






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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase = "x"					,			_lowercase = 10**-10					,			_lowercase = 1					,			):

					'''simple docstring'''







					_A								= symbols(_lowercase )
					_A								= lambdify(_lowercase					,			_lowercase )
					_A								= lambdify(_lowercase					,			diff(_lowercase					,			_lowercase ) )

					_A								= starting_point

					while True:
										if diff_function(_lowercase ) != 0:
															_A								= prev_guess - multiplicity * func(_lowercase ) / diff_function(
															    _lowercase )
										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								= 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)}')











import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				str:
												_A								= question_encoder
												_A								= generator
												_A								= self.question_encoder

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Dict:
												if os.path.isfile(__A   ):
																	raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
												os.makedirs(__A		,     exist_ok=__A   )
												_A								= os.path.join(__A		,     '''question_encoder_tokenizer'''   )
												_A								= os.path.join(__A		,     '''generator_tokenizer'''   )
												self.question_encoder.save_pretrained(__A   )
												self.generator.save_pretrained(__A   )

							@classmethod
							def       __A (  cls:   Optional[Any]		,     __A:   List[str]		,     **__A:   int   )    ->				Any:
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												_A								= kwargs.pop('''config'''		,     __A   )

												if config is None:
																	_A								= RagConfig.from_pretrained(__A   )

												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.question_encoder		,     subfolder='''question_encoder_tokenizer'''   )
												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.generator		,     subfolder='''generator_tokenizer'''   )
												return cls(question_encoder=__A		,     generator=__A   )

							def __call__(  self:   int		,     *__A:   Optional[int]		,     **__A:   List[str]   )    ->				int:
												return self.current_tokenizer(*__A		,     **__A   )

							def       __A (  self:   Dict		,     *__A:   List[str]		,     **__A:   List[str]   )    ->				Dict:
												return self.generator.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Union[str, Any]		,     *__A:   Tuple		,     **__A:   List[str]   )    ->				Tuple:
												return self.generator.decode(*__A		,     **__A   )

							def       __A (  self:   Dict   )    ->				List[str]:
												_A								= self.question_encoder

							def       __A (  self:   Union[str, Any]   )    ->				int:
												_A								= self.generator



							def       __A (  self:   Dict		,     __A:   List[str]		,     __A:   Optional[List[str]] = None		,     __A:   Optional[int] = None		,     __A:   Optional[int] = None		,     __A:   str = "longest"		,     __A:   str = None		,     __A:   bool = True		,     **__A:   Tuple		,     )    ->				BatchEncoding:
												warnings.warn(
												    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
												    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
												    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
												    '''details'''		,     __A		,     )
												if max_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    __A		,     add_special_tokens=__A		,     return_tensors=__A		,     max_length=__A		,     padding=__A		,     truncation=__A		,     **__A		,     )
												if tgt_texts is None:
																	return model_inputs
												# Process tgt_texts
												if max_target_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    text_target=__A		,     add_special_tokens=__A		,     return_tensors=__A		,     padding=__A		,     max_length=__A		,     truncation=__A		,     **__A		,     )
												_A								= labels['''input_ids''']
												return model_inputs



