



'''simple docstring'''





import copy

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


UpperCamelCase__    :     Optional[Any]			  =   logging.get_logger(__name__)

UpperCamelCase__    :     Any			  =   {}


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     '''fsmt_decoder'''







 def __init__(      self					,    _lowerCamelCase=0					,    _lowerCamelCase=0  )    ->    Any:
    super().__init__()
    A_  :		Optional[int]						=					vocab_size
    A_  :		Union[str, Any]						=					bos_token_id


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     '''fsmt'''
 lowerCamelCase									=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







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

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

    A_  :		Optional[Any]						=					DecoderConfig(vocab_size=_lowerCamelCase					,    bos_token_id=_lowerCamelCase  )
    if "decoder" in common_kwargs:
       del common_kwargs["decoder"]

    A_  :		Dict						=					scale_embedding  # scale factor will be sqrt(d_model) if True

    # 3 Types of Dropout
    A_  :		str						=					attention_dropout
    A_  :		Tuple						=					activation_dropout
    A_  :		Optional[int]						=					dropout

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





'''simple docstring'''





def     UpperCAmelCase     (    a_      ,		a_		)   ->       set:



   """simple docstring"""






   assert isinstance(a_      ,		a_		), F"The input value of [set_a={set_a}] is not a set"
   assert isinstance(a_      ,		a_		), F"The input value of [set_b={set_b}] is not a set"

   return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
  from doctest import testmod

  testmod()
