






import math as m



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=0.001	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=0					,     lowerCAmelCase__=100					,     lowerCAmelCase__=0.0_001					,     lowerCAmelCase__=1E-6					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		__A    =							xa  # set the initial guess
		__A    =							[a]
		__A    =							abs(f(lowerCAmelCase__	)	)
		__A    =							lambda lowerCAmelCase__	: calc_derivative(lowerCAmelCase__					,     lowerCAmelCase__					,     h=lowerCAmelCase__	)  # noqa: E731  Derivative of f(x)
		for _ in range(lowerCAmelCase__	):
				if fa(lowerCAmelCase__	) == 0:
						raise ValueError("No converging solution found"	)
				__A    =							a - f(lowerCAmelCase__	) / fa(lowerCAmelCase__	)  # Calculate the next estimate
				if logsteps:
						steps.append(lowerCAmelCase__	)
				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

					snake_case_   :	Tuple							      =lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
					snake_case_      ,	snake_case_      ,	snake_case_   :	str							      =newton_raphson(
					    f, xa=10, maxiter=1000, 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:
       UpperCamelCase__	:   Optional[int]								=     None


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)


UpperCamelCase__	:   Any								=     "▁"

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

UpperCamelCase__	:   List[Any]								=     {
    "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"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_A    ,		**_A    ,		)

      SCREAMING_SNAKE_CASE_						=    vocab_file
      SCREAMING_SNAKE_CASE_						=    False if not self.vocab_file else True


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):
      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
      SCREAMING_SNAKE_CASE_						=    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,)


