
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.get_logger(__name__)






class 					__lowercase		(					A       ):
					__magic_name__						:							Any     	=	'''speech-encoder-decoder'''
					__magic_name__						:							Optional[int]     	=	True


					def __init__(	self       ,       **a__						)		->							Optional[int]:





									'''simple docstring'''
									super().__init__(**a_						)
									if "encoder" not in kwargs or "decoder" not in kwargs:
													raise ValueError(
													    F"A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"
													    F" `decoder` sub-configurations are passed, but only {kwargs}"						)

									A_       	=  kwargs.pop('''encoder'''						)
									A_       	=  encoder_config.pop('''model_type'''						)
									A_       	=  kwargs.pop('''decoder'''						)
									A_       	=  decoder_config.pop('''model_type'''						)

									A_       	=  AutoConfig.for_model(a_       ,       **a_						)
									A_       	=  AutoConfig.for_model(a_       ,       **a_						)
									A_       	=  True


					@classmethod
					def        lowerCAmelCase_    (	cls       ,       a__       ,       a__       ,       **a__						)		->							PretrainedConfig:





									'''simple docstring'''
									logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''						)
									A_       	=  True
									A_       	=  True

									return cls(encoder=encoder_config.to_dict()       ,       decoder=decoder_config.to_dict()       ,       **a_						)


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  copy.deepcopy(self.__dict__						)
									A_       	=  self.encoder.to_dict()
									A_       	=  self.decoder.to_dict()
									A_       	=  self.__class__.model_type
									return output

from collections import namedtuple

SCREAMING_SNAKE_CASE   :		Any					 =       namedtuple("from_to", "from_ to")

SCREAMING_SNAKE_CASE   :		str					 =       {
    "atm": from_to(1, 1),
    "pascal": from_to(0.0000098, 10_1325),
    "bar": from_to(0.986923, 1.01325),
    "kilopascal": from_to(0.00986923, 101.325),
    "megapascal": from_to(9.86923, 0.101325),
    "psi": from_to(0.068046, 14.6959),
    "inHg": from_to(0.0334211, 29.9213),
    "torr": from_to(0.00131579, 760),
}



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


if __name__ == "__main__":
						import doctest

						doctest.testmod()