
'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_            =    logging.get_logger(__name__)

UpperCamelCase_            =    {
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class 					a_					(_a      ):
       __lowerCAmelCase      :			Dict      = """rembert"""




       def __init__(       self				,				snake_case_=2_5_0_3_0_0				,				snake_case_=1_1_5_2				,				snake_case_=3_2				,				snake_case_=1_8				,				snake_case_=2_5_6				,				snake_case_=1_6_6_4				,				snake_case_=4_6_0_8				,				snake_case_="gelu"				,				snake_case_=0.0				,				snake_case_=0.0				,				snake_case_=0.1				,				snake_case_=5_1_2				,				snake_case_=2				,				snake_case_=0.02				,				snake_case_=1E-12				,				snake_case_=True				,				snake_case_=0				,				snake_case_=3_1_2				,				snake_case_=3_1_3				,				**snake_case_				,				):
         super().__init__(pad_token_id=snake_case_				,				bos_token_id=snake_case_				,				eos_token_id=snake_case_				,				**snake_case_					)

         _lowerCAmelCase    :					Tuple							=   vocab_size
         _lowerCAmelCase    :					Optional[int]							=   input_embedding_size
         _lowerCAmelCase    :					Optional[int]							=   output_embedding_size
         _lowerCAmelCase    :					Union[str, Any]							=   max_position_embeddings
         _lowerCAmelCase    :					Optional[int]							=   hidden_size
         _lowerCAmelCase    :					Optional[Any]							=   num_hidden_layers
         _lowerCAmelCase    :					Tuple							=   num_attention_heads
         _lowerCAmelCase    :					int							=   intermediate_size
         _lowerCAmelCase    :					int							=   hidden_act
         _lowerCAmelCase    :					str							=   hidden_dropout_prob
         _lowerCAmelCase    :					int							=   attention_probs_dropout_prob
         _lowerCAmelCase    :					int							=   classifier_dropout_prob
         _lowerCAmelCase    :					Dict							=   initializer_range
         _lowerCAmelCase    :					Optional[int]							=   type_vocab_size
         _lowerCAmelCase    :					Optional[int]							=   layer_norm_eps
         _lowerCAmelCase    :					Optional[Any]							=   use_cache
         _lowerCAmelCase    :					Dict							=   False



class 					a_					(_a      ):




       @property
       def 							__UpperCamelCase    (       self					):
         if self.task == "multiple-choice":
           _lowerCAmelCase    :					Optional[int]							=   {0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           _lowerCAmelCase    :					List[Any]							=   {0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
                 ("""token_type_ids""", dynamic_axis),
             ]					)







       @property
       def 							__UpperCamelCase    (       self					):
         return 1E-4




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    list[list[int]]					, _lowerCamelCase  :    list[list[int]]						)      ->  list[list[int]]:
  _lowerCAmelCase    :					int							=   []
  _lowerCAmelCase    :					Union[str, Any]							=   len(_lowerCamelCase						)
  for i in range(_lowerCamelCase						):
    _lowerCAmelCase    :					List[str]							=   []
    for j in range(_lowerCamelCase						):
      _lowerCAmelCase    :					Union[str, Any]							=   0
      for k in range(_lowerCamelCase						):
        _lowerCAmelCase    :					Any							=   val + matrix_a[i][k] * matrix_b[k][j]
      list_a.append(_lowerCamelCase						)
    matrix_c.append(_lowerCamelCase						)
  return matrix_c


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  list[list[int]]:
  return [[int(row == column						) for column in range(_lowerCamelCase						)] for row in range(_lowerCamelCase						)]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Tuple							=   identity(2						)
  _lowerCAmelCase    :					Union[str, Any]							=   [[1, 1], [1, 0]]
  _lowerCAmelCase    :					Optional[int]							=   n - 1
  while n > 0:
    if n % 2 == 1:
      _lowerCAmelCase    :					str							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Tuple							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Dict							=   int(n / 2						)
  return res_matrix[0][0]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Any							=   0
  _lowerCAmelCase    :					Dict							=   1
  for _ in range(2					, n + 1						):
    _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   fiba, fiba + fiba
  return fiba


def 		_UpperCAmelCase			(					)      ->  None:
  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    _lowerCAmelCase    :					Union[str, Any]							=   int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
    print(
        f'{ordinal} fibonacci number using matrix exponentiation is '
        f'{nth_fibonacci_matrix(_lowerCamelCase						)} and using bruteforce is '
        f'{nth_fibonacci_bruteforce(_lowerCamelCase						)}\n'						)
  # from timeit import timeit
  # print(timeit("nth_fibonacci_matrix(1000000)",
  #              "from main import nth_fibonacci_matrix", number=5))
  # print(timeit("nth_fibonacci_bruteforce(1000000)",
  #              "from main import nth_fibonacci_bruteforce", number=5))
  # 2.3342058970001744
  # 57.256506615000035


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()


