'''simple docstring'''




from collections.abc import Callable

__snake_case   :      Optional[int]						    =  Callable[[float], float]  # type alias for a real -> real function





def      _UpperCAmelCase    (							_UpperCamelCase    :	RealFunc,   _UpperCamelCase    :	RealFunc,   _UpperCamelCase    :	int,   )	->      float:
    A_     =   float(_UpperCamelCase   )
    while True:
        try:
            A_     =   prev_guess - function(_UpperCamelCase   ) / derivative(_UpperCamelCase   )
        except ZeroDivisionError:
            raise ZeroDivisionError('''Could not find root'''   ) from None
        if abs(prev_guess - next_guess   ) < 10**-5:
            return next_guess
        A_     =   next_guess





def      _UpperCAmelCase    (							_UpperCamelCase    :	float   )	->      float:
    return (x**3) - (2 * x) - 5





def      _UpperCAmelCase    (							_UpperCamelCase    :	float   )	->      float:
    return 3 * (x**2) - 2


if __name__ == "__main__":
  print(newton(f, fa, 3))














import copy

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


__A          = logging.get_logger(__name__)

__A          = {}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt_decoder"

							def __init__(     self:      List[Any]						,   __A:      Dict=0						,   __A:      str=0      )			-> Optional[int]:
												super().__init__()
												_A									=       vocab_size
												_A									=       bos_token_id





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt"
							A_       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(     self:      Optional[Any]						,   __A:      Optional[Any]=["en", "de"]						,   __A:      List[str]=4_20_24						,   __A:      Any=4_20_24						,   __A:      Optional[int]="relu"						,   __A:      List[str]=10_24						,   __A:      str=2_00						,   __A:      Tuple=10_24						,   __A:      List[str]=40_96						,   __A:      List[Any]=12						,   __A:      Union[str, Any]=16						,   __A:      List[str]=0.0						,   __A:      int=40_96						,   __A:      Union[str, Any]=12						,   __A:      List[str]=16						,   __A:      Union[str, Any]=0.0						,   __A:      int=0.0						,   __A:      str=0.1						,   __A:      int=0.0						,   __A:      Union[str, Any]=0.02						,   __A:      Tuple=2						,   __A:      int=True						,   __A:      Dict=True						,   __A:      int=False						,   __A:      Any=5						,   __A:      Tuple=1.0						,   __A:      Optional[int]=False						,   __A:      List[Any]=True						,   __A:      Tuple=1						,   __A:      List[Any]=0						,   __A:      Any=2						,   __A:      Optional[Any]=2						,   **__A:      Optional[Any]						,   )			-> Tuple:
												_A									=       langs
												_A									=       src_vocab_size
												_A									=       tgt_vocab_size
												_A									=       d_model  # encoder_embed_dim and decoder_embed_dim

												_A									=       encoder_ffn_dim
												_A									=       _A									=       encoder_layers
												_A									=       encoder_attention_heads
												_A									=       encoder_layerdrop
												_A									=       decoder_layerdrop
												_A									=       decoder_ffn_dim
												_A									=       decoder_layers
												_A									=       decoder_attention_heads
												_A									=       max_position_embeddings
												_A									=       init_std  # Normal(0, this parameter)
												_A									=       activation_function

												_A									=       DecoderConfig(vocab_size=__A						,   bos_token_id=__A      )
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												_A									=       scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												_A									=       attention_dropout
												_A									=       activation_dropout
												_A									=       dropout

												_A									=       use_cache
												super().__init__(
												    pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   decoder_start_token_id=__A						,   is_encoder_decoder=__A						,   tie_word_embeddings=__A						,   forced_eos_token_id=__A						,   max_length=__A						,   num_beams=__A						,   length_penalty=__A						,   early_stopping=__A						,   **__A						,   )



							def 					__A       (     self:      Optional[int]      )			-> List[Any]:
												_A									=       copy.deepcopy(self.__dict__      )
												_A									=       self.decoder.to_dict()
												_A									=       self.__class__.model_type
												return output



