

'''simple docstring'''


import copy

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


lowerCAmelCase__      				=			logging.get_logger(__name__)

lowerCAmelCase__      				=			{}



class        lowercase    (	a_			):
							_lowerCamelCase		:   Union[str, Any]= "fsmt_decoder"


							def __init__(      self     ,      _snake_case=0     ,      _snake_case=0)				->					int:
								super().__init__()
								UpperCAmelCase_		:   Dict													=       vocab_size
								UpperCAmelCase_		:   Union[str, Any]													=       bos_token_id



class        lowercase    (	a_			):
							_lowerCamelCase		:   int= "fsmt"
							_lowerCamelCase		:   Union[str, Any]= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


							def __init__(      self     ,      _snake_case=["en", "de"]     ,      _snake_case=4_2024     ,      _snake_case=4_2024     ,      _snake_case="relu"     ,      _snake_case=1024     ,      _snake_case=200     ,      _snake_case=1024     ,      _snake_case=4096     ,      _snake_case=12     ,      _snake_case=16     ,      _snake_case=0.0     ,      _snake_case=4096     ,      _snake_case=12     ,      _snake_case=16     ,      _snake_case=0.0     ,      _snake_case=0.0     ,      _snake_case=0.1     ,      _snake_case=0.0     ,      _snake_case=0.02     ,      _snake_case=2     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case=False     ,      _snake_case=5     ,      _snake_case=1.0     ,      _snake_case=False     ,      _snake_case=True     ,      _snake_case=1     ,      _snake_case=0     ,      _snake_case=2     ,      _snake_case=2     ,      **_snake_case     ,      )				->					Union[str, Any]:
								UpperCAmelCase_		:   int													=       langs
								UpperCAmelCase_		:   List[str]													=       src_vocab_size
								UpperCAmelCase_		:   Optional[int]													=       tgt_vocab_size
								UpperCAmelCase_		:   Dict													=       d_model  # encoder_embed_dim and decoder_embed_dim

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

								UpperCAmelCase_		:   int													=       DecoderConfig(vocab_size=_snake_case     ,      bos_token_id=_snake_case)
								if "decoder" in common_kwargs:
									del common_kwargs["decoder"]

								UpperCAmelCase_		:   List[Any]													=       scale_embedding  # scale factor will be sqrt(d_model) if True

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

								UpperCAmelCase_		:   Optional[int]													=       use_cache
								super().__init__(
								    pad_token_id=_snake_case     ,      bos_token_id=_snake_case     ,      eos_token_id=_snake_case     ,      decoder_start_token_id=_snake_case     ,      is_encoder_decoder=_snake_case     ,      tie_word_embeddings=_snake_case     ,      forced_eos_token_id=_snake_case     ,      max_length=_snake_case     ,      num_beams=_snake_case     ,      length_penalty=_snake_case     ,      early_stopping=_snake_case     ,      **_snake_case     ,      )





							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       copy.deepcopy(self.__dict__)
								UpperCAmelCase_		:   Optional[int]													=       self.decoder.to_dict()
								UpperCAmelCase_		:   List[Any]													=       self.__class__.model_type
								return output









'''simple docstring'''


from itertools import combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase = 5_0	)     ->     int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1	) ,repeat=2	) ,2	)
	    if is_right(*UpperCamelCase ,*UpperCamelCase	)	)


if __name__ == "__main__":
						print(F'{solution() = }')






