


"""simple docstring"""







from collections import namedtuple

lowerCamelCase_     :     List[str]    		=						namedtuple("""from_to""", """from_ to""")

lowerCamelCase_     :     List[Any]    		=						{
    """atm""": from_to(1, 1),
    """pascal""": from_to(0.000_0098, 1_0_1_3_2_5),
    """bar""": from_to(0.98_6923, 1.0_1325),
    """kilopascal""": from_to(0.0098_6923, 101.325),
    """megapascal""": from_to(9.8_6923, 0.10_1325),
    """psi""": from_to(0.06_8046, 14.6959),
    """inHg""": from_to(0.033_4211, 29.9213),
    """torr""": from_to(0.0013_1579, 7_6_0),
}
def _A		(		lowercase ,					lowercase ,					lowercase	):







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


if __name__ == "__main__":
       import doctest

       doctest.testmod()



"""simple docstring"""







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


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Optional[Any]    		=						{
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"vilt"


 def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
     super().__init__(tie_word_embeddings=__A				,					**__A		)

     a					=vocab_size
     a					=type_vocab_size
     a					=modality_type_vocab_size
     a					=max_position_embeddings

     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=initializer_range
     a					=layer_norm_eps

     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=qkv_bias
     a					=max_image_length
     a					=num_images