Optional[int]					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''audio''',  lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =kwargs.pop('''text''',  lowerCamelCase					)
								lowerCamelCase__		:		int		     =kwargs.pop('''text_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''audio_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Any		     =kwargs.pop('''sampling_rate''',  lowerCamelCase					)

								if audio is not None and text is not None:
											raise ValueError(
											    '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''					)
								if audio_target is not None and text_target is not None:
											raise ValueError(
											    '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''					)
								if audio is None and audio_target is None and text is None and text_target is None:
											raise ValueError(
											    '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''					)

								if audio is not None:
											lowerCamelCase__		:		Union[str, Any]		     =self.feature_extractor(lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
								elif text is not None:
											lowerCamelCase__		:		List[Any]		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if audio_target is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor(audio_target=lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								elif text_target is not None:
											lowerCamelCase__		:		Dict		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		int		     =targets['''input_ids''']
								else:
											lowerCamelCase__		:		List[str]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Dict		     =labels

											lowerCamelCase__		:		Any		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Dict		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  int,  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Optional[int]					)-> Optional[Any]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''input_values''',  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =kwargs.pop('''input_ids''',  lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =kwargs.pop('''labels''',  lowerCamelCase					)

								if input_values is not None and input_ids is not None:
											raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''					)
								if input_values is None and input_ids is None and labels is None:
											raise ValueError(
											    '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''					)

								if input_values is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
								elif input_ids is not None:
											lowerCamelCase__		:		Tuple		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if labels is not None:
											if "input_ids" in labels or (isinstance(lowerCamelCase,  lowerCamelCase					) and "input_ids" in labels[0]):
														lowerCamelCase__		:		str		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =targets['''input_ids''']
											else:
														lowerCamelCase__		:		Any		     =self.feature_extractor.feature_size
														lowerCamelCase__		:		Optional[Any]		     =self.feature_extractor.num_mel_bins
														lowerCamelCase__		:		Optional[int]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =feature_size_hack
														lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								else:
											lowerCamelCase__		:		Optional[Any]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Tuple		     =labels

											lowerCamelCase__		:		Optional[int]		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Optional[Any]		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  Union[str, Any],  **lowerCamelCase							:  List[Any]					)-> List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase					)




					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Tuple					)-> int:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase					)




