

import copy

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


A__        =			logging.get_logger(__name__)

A__        =			{}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: str										=      """fsmt_decoder"""



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







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: str										=      """fsmt"""
  SCREAMING_SNAKE_CASE__				: Optional[Any]										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



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

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

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

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

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

      SCREAMING_SNAKE_CASE__					     =						use_cache
      super().__init__(
          pad_token_id=A_     ,  bos_token_id=A_     ,  eos_token_id=A_     ,  decoder_start_token_id=A_     ,  is_encoder_decoder=A_     ,  tie_word_embeddings=A_     ,  forced_eos_token_id=A_     ,  max_length=A_     ,  num_beams=A_     ,  length_penalty=A_     ,  early_stopping=A_     ,  **A_     ,  )







  def      _UpperCamelCase    (	self    :				Optional[int]	)					-> str:
      SCREAMING_SNAKE_CASE__					     =						copy.deepcopy(self.__dict__	)
      SCREAMING_SNAKE_CASE__					     =						self.decoder.to_dict()
      SCREAMING_SNAKE_CASE__					     =						self.__class__.model_type
      return output





from collections import namedtuple

A__        =			namedtuple("from_to", "from_ to")

A__        =			{
    "atm": from_to(1, 1),
    "pascal": from_to(0.0_000_098, 10_1325),
    "bar": from_to(0.986_923, 1.01_325),
    "kilopascal": from_to(0.00_986_923, 101.325),
    "megapascal": from_to(9.86_923, 0.101_325),
    "psi": from_to(0.068_046, 14.6_959),
    "inHg": from_to(0.0_334_211, 29.9_213),
    "torr": from_to(0.00_131_579, 760),
}





def 							__a       (					__A ,       __A ,       __A	):
    if from_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
            + ", ".join(__A	)	)
    if to_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
            + ", ".join(__A	)	)
    return (
        value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


