



'''simple docstring'''

import copy

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


_A					:     int      =logging.get_logger(__name__)

_A					:     Optional[Any]      ={}



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    """fsmt_decoder"""




					def __init__(	self					:						Dict  ,	UpperCamelCase_					:						Optional[int]=0  ,	UpperCamelCase_					:						Optional[Any]=0       )	->							Optional[Any]:



						'''simple docstring'''





						super().__init__()
						_lowercase       :							Tuple  	=		vocab_size
						_lowercase       :							int  	=		bos_token_id






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



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




					def __init__(	self					:						Tuple  ,	UpperCamelCase_					:						List[str]=["en", "de"]  ,	UpperCamelCase_					:						Tuple=4_2024  ,	UpperCamelCase_					:						Optional[Any]=4_2024  ,	UpperCamelCase_					:						List[str]="relu"  ,	UpperCamelCase_					:						Tuple=1024  ,	UpperCamelCase_					:						List[Any]=200  ,	UpperCamelCase_					:						Optional[Any]=1024  ,	UpperCamelCase_					:						List[Any]=4096  ,	UpperCamelCase_					:						Optional[Any]=12  ,	UpperCamelCase_					:						Dict=16  ,	UpperCamelCase_					:						Any=0.0  ,	UpperCamelCase_					:						Dict=4096  ,	UpperCamelCase_					:						Dict=12  ,	UpperCamelCase_					:						Dict=16  ,	UpperCamelCase_					:						Tuple=0.0  ,	UpperCamelCase_					:						str=0.0  ,	UpperCamelCase_					:						List[Any]=0.1  ,	UpperCamelCase_					:						Optional[Any]=0.0  ,	UpperCamelCase_					:						Union[str, Any]=0.02  ,	UpperCamelCase_					:						Dict=2  ,	UpperCamelCase_					:						int=True  ,	UpperCamelCase_					:						Optional[int]=True  ,	UpperCamelCase_					:						Dict=False  ,	UpperCamelCase_					:						Union[str, Any]=5  ,	UpperCamelCase_					:						Union[str, Any]=1.0  ,	UpperCamelCase_					:						Tuple=False  ,	UpperCamelCase_					:						Union[str, Any]=True  ,	UpperCamelCase_					:						List[str]=1  ,	UpperCamelCase_					:						str=0  ,	UpperCamelCase_					:						Optional[Any]=2  ,	UpperCamelCase_					:						Optional[int]=2  ,	**UpperCamelCase_					:						str  ,	)	->							Any:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		langs
						_lowercase       :							List[str]  	=		src_vocab_size
						_lowercase       :							Optional[int]  	=		tgt_vocab_size
						_lowercase       :							List[str]  	=		d_model  # encoder_embed_dim and decoder_embed_dim

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

						_lowercase       :							Optional[int]  	=		DecoderConfig(vocab_size=UpperCamelCase_  ,	bos_token_id=UpperCamelCase_       )
						if "decoder" in common_kwargs:
							del common_kwargs["decoder"]

						_lowercase       :							str  	=		scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						_lowercase       :							Union[str, Any]  	=		attention_dropout
						_lowercase       :							Union[str, Any]  	=		activation_dropout
						_lowercase       :							str  	=		dropout

						_lowercase       :							List[str]  	=		use_cache
						super().__init__(
						    pad_token_id=UpperCamelCase_  ,	bos_token_id=UpperCamelCase_  ,	eos_token_id=UpperCamelCase_  ,	decoder_start_token_id=UpperCamelCase_  ,	is_encoder_decoder=UpperCamelCase_  ,	tie_word_embeddings=UpperCamelCase_  ,	forced_eos_token_id=UpperCamelCase_  ,	max_length=UpperCamelCase_  ,	num_beams=UpperCamelCase_  ,	length_penalty=UpperCamelCase_  ,	early_stopping=UpperCamelCase_  ,	**UpperCamelCase_  ,	)




					def     __UpperCAmelCase  (	self					:						List[Any]       )	->							Dict:



						'''simple docstring'''





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





'''simple docstring'''

from __future__ import annotations

import sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
