'''simple docstring'''




import copy

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


__snake_case   :      Dict						    =  logging.get_logger(__name__)

__snake_case   :      Optional[int]						    =  {}




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'fsmt_decoder'

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




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					List[str]    =				'fsmt'
       __lowercase					:					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=0.02    ,     _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    ,     )		->     List[str]:
           A_     =   langs
           A_     =   src_vocab_size
           A_     =   tgt_vocab_size
           A_     =   d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

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

           A_     =   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 					__A       (      self )		->     Tuple:
           A_     =   copy.deepcopy(self.__dict__ )
           A_     =   self.decoder.to_dict()
           A_     =   self.__class__.model_type
           return output







'''simple docstring'''




from scipy.constants import g





def      _UpperCAmelCase    (							_UpperCamelCase    :	float,   _UpperCamelCase    :	float   )	->      float:
    if mass < 0:
        # handling of negative values of mass
        raise ValueError('''The mass of a body cannot be negative'''   )
    if height < 0:
        # handling of negative values of height
        raise ValueError('''The height above the ground cannot be negative'''   )
    return mass * g * height


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

  testmod(name='potential_energy')






