code: 
  
  """simple docstring"""
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __A  :					Any												=	logging.get_logger(__name__)
  
  __A  :					List[str]												=	{
      'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
      # See all RemBERT models at https://huggingface.co/models?filter=rembert
  }
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					"rembert"
  
  
  		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	str=2_5_0_3_0_0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_1_5_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_8  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=2_5_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=1_6_6_4  ,  __SCREAMING_SNAKE_CASE				:	str=4_6_0_8  ,  __SCREAMING_SNAKE_CASE				:	List[Any]="gelu"  ,  __SCREAMING_SNAKE_CASE				:	Any=0.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=0.0  ,  __SCREAMING_SNAKE_CASE				:	int=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Dict=2  ,  __SCREAMING_SNAKE_CASE				:	Any=0.0_2  ,  __SCREAMING_SNAKE_CASE				:	str=1E-12  ,  __SCREAMING_SNAKE_CASE				:	Dict=True  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=3_1_2  ,  __SCREAMING_SNAKE_CASE				:	List[str]=3_1_3  ,  **__SCREAMING_SNAKE_CASE				:	Any  ,  ):
  							super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,  bos_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  							A    =      vocab_size
  							A    =      input_embedding_size
  							A    =      output_embedding_size
  							A    =      max_position_embeddings
  							A    =      hidden_size
  							A    =      num_hidden_layers
  							A    =      num_attention_heads
  							A    =      intermediate_size
  							A    =      hidden_act
  							A    =      hidden_dropout_prob
  							A    =      attention_probs_dropout_prob
  							A    =      classifier_dropout_prob
  							A    =      initializer_range
  							A    =      type_vocab_size
  							A    =      layer_norm_eps
  							A    =      use_cache
  							A    =      False
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							if self.task == "multiple-choice":
  												A    =      {0: "batch", 1: "choice", 2: "sequence"}
  							else:
  												A    =      {0: "batch", 1: "sequence"}
  							return OrderedDict(
  							    [
  							        ("input_ids", dynamic_axis),
  							        ("attention_mask", dynamic_axis),
  							        ("token_type_ids", dynamic_axis),
  							    ])
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							return 1E-4
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import functools
  from math import sqrt
  from time import time
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							*lowercase__    ,							**lowercase__   ):
  					"""simple docstring"""
  					A    =      time()
  					A    =      func(*lowercase__    ,							**lowercase__   )
  					A    =      time()
  					if int(end - start   ) > 0:
  										print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""   )
  					else:
  										print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""   )
  					return output
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					if n == 0:
  										return [0]
  					A    =      [0, 1]
  					for _ in range(n - 1   ):
  										fib.append(fib[-1] + fib[-2]   )
  					return fib
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  
  					def fib_recursive_term(lowercase__   ) -> int:
  										if i < 0:
  															raise Exception("n is negative"   )
  										if i < 2:
  															return i
  										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )
  
  					if n < 0:
  										raise Exception("n is negative"   )
  					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  
  					@functools.cache
  					def fib_recursive_term(lowercase__   ) -> int:
  										if i < 0:
  															raise Exception("n is negative"   )
  										if i < 2:
  															return i
  										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )
  
  					if n < 0:
  										raise Exception("n is negative"   )
  					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					# Cache must be outside recursuive function
  					# other it will reset every time it calls itself.
  					A    =      {0: 0, 1: 1, 2: 1}  # Prefilled cache
  
  					def rec_fn_memoized(lowercase__   ) -> int:
  										if num in cache:
  															return cache[num]
  
  										A    =      rec_fn_memoized(num - 1   ) + rec_fn_memoized(num - 2   )
  										A    =      value
  										return value
  
  					return [rec_fn_memoized(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					if n >= 1_475:
  										raise Exception("n is too large"   )
  					A    =      sqrt(5   )
  					A    =      (1 + sqrt_a) / 2
  					return [round(phi**i / sqrt_a   ) for i in range(n + 1   )]
  
  
  if __name__ == "__main__":
  	__A  :					Optional[Any]												=	30
  	time_func(fib_iterative, num)
  	time_func(fib_recursive, num)  # Around 3s runtime
  	time_func(fib_recursive_cached, num)  # Around 0ms runtime
  	time_func(fib_memoization, num)
  	time_func(fib_binet, num)
  
  
  
  
  
  