





'''simple docstring'''

from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy


class  a_					(    snake_case_	):

      '''simple docstring'''
      UpperCamelCase			   =			'''MarkupLMFeatureExtractor'''
      UpperCamelCase			   =			('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
      UpperCamelCase			   =			True


      def __call__(		self			,		A=None			,		A=None			,		A=None			,		A=None			,		A=None			,		A = True			,		A = False			,		A = None			,		A = None			,		A = 0			,		A = None			,		A = None			,		A = None			,		A = False			,		A = False			,		A = False			,		A = False			,		A = True			,		A = None			,		**A			,		)		->       BatchEncoding:
          # first, create nodes and xpaths
          if self.parse_html:
              if html_strings is None:
                  raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

              if nodes is not None or xpaths is not None or node_labels is not None:
                  raise ValueError(
                      """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

              _SCREAMING_SNAKE_CASE     =      self.feature_extractor(A	)
              _SCREAMING_SNAKE_CASE     =      features["""nodes"""]
              _SCREAMING_SNAKE_CASE     =      features["""xpaths"""]
          else:
              if html_strings is not None:
                  raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
              if nodes is None or xpaths is None:
                  raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
          if questions is not None and self.parse_html:
              if isinstance(A			,		A	):
                  _SCREAMING_SNAKE_CASE     =      [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

          _SCREAMING_SNAKE_CASE     =      self.tokenizer(
              text=questions if questions is not None else nodes			,		text_pair=nodes if questions is not None else None			,		xpaths=A			,		node_labels=A			,		add_special_tokens=A			,		padding=A			,		truncation=A			,		max_length=A			,		stride=A			,		pad_to_multiple_of=A			,		return_token_type_ids=A			,		return_attention_mask=A			,		return_overflowing_tokens=A			,		return_special_tokens_mask=A			,		return_offsets_mapping=A			,		return_length=A			,		verbose=A			,		return_tensors=A			,		**A			,		)

          return encoded_inputs


      def       snake_case_(		self			,		*A			,		**A	)		->       int:
          return self.tokenizer.batch_decode(*A			,		**A	)


      def       snake_case_(		self			,		*A			,		**A	)		->       Union[str, Any]:
          return self.tokenizer.decode(*A			,		**A	)





      @property
      def       snake_case_(		self	)		->       Optional[Any]:
          _SCREAMING_SNAKE_CASE     =      self.tokenizer.model_input_names
          return tokenizer_input_names










'''simple docstring'''

import timeit


class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A	)		->       Optional[int]:
        if isinstance(A			,		A	):  # Initializes a matrix identical to the one provided.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      len(A	)
        else:  # Initializes a square matrix of the given size and set values to zero.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      [[0 for _ in range(self.n	)] for _ in range(self.n	)]





    def __mul__(		self			,		A	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      Matrix(self.n	)
        for i in range(self.n	):
            for j in range(self.n	):
                for k in range(self.n	):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Optional[int]   )						->int:
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 0], [0, 1]]   )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Tuple   )						->Dict:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 1], [1, 0]]   )
    _SCREAMING_SNAKE_CASE     =      modular_exponentiation(__lowerCamelCase ,	n - 2   )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple   )						->str:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    _SCREAMING_SNAKE_CASE     =      fa
    _SCREAMING_SNAKE_CASE     =      fa
    n -= 2

    while n > 0:
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      fn_a + fn_a, fn_a
        n -= 1

    return fn_a





def lowerCamelCase						(					)						->str:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    _SCREAMING_SNAKE_CASE     =      """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print("""With matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import simple_fibonacci
"""
    _SCREAMING_SNAKE_CASE     =      """simple_fibonacci(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print(
        """Without matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->List[str]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
      main()



