



import math as m

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: List[str]=0.0_01						):







		'''simple docstring'''





		return (f(a + h						) - f(a - h						)) / (2 * h)

def 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: Optional[Any]=0			,       _UpperCAmelCase							: Optional[Any]=1_00			,       _UpperCAmelCase							: Union[str, Any]=0.00_01			,       _UpperCAmelCase							: str=1e-6			,       _UpperCAmelCase							: str=False						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[Any]													=     xa  # set the initial guess
		__UpperCAmelCase   :					List[str]													=     [a]
		__UpperCAmelCase   :					Tuple													=     abs(f(_UpperCAmelCase						)						)
		__UpperCAmelCase   :					List[Any]													=     lambda _UpperCAmelCase						: calc_derivative(_UpperCAmelCase			,       _UpperCAmelCase			,       h=_UpperCAmelCase						)  # noqa: E731  Derivative of f(x)
		for _ in range(_UpperCAmelCase						):
				if fa(_UpperCAmelCase						) == 0:
						raise ValueError('''No converging solution found'''						)
				__UpperCAmelCase   :					List[str]													=     a - f(_UpperCAmelCase						) / fa(_UpperCAmelCase						)  # Calculate the next estimate
				if logsteps:
						steps.append(_UpperCAmelCase						)
				if error < maxerror:
						break
		else:
				raise ValueError('''Iteration limit reached, no converging solution found'''						)
		if logsteps:
				# If logstep is true, then log intermediate steps
				return a, error, steps
		return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				__A             =lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				__A      ,      __A      ,      __A             =newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("step")
				plt.ylabel("error")
				plt.show()
				print(f'''solution = {{{solution:f}}}, error = {{{error:f}}}''')










import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = None			):
					'''simple docstring'''





					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''			)

					if not os.path.isdir(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(a_			):
							copyfile(self.vocab_file	,	a_			)

					return (out_vocab_file,)





