



"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

UpperCamelCase     =							tuple[int, int]






class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				A__											=					vertices
				A__											=					{
				    (min(SCREAMING_SNAKE_CASE__		), max(SCREAMING_SNAKE_CASE__		)): weight for edge, weight in edges.items()
				}


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				self.vertices.add(edge[0]		)
				self.vertices.add(edge[1]		)
				A__											=					weight







			def   snake_case__	(      self		)      ->	Graph:
				A__											=					Graph({min(self.vertices		)}						,	{}		)
				A__											=					42
				A__											=					42
				A__											=					42
				A__											=					42

				while len(subgraph.vertices		) < len(self.vertices		):
					A__											=					max(self.edges.values()		) + 1
					for edge, weight in self.edges.items():
						if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
							if weight < min_weight:
								A__											=					edge
								A__											=					weight

					subgraph.add_edge(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				return subgraph



def       _lowerCamelCase				(							UpperCAmelCase_ :       str = "p107_network.txt"     )	-> int:






	"""simple docstring"""





	A__											=					os.path.abspath(os.path.dirname(UpperCAmelCase_     )     )
	A__											=					os.path.join(UpperCAmelCase_,	UpperCAmelCase_     )
	A__											=					{}
	A__											=					42
	A__											=					42
	A__											=					42

	with open(UpperCAmelCase_     ) as f:
		A__											=					f.read().strip().split("\n"     )

	A__											=					[line.split(","     ) for line in data]

	for edgea in range(1,	len(UpperCAmelCase_     )     ):
		for edgea in range(UpperCAmelCase_     ):
			if adjaceny_matrix[edgea][edgea] != "-":
				A__											=					int(adjaceny_matrix[edgea][edgea]     )

	A__											=					Graph(set(range(len(UpperCAmelCase_     )     )     ),	UpperCAmelCase_     )

	A__											=					graph.prims_algorithm()

	A__											=					sum(graph.edges.values()     )
	A__											=					sum(subgraph.edges.values()     )

	return initial_total - optimal_total


if __name__ == "__main__":
			print(f'{solution() = }')









"""simple docstring"""







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       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False						,	)      ->	Union[str, Any]:
				super().__init__()

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					False

				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)

				A__											=					TaConfig(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	d_model=SCREAMING_SNAKE_CASE__						,	num_heads=SCREAMING_SNAKE_CASE__						,	d_kv=SCREAMING_SNAKE_CASE__						,	d_ff=SCREAMING_SNAKE_CASE__						,	dropout_rate=SCREAMING_SNAKE_CASE__						,	feed_forward_proj=SCREAMING_SNAKE_CASE__						,	is_decoder=SCREAMING_SNAKE_CASE__						,	is_encoder_decoder=SCREAMING_SNAKE_CASE__						,	)

				A__											=					nn.ModuleList()
				for lyr_num in range(SCREAMING_SNAKE_CASE__		):
					A__											=					TaBlock(SCREAMING_SNAKE_CASE__		)
					self.encoders.append(SCREAMING_SNAKE_CASE__		)

				A__											=					TaLayerNorm(SCREAMING_SNAKE_CASE__		)
				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					self.token_embedder(SCREAMING_SNAKE_CASE__		)

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

				A__											=					self.dropout_pre(SCREAMING_SNAKE_CASE__		)

				# inverted the attention mask
				A__											=					encoder_input_tokens.size()
				A__											=					self.get_extended_attention_mask(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				for lyr in self.encoders:
					A__											=					lyr(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)[0]
				A__											=					self.layer_norm(SCREAMING_SNAKE_CASE__		)

				return self.dropout_post(SCREAMING_SNAKE_CASE__		), encoder_inputs_mask




