
from typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE   :		int					 =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE   :		Optional[int]					 =       {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}






class 					__lowercase		(					A       ):
					__magic_name__						:							List[Any]     	=	"prophetnet"
					__magic_name__						:							List[Any]     	=	["past_key_values"]
					__magic_name__						:							Any     	=	{
					    "num_attention_heads": "num_encoder_attention_heads",
					}


					def __init__(	self       ,       a__ = 0.1       ,       a__ = "gelu"       ,       a__ = 3_0_5_2_2       ,       a__ = 1_0_2_4       ,       a__ = 4_0_9_6       ,       a__ = 1_2       ,       a__ = 1_6       ,       a__ = 4_0_9_6       ,       a__ = 1_2       ,       a__ = 1_6       ,       a__ = 0.1       ,       a__ = 0.1       ,       a__ = 5_1_2       ,       a__ = 0.02       ,       a__ = True       ,       a__ = True       ,       a__ = 0       ,       a__ = 2       ,       a__ = 3_2       ,       a__ = 1_2_8       ,       a__ = False       ,       a__ = 0.0       ,       a__ = True       ,       a__ = 0       ,       a__ = 1       ,       a__ = 2       ,       **a__       ,       )		->							List[Any]:





									'''simple docstring'''
									A_       	=  vocab_size
									A_       	=  hidden_size
									A_       	=  encoder_ffn_dim
									A_       	=  num_encoder_layers
									A_       	=  num_encoder_attention_heads
									A_       	=  decoder_ffn_dim
									A_       	=  num_decoder_layers
									A_       	=  num_decoder_attention_heads
									A_       	=  max_position_embeddings
									A_       	=  init_std  # Normal(0, this parameter)
									A_       	=  activation_function

									# parameters for prophetnet
									A_       	=  ngram
									A_       	=  num_buckets
									A_       	=  relative_max_distance
									A_       	=  disable_ngram_loss
									A_       	=  eps

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

									A_       	=  use_cache

									super().__init__(
									    pad_token_id=UpperCAmelCase_       ,       bos_token_id=UpperCAmelCase_       ,       eos_token_id=UpperCAmelCase_       ,       is_encoder_decoder=UpperCAmelCase_       ,       add_cross_attention=UpperCAmelCase_       ,       decoder_start_token_id=UpperCAmelCase_       ,       **UpperCAmelCase_       ,       )


					@property
					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									return self.num_encoder_layers + self.num_decoder_layers


					@num_hidden_layers.setter
					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									raise NotImplementedError(
									    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
									    ''' `num_decoder_layers`.'''						)

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()