

import copy

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


__A	:   Dict	=					logging.get_logger(__name__)

__A	:   Union[str, Any]	=					{}


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:List[Any]	=		"fsmt_decoder"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE=0							)->							int:
													super().__init__()
													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=bos_token_id


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		"fsmt"
						_UpperCamelCase:str	=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=["en", "de"]     ,						_SCREAMING_SNAKE_CASE=4_2024     ,						_SCREAMING_SNAKE_CASE=4_2024     ,						_SCREAMING_SNAKE_CASE="relu"     ,						_SCREAMING_SNAKE_CASE=1024     ,						_SCREAMING_SNAKE_CASE=200     ,						_SCREAMING_SNAKE_CASE=1024     ,						_SCREAMING_SNAKE_CASE=4096     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=16     ,						_SCREAMING_SNAKE_CASE=0.0     ,						_SCREAMING_SNAKE_CASE=4096     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=16     ,						_SCREAMING_SNAKE_CASE=0.0     ,						_SCREAMING_SNAKE_CASE=0.0     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.0     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=5     ,						_SCREAMING_SNAKE_CASE=1.0     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=2     ,						**_SCREAMING_SNAKE_CASE     ,						)->							str:
													lowerCamelCase_								=langs
													lowerCamelCase_								=src_vocab_size
													lowerCamelCase_								=tgt_vocab_size
													lowerCamelCase_								=d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

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

													lowerCamelCase_								=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     ,						is_encoder_decoder=_SCREAMING_SNAKE_CASE     ,						tie_word_embeddings=_SCREAMING_SNAKE_CASE     ,						forced_eos_token_id=_SCREAMING_SNAKE_CASE     ,						max_length=_SCREAMING_SNAKE_CASE     ,						num_beams=_SCREAMING_SNAKE_CASE     ,						length_penalty=_SCREAMING_SNAKE_CASE     ,						early_stopping=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE     ,						)







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



from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
