



import copy

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


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {}







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						"fsmt_decoder"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[str]=0							)	->      Any:
       super().__init__()
       UpperCAmelCase_     :       Optional[Any]  		=					vocab_size
       UpperCAmelCase_     :       Union[str, Any]  		=					bos_token_id







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"fsmt"
   A__      : int       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[Any]=["en", "de"]  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     List[str]="relu"  ,lowerCamelCase_:     Any=1024  ,lowerCamelCase_:     Optional[int]=200  ,lowerCamelCase_:     str=1024  ,lowerCamelCase_:     Any=4096  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     Tuple=16  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     str=4096  ,lowerCamelCase_:     Union[str, Any]=12  ,lowerCamelCase_:     Any=16  ,lowerCamelCase_:     List[str]=0.0  ,lowerCamelCase_:     str=0.0  ,lowerCamelCase_:     Optional[int]=0.1  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     str=2  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Any=False  ,lowerCamelCase_:     Any=5  ,lowerCamelCase_:     Optional[int]=1.0  ,lowerCamelCase_:     List[str]=False  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     List[str]=1  ,lowerCamelCase_:     int=0  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Any=2  ,**lowerCamelCase_:     Optional[Any]  ,)	->      int:
       UpperCAmelCase_     :       Dict  		=					langs
       UpperCAmelCase_     :       Tuple  		=					src_vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					tgt_vocab_size
       UpperCAmelCase_     :       Optional[Any]  		=					d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

       # 3 Types of Dropout
       UpperCAmelCase_     :       int  		=					attention_dropout
       UpperCAmelCase_     :       int  		=					activation_dropout
       UpperCAmelCase_     :       Any  		=					dropout

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





import math







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5   ,		int(math.sqrt(_a    ) + 1    )   ,		6    ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True







def       lowerCamelCase_  (    _a       :						int = 6008_5147_5143    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       str  		=					int(_a    )
    except (TypeError, ValueError):
        raise TypeError("""Parameter n must be int or castable to int."""    )
    if n <= 0:
        raise ValueError("""Parameter n must be greater than or equal to one."""    )
    UpperCAmelCase_     :       Optional[int]  		=					0
    if is_prime(_a    ):
        return n
    while n % 2 == 0:
        n //= 2
    if is_prime(_a    ):
        return n
    for i in range(3   ,		int(math.sqrt(_a    )    ) + 1   ,		2    ):
        if n % i == 0:
            if is_prime(n // i    ):
                UpperCAmelCase_     :       Optional[int]  		=					n // i
                break
            elif is_prime(_a    ):
                UpperCAmelCase_     :       str  		=					i
    return max_number


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