
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 					__lowercase		(					A							,       A							,       A       ):


					@register_to_config
					def __init__(	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__ = False       ,       )		->							str:





									'''simple docstring'''
									super().__init__()

									A_       	=  nn.Linear(a__       ,       a__       ,       bias=a__						)

									A_       	=  nn.Embedding(a__       ,       a__						)
									A_       	=  False

									A_       	=  nn.Dropout(p=a__						)

									A_       	=  TaConfig(
									    d_model=a__       ,       num_heads=a__       ,       d_kv=a__       ,       d_ff=a__       ,       feed_forward_proj=a__       ,       dropout_rate=a__       ,       is_decoder=a__       ,       is_encoder_decoder=a__       ,       )
									A_       	=  nn.ModuleList()
									for lyr_num in range(a__						):
													A_       	=  TaBlock(a__						)
													self.encoders.append(a__						)

									A_       	=  TaLayerNorm(a__						)
									A_       	=  nn.Dropout(p=a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__						)		->							int:





									'''simple docstring'''
									A_       	=  self.input_proj(a__						)

									# terminal relative positional encodings
									A_       	=  encoder_inputs.shape[1]
									A_       	=  torch.arange(a__       ,       device=encoder_inputs.device						)

									A_       	=  encoder_inputs_mask.sum(-1						)
									A_       	=  torch.roll(input_positions.unsqueeze(0						)       ,       tuple(seq_lens.tolist()						)       ,       dims=0						)
									x += self.position_encoding(a__						)

									A_       	=  self.dropout_pre(a__						)

									# inverted the attention mask
									A_       	=  encoder_inputs.size()
									A_       	=  self.get_extended_attention_mask(a__       ,       a__						)

									for lyr in self.encoders:
													A_       	=  lyr(a__       ,       a__						)[0]
									A_       	=  self.layer_norm(a__						)

									return self.dropout_post(a__						), encoder_inputs_mask

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()