





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 	A_       (a_     ,							a_     ,							a_			):
	@register_to_config
	def __init__(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A							,					_A = False							,					):






				'''simple docstring'''







				super().__init__()

				UpperCAmelCase   		=				nn.Embedding(_A							,					_A		)

				UpperCAmelCase   		=				nn.Embedding(_A							,					_A		)
				UpperCAmelCase   		=				False

				UpperCAmelCase   		=				nn.Dropout(p=_A		)

				UpperCAmelCase   		=				TaConfig(
				    vocab_size=_A							,					d_model=_A							,					num_heads=_A							,					d_kv=_A							,					d_ff=_A							,					dropout_rate=_A							,					feed_forward_proj=_A							,					is_decoder=_A							,					is_encoder_decoder=_A							,					)

				UpperCAmelCase   		=				nn.ModuleList()
				for lyr_num in range(_A		):
							UpperCAmelCase   		=				TaBlock(_A		)
							self.encoders.append(_A		)

				UpperCAmelCase   		=				TaLayerNorm(_A		)
				UpperCAmelCase   		=				nn.Dropout(p=_A		)







	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.token_embedder(_A		)

				UpperCAmelCase   		=				encoder_input_tokens.shape[1]
				UpperCAmelCase   		=				torch.arange(_A							,					device=encoder_input_tokens.device		)
				x += self.position_encoding(_A		)

				UpperCAmelCase   		=				self.dropout_pre(_A		)

				# inverted the attention mask
				UpperCAmelCase   		=				encoder_input_tokens.size()
				UpperCAmelCase   		=				self.get_extended_attention_mask(_A							,					_A		)

				for lyr in self.encoders:
							UpperCAmelCase   		=				lyr(_A							,					_A		)[0]
				UpperCAmelCase   		=				self.layer_norm(_A		)

				return self.dropout_post(_A		), encoder_inputs_mask










from __future__ import annotations

from collections import namedtuple





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				namedtuple('''result'''			,			'''name value''' )
			if (voltage, current, power).count(0 ) != 1:
						raise ValueError('''Only one argument must be 0''' )
			elif power < 0:
						raise ValueError(
						    '''Power cannot be negative in any electrical/electronics system''' )
			elif voltage == 0:
						return result('''voltage'''			,			power / current )
			elif current == 0:
						return result('''current'''			,			power / voltage )
			elif power == 0:
						return result('''power'''			,			float(round(abs(voltage * current )			,			2 ) ) )
			else:
						raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



