
"""simple docstring"""
import copy

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


lowercase		:    Any			=       logging.get_logger(__name__)

lowercase		:    Tuple			=       {}




class  _snake_case	(    _a							):
							SCREAMING_SNAKE_CASE		  =    """fsmt_decoder"""






							def __init__(       self    :      Optional[Any]    ,			snake_case__    :      Optional[Any]=0    ,			snake_case__    :      Optional[Any]=0     ):






												"""simple docstring"""



												super().__init__()
												__a:    int		     =       vocab_size
												__a:    Union[str, Any]		     =       bos_token_id






class  _snake_case	(    _a							):
							SCREAMING_SNAKE_CASE		  =    """fsmt"""
							SCREAMING_SNAKE_CASE		  =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(       self    :      Any    ,			snake_case__    :      Optional[Any]=["en", "de"]    ,			snake_case__    :      Dict=4_2_0_2_4    ,			snake_case__    :      List[str]=4_2_0_2_4    ,			snake_case__    :      Union[str, Any]="relu"    ,			snake_case__    :      Optional[Any]=1_0_2_4    ,			snake_case__    :      Dict=2_0_0    ,			snake_case__    :      Union[str, Any]=1_0_2_4    ,			snake_case__    :      Any=4_0_9_6    ,			snake_case__    :      str=1_2    ,			snake_case__    :      Union[str, Any]=1_6    ,			snake_case__    :      int=0.0    ,			snake_case__    :      int=4_0_9_6    ,			snake_case__    :      Optional[Any]=1_2    ,			snake_case__    :      Optional[int]=1_6    ,			snake_case__    :      Tuple=0.0    ,			snake_case__    :      List[Any]=0.0    ,			snake_case__    :      Any=0.1    ,			snake_case__    :      str=0.0    ,			snake_case__    :      Tuple=0.02    ,			snake_case__    :      Dict=2    ,			snake_case__    :      List[Any]=True    ,			snake_case__    :      Optional[int]=True    ,			snake_case__    :      Optional[Any]=False    ,			snake_case__    :      Optional[int]=5    ,			snake_case__    :      Union[str, Any]=1.0    ,			snake_case__    :      Tuple=False    ,			snake_case__    :      Optional[Any]=True    ,			snake_case__    :      Any=1    ,			snake_case__    :      Tuple=0    ,			snake_case__    :      Tuple=2    ,			snake_case__    :      str=2    ,			**snake_case__    :      Any    ,			):






												"""simple docstring"""



												__a:    List[Any]		     =       langs
												__a:    int		     =       src_vocab_size
												__a:    Dict		     =       tgt_vocab_size
												__a:    Union[str, Any]		     =       d_model  # encoder_embed_dim and decoder_embed_dim

												__a:    int		     =       encoder_ffn_dim
												__a:    str		     =       encoder_layers
												__a:    List[Any]		     =       encoder_attention_heads
												__a:    Tuple		     =       encoder_layerdrop
												__a:    Dict		     =       decoder_layerdrop
												__a:    List[str]		     =       decoder_ffn_dim
												__a:    List[Any]		     =       decoder_layers
												__a:    int		     =       decoder_attention_heads
												__a:    List[str]		     =       max_position_embeddings
												__a:    List[Any]		     =       init_std  # Normal(0, this parameter)
												__a:    Tuple		     =       activation_function

												__a:    str		     =       DecoderConfig(vocab_size=__lowerCamelCase    ,			bos_token_id=__lowerCamelCase     )
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												__a:    Optional[int]		     =       scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												__a:    Optional[Any]		     =       attention_dropout
												__a:    Tuple		     =       activation_dropout
												__a:    str		     =       dropout

												__a:    str		     =       use_cache
												super().__init__(
												    pad_token_id=__lowerCamelCase    ,			bos_token_id=__lowerCamelCase    ,			eos_token_id=__lowerCamelCase    ,			decoder_start_token_id=__lowerCamelCase    ,			is_encoder_decoder=__lowerCamelCase    ,			tie_word_embeddings=__lowerCamelCase    ,			forced_eos_token_id=__lowerCamelCase    ,			max_length=__lowerCamelCase    ,			num_beams=__lowerCamelCase    ,			length_penalty=__lowerCamelCase    ,			early_stopping=__lowerCamelCase    ,			**__lowerCamelCase    ,			)






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    List[str]		     =       copy.deepcopy(self.__dict__     )
												__a:    int		     =       self.decoder.to_dict()
												__a:    Optional[Any]		     =       self.__class__.model_type
												return output








from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase_					:		Union[str, Any]           = 6.6_743E-11  # unit of G : m^3 * kg^-1 * s^-2

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float   )						->     dict[str, float]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      mass_a * mass_a

	if (force, mass_a, mass_a, distance).count(0   ) != 1:
		raise ValueError("""One and only one argument must be 0"""   )
	if force < 0:
		raise ValueError("""Gravitational force can not be negative"""   )
	if distance < 0:
		raise ValueError("""Distance can not be negative"""   )
	if mass_a < 0 or mass_a < 0:
		raise ValueError("""Mass can not be negative"""   )
	if force == 0:
		UpperCamelCase	:Optional[int]		=      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
		return {"force": force}
	elif mass_a == 0:
		UpperCamelCase	:Any		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_1": mass_a}
	elif mass_a == 0:
		UpperCamelCase	:Optional[int]		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_2": mass_a}
	elif distance == 0:
		UpperCamelCase	:str		=      (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
		return {"distance": distance}
	raise ValueError("""One and only one argument must be 0"""   )


# Run doctest
if __name__ == "__main__":
	import doctest

	doctest.testmod()
