


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


snake_case__				:	Tuple					       =					logging.get_logger(__name__)

snake_case__				:	Optional[Any]					       =					{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"trocr"
       _a   							=							["past_key_values"]
       _a   							=							{
           "num_attention_heads": "decoder_attention_heads",
           "hidden_size": "d_model",
           "num_hidden_layers": "decoder_layers",
       }



       def __init__(						self				:				List[Any]		,				__a				:				Dict=50_265		,				__a				:				Tuple=1_024		,				__a				:				Tuple=12		,				__a				:				Tuple=16		,				__a				:				Optional[int]=4_096		,				__a				:				Any="gelu"		,				__a				:				Tuple=512		,				__a				:				str=0.1		,				__a				:				Tuple=0.0		,				__a				:				int=0.0		,				__a				:				Optional[Any]=2		,				__a				:				Optional[Any]=0.02		,				__a				:				str=0.0		,				__a				:				Tuple=True		,				__a				:				int=False		,				__a				:				Dict=True		,				__a				:				int=True		,				__a				:				List[Any]=1		,				__a				:				Optional[Any]=0		,				__a				:				Dict=2		,				**__a				:				List[Any]		,				)					->Dict:
          lowerCamelCase_						:   Optional[Any]		= vocab_size
          lowerCamelCase_						:   List[Any]		= d_model
          lowerCamelCase_						:   Union[str, Any]		= decoder_layers
          lowerCamelCase_						:   Union[str, Any]		= decoder_attention_heads
          lowerCamelCase_						:   Union[str, Any]		= decoder_ffn_dim
          lowerCamelCase_						:   List[Any]		= activation_function
          lowerCamelCase_						:   Union[str, Any]		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= dropout
          lowerCamelCase_						:   Tuple		= attention_dropout
          lowerCamelCase_						:   Dict		= activation_dropout
          lowerCamelCase_						:   Tuple		= init_std
          lowerCamelCase_						:   List[Any]		= decoder_layerdrop
          lowerCamelCase_						:   Union[str, Any]		= use_cache
          lowerCamelCase_						:   List[Any]		= scale_embedding
          lowerCamelCase_						:   Any		= use_learned_position_embeddings
          lowerCamelCase_						:   List[Any]		= layernorm_embedding

          super().__init__(
              pad_token_id=__a		,				bos_token_id=__a		,				eos_token_id=__a		,				decoder_start_token_id=__a		,				**__a		,				)









def    __lowerCamelCase   (      A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						)     ->    float:
   lowerCamelCase_						:   List[str]		= [redshift, radiation_density, matter_density, dark_energy]
   if any(p < 0 for p in parameters					):
      raise ValueError("""All input parameters must be positive"""					)

   if any(p > 1 for p in parameters[1:4]					):
      raise ValueError("""Relative densities cannot be greater than one"""					)
   else:
      lowerCamelCase_						:   Union[str, Any]		= 1 - (matter_density + radiation_density + dark_energy)

      lowerCamelCase_						:   int		= (
          radiation_density * (redshift + 1) ** 4
          + matter_density * (redshift + 1) ** 3
          + curvature * (redshift + 1) ** 2
          + dark_energy
      )

      lowerCamelCase_						:   Dict		= hubble_constant * e_a ** (1 / 2)
      return hubble


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo LCDM approximation
      snake_case__				:	str					       =					0.3

      print(
          hubble_parameter(
              hubble_constant=6_8.3,
              radiation_density=1e-4,
              matter_density=matter_density,
              dark_energy=1 - matter_density,
              redshift=0,
          )
      )





