


'''simple docstring'''






import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    LiltForQuestionAnswering,
							    LiltForSequenceClassification,
							    LiltForTokenClassification,
							    LiltModel,
							)
							from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class 						__UpperCamelCase	:





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]=1_3  ,_UpperCamelCase		:List[str]=7  ,_UpperCamelCase		:List[str]=True  ,_UpperCamelCase		:str=True  ,_UpperCamelCase		:Union[str, Any]=True  ,_UpperCamelCase		:List[str]=True  ,_UpperCamelCase		:Optional[Any]=9_9  ,_UpperCamelCase		:List[str]=2_4  ,_UpperCamelCase		:Union[str, Any]=2  ,_UpperCamelCase		:Any=6  ,_UpperCamelCase		:int=3_7  ,_UpperCamelCase		:List[Any]="gelu"  ,_UpperCamelCase		:str=0.1  ,_UpperCamelCase		:Union[str, Any]=0.1  ,_UpperCamelCase		:str=5_1_2  ,_UpperCamelCase		:Tuple=1_6  ,_UpperCamelCase		:Union[str, Any]=2  ,_UpperCamelCase		:List[str]=0.02  ,_UpperCamelCase		:Optional[Any]=3  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Optional[Any]=1_0_0_0  ,):
									snake_case_		:							str			       =						parent
									snake_case_		:							Dict			       =						batch_size
									snake_case_		:							Union[str, Any]			       =						seq_length
									snake_case_		:							List[str]			       =						is_training
									snake_case_		:							Union[str, Any]			       =						use_input_mask
									snake_case_		:							List[str]			       =						use_token_type_ids
									snake_case_		:							Optional[Any]			       =						use_labels
									snake_case_		:							str			       =						vocab_size
									snake_case_		:							Any			       =						hidden_size
									snake_case_		:							int			       =						num_hidden_layers
									snake_case_		:							str			       =						num_attention_heads
									snake_case_		:							List[str]			       =						intermediate_size
									snake_case_		:							Union[str, Any]			       =						hidden_act
									snake_case_		:							Optional[int]			       =						hidden_dropout_prob
									snake_case_		:							Dict			       =						attention_probs_dropout_prob
									snake_case_		:							int			       =						max_position_embeddings
									snake_case_		:							int			       =						type_vocab_size
									snake_case_		:							Tuple			       =						type_sequence_label_size
									snake_case_		:							List[str]			       =						initializer_range
									snake_case_		:							int			       =						num_labels
									snake_case_		:							List[Any]			       =						scope
									snake_case_		:							str			       =						range_bbox





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							List[Any]			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size			)

									snake_case_		:							List[str]			       =						ids_tensor([self.batch_size, self.seq_length, 4]  ,self.range_bbox			)
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]			):
											for j in range(bbox.shape[1]			):
													if bbox[i, j, 3] < bbox[i, j, 1]:
															snake_case_		:							Optional[int]			       =						bbox[i, j, 3]
															snake_case_		:							Any			       =						bbox[i, j, 1]
															snake_case_		:							List[str]			       =						t
													if bbox[i, j, 2] < bbox[i, j, 0]:
															snake_case_		:							Any			       =						bbox[i, j, 2]
															snake_case_		:							Union[str, Any]			       =						bbox[i, j, 0]
															snake_case_		:							Any			       =						t

									snake_case_		:							str			       =						None
									if self.use_input_mask:
											snake_case_		:							List[str]			       =						ids_tensor([self.batch_size, self.seq_length]  ,vocab_size=2			)

									snake_case_		:							List[Any]			       =						None
									if self.use_token_type_ids:
											snake_case_		:							int			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size			)

									snake_case_		:							Optional[int]			       =						None
									snake_case_		:							str			       =						None
									if self.use_labels:
											snake_case_		:							Optional[Any]			       =						ids_tensor([self.batch_size]  ,self.type_sequence_label_size			)
											snake_case_		:							Union[str, Any]			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels			)

									snake_case_		:							Union[str, Any]			       =						self.get_config()

									return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels





							def 		a__   (  self		:str			):
									return LiltConfig(
									    vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,intermediate_size=self.intermediate_size  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,max_position_embeddings=self.max_position_embeddings  ,type_vocab_size=self.type_vocab_size  ,initializer_range=self.initializer_range  ,)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Optional[Any]  ,):
									snake_case_		:							Optional[Any]			       =						LiltModel(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							List[Any]			       =						model(_UpperCamelCase  ,bbox=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase			)
									snake_case_		:							int			       =						model(_UpperCamelCase  ,bbox=_UpperCamelCase  ,token_type_ids=_UpperCamelCase			)
									snake_case_		:							str			       =						model(_UpperCamelCase  ,bbox=_UpperCamelCase			)

									self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)			)
									self.parent.assertEqual(result.pooler_output.shape  ,(self.batch_size, self.hidden_size)			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Optional[int]  ,):
									snake_case_		:							List[str]			       =						self.num_labels
									snake_case_		:							List[str]			       =						LiltForTokenClassification(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							Tuple			       =						model(
									    _UpperCamelCase  ,bbox=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,labels=_UpperCamelCase			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)			)





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Tuple  ,):
									snake_case_		:							Union[str, Any]			       =						LiltForQuestionAnswering(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							Tuple			       =						model(
									    _UpperCamelCase  ,bbox=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,start_positions=_UpperCamelCase  ,end_positions=_UpperCamelCase  ,)
									self.parent.assertEqual(result.start_logits.shape  ,(self.batch_size, self.seq_length)			)
									self.parent.assertEqual(result.end_logits.shape  ,(self.batch_size, self.seq_length)			)





							def 		a__   (  self		:List[str]			):
									snake_case_		:							str			       =						self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      
									)		:							Tuple			       =						config_and_inputs
									snake_case_		:							Any			       =						{
									    """input_ids""": input_ids,
									    """bbox""": bbox,
									    """token_type_ids""": token_type_ids,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict





@require_torch
class 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		Tuple   		=  (
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase			:		Optional[Any]   		=  (
							    {
							        'feature-extraction': LiltModel,
							        'question-answering': LiltForQuestionAnswering,
							        'text-classification': LiltForSequenceClassification,
							        'token-classification': LiltForTokenClassification,
							        'zero-shot': LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase			:		List[Any]   		=  False
							lowercase			:		Optional[Any]   		=  False





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Optional[int]			):
									return True





							def 		a__   (  self		:Dict			):
									snake_case_		:							Optional[Any]			       =						LiltModelTester(self			)
									snake_case_		:							List[str]			       =						ConfigTester(self  ,config_class=_UpperCamelCase  ,hidden_size=3_7			)





							def 		a__   (  self		:Dict			):
									self.config_tester.run_common_tests()





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Optional[Any]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											snake_case_		:							Dict			       =						type
											self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							str			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase			)





							@slow
							def 		a__   (  self		:Any			):
									for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:							List[Any]			       =						LiltModel.from_pretrained(_UpperCamelCase			)
											self.assertIsNotNone(_UpperCamelCase			)





@require_torch
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:str			):
									snake_case_		:							int			       =						LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""			).to(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						torch.tensor([[1, 2]]  ,device=_UpperCamelCase			)
									snake_case_		:							Any			       =						torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]  ,device=_UpperCamelCase			)

									# forward pass
									with torch.no_grad():
											snake_case_		:							List[Any]			       =						model(input_ids=_UpperCamelCase  ,bbox=_UpperCamelCase			)

									snake_case_		:							Optional[int]			       =						torch.Size([1, 2, 7_6_8]			)
									snake_case_		:							Tuple			       =						torch.tensor(
									    [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]  ,device=_UpperCamelCase  ,)

									self.assertTrue(outputs.last_hidden_state.shape  ,_UpperCamelCase			)
									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]  ,_UpperCamelCase  ,atol=1E-3			)			)



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						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:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


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