






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class       __A		(   __SCREAMING_SNAKE_CASE			):
					UpperCAmelCase__								=				"xglm"
					UpperCAmelCase__								=				["past_key_values"]

					UpperCAmelCase__								=				{
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      List[Any]=2_5_6_0_0_8     ,		__snake_case  :      Dict=2_0_4_8     ,		__snake_case  :      int=1_0_2_4     ,		__snake_case  :      Dict=4_0_9_6     ,		__snake_case  :      Optional[Any]=2_4     ,		__snake_case  :      int=1_6     ,		__snake_case  :      List[str]="gelu"     ,		__snake_case  :      Optional[Any]=0.1     ,		__snake_case  :      int=0.1     ,		__snake_case  :      List[Any]=0.0     ,		__snake_case  :      Union[str, Any]=0.0     ,		__snake_case  :      Any=0.02     ,		__snake_case  :      Any=True     ,		__snake_case  :      Tuple=True     ,		__snake_case  :      Any=2     ,		__snake_case  :      str=1     ,		__snake_case  :      Dict=0     ,		__snake_case  :      Dict=2     ,		**__snake_case  :      List[Any]     ,		)			->							List[str]:
											__magic_name__:	Optional[int]							=       vocab_size
											__magic_name__:	int							=       max_position_embeddings
											__magic_name__:	Optional[Any]							=       d_model
											__magic_name__:	List[Any]							=       ffn_dim
											__magic_name__:	int							=       num_layers
											__magic_name__:	Any							=       attention_heads
											__magic_name__:	int							=       activation_function
											__magic_name__:	List[Any]							=       dropout
											__magic_name__:	Optional[int]							=       attention_dropout
											__magic_name__:	Optional[int]							=       activation_dropout
											__magic_name__:	Optional[int]							=       layerdrop
											__magic_name__:	Optional[int]							=       init_std
											__magic_name__:	Tuple							=       scale_embedding  # scale factor will be sqrt(d_model) if True
											__magic_name__:	Dict							=       use_cache

											super().__init__(
											    pad_token_id=_SCREAMING_SNAKE_CASE     ,		bos_token_id=_SCREAMING_SNAKE_CASE     ,		eos_token_id=_SCREAMING_SNAKE_CASE     ,		decoder_start_token_id=_SCREAMING_SNAKE_CASE     ,		**_SCREAMING_SNAKE_CASE     ,		)








"""simple docstring"""






def      _lowercase							(    __snake_case      )		->  float:
			return 10 - x * x


def      _lowercase							(    __snake_case   ,__snake_case      )		->  float:
			# Bolzano theory in order to find if there is a root between a and b
			if equation(__snake_case      ) * equation(__snake_case      ) >= 0:
						raise ValueError("Wrong space!"      )

			__lowerCAmelCase :    List[Any]				=			a
			while (b - a) >= 0.01:
						# Find middle point
						__lowerCAmelCase :    str				=			(a + b) / 2
						# Check if middle point is root
						if equation(__snake_case      ) == 0.0:
									break
						# Decide the side to repeat the steps
						if equation(__snake_case      ) * equation(__snake_case      ) < 0:
									__lowerCAmelCase :    List[Any]				=			c
						else:
									__lowerCAmelCase :    List[str]				=			c
			return c


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(bisection(-2, 5))
	print(bisection(0, 6))