


'''simple docstring'''






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def      lowerCamelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		# Load configuration defined in the metadata file
		with open(lowerCamelCase_ ) as metadata_file:
				snake_case_		:							Tuple			       =						json.load(lowerCamelCase_ )
		snake_case_		:							Any			       =						LukeConfig(use_entity_aware_attention=lowerCamelCase_				,      **metadata["""model_config"""] )

		# Load in the weights from the checkpoint_path
		snake_case_		:							str			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )

		# Load the entity vocab file
		snake_case_		:							Optional[Any]			       =						load_entity_vocab(lowerCamelCase_ )

		snake_case_		:							Union[str, Any]			       =						RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] )

		# Add special tokens to the token vocabulary for downstream tasks
		snake_case_		:							Optional[Any]			       =						AddedToken("""<ent>"""				,      lstrip=lowerCamelCase_				,      rstrip=lowerCamelCase_ )
		snake_case_		:							str			       =						AddedToken("""<ent2>"""				,      lstrip=lowerCamelCase_				,      rstrip=lowerCamelCase_ )
		tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]} )
		config.vocab_size += 2

		print(F'''Saving tokenizer to {pytorch_dump_folder_path}''' )
		tokenizer.save_pretrained(lowerCamelCase_ )
		with open(os.path.join(lowerCamelCase_				,      LukeTokenizer.vocab_files_names["""entity_vocab_file"""] )				,      """w""" ) as f:
				json.dump(lowerCamelCase_				,      lowerCamelCase_ )

		snake_case_		:							Dict			       =						LukeTokenizer.from_pretrained(lowerCamelCase_ )

		# Initialize the embeddings of the special tokens
		snake_case_		:							Optional[Any]			       =						state_dict["""embeddings.word_embeddings.weight"""]
		snake_case_		:							Any			       =						word_emb[tokenizer.convert_tokens_to_ids(["""@"""] )[0]].unsqueeze(0 )
		snake_case_		:							Any			       =						word_emb[tokenizer.convert_tokens_to_ids(["""#"""] )[0]].unsqueeze(0 )
		snake_case_		:							Any			       =						torch.cat([word_emb, ent_emb, enta_emb] )

		# Initialize the query layers of the entity-aware self-attention mechanism
		for layer_index in range(config.num_hidden_layers ):
				for matrix_name in ["query.weight", "query.bias"]:
						snake_case_		:							Dict			       =						F'''encoder.layer.{layer_index}.attention.self.'''
						snake_case_		:							str			       =						state_dict[prefix + matrix_name]
						snake_case_		:							int			       =						state_dict[prefix + matrix_name]
						snake_case_		:							Tuple			       =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		snake_case_		:							List[str]			       =						state_dict["""entity_embeddings.entity_embeddings.weight"""]
		snake_case_		:							Tuple			       =						entity_emb[entity_vocab["""[MASK]"""]]

		snake_case_		:							Optional[int]			       =						LukeModel(config=lowerCamelCase_ ).eval()

		snake_case_		:							Tuple			       =						model.load_state_dict(lowerCamelCase_				,      strict=lowerCamelCase_ )
		if not (len(lowerCamelCase_ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
				raise ValueError(F'''Missing keys {", ".join(lowerCamelCase_ )}. Expected only missing embeddings.position_ids''' )
		if not (all(key.startswith("""entity_predictions""" ) or key.startswith("""lm_head""" ) for key in unexpected_keys )):
				raise ValueError(
				    """Unexpected keys"""
				    F''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}''' )

		# Check outputs
		snake_case_		:							Optional[int]			       =						LukeTokenizer.from_pretrained(lowerCamelCase_				,      task="""entity_classification""" )

		snake_case_		:							List[Any]			       =						(
		    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
		    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
		)
		snake_case_		:							List[Any]			       =						(39, 42)
		snake_case_		:							Optional[Any]			       =						tokenizer(lowerCamelCase_				,      entity_spans=[span]				,      add_prefix_space=lowerCamelCase_				,      return_tensors="""pt""" )

		snake_case_		:							List[str]			       =						model(**lowerCamelCase_ )

		# Verify word hidden states
		if model_size == "large":
				snake_case_		:							Dict			       =						torch.Size((1, 42, 10_24) )
				snake_case_		:							Union[str, Any]			       =						torch.tensor(
				    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
		else:  # base
				snake_case_		:							Any			       =						torch.Size((1, 42, 7_68) )
				snake_case_		:							Any			       =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )

		if not (outputs.last_hidden_state.shape == expected_shape):
				raise ValueError(
				    F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' )
		if not torch.allclose(outputs.last_hidden_state[0, :3, :3]				,      lowerCamelCase_				,      atol=1E-4 ):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				snake_case_		:							List[str]			       =						torch.Size((1, 1, 10_24) )
				snake_case_		:							Optional[Any]			       =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
		else:  # base
				snake_case_		:							Optional[int]			       =						torch.Size((1, 1, 7_68) )
				snake_case_		:							int			       =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )

		if not (outputs.entity_last_hidden_state.shape != expected_shape):
				raise ValueError(
				    F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
				    F''' {expected_shape}''' )
		if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]				,      lowerCamelCase_				,      atol=1E-4 ):
				raise ValueError

		# Finally, save our PyTorch model and tokenizer
		print("""Saving PyTorch model to {}""".format(lowerCamelCase_ ) )
		model.save_pretrained(lowerCamelCase_ )





def      lowerCamelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							str			       =						{}
		with open(lowerCamelCase_				,      """r"""				,      encoding="""utf-8""" ) as f:
				for index, line in enumerate(lowerCamelCase_ ):
						snake_case_		:							Any			       =						line.rstrip().split("""\t""" )
						snake_case_		:							Union[str, Any]			       =						index

		return entity_vocab


if __name__ == "__main__":
							__A	:	List[Any]     =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
							parser.add_argument(
							    '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
							)
							parser.add_argument(
							    '--entity_vocab_path',
							    default=None,
							    type=str,
							    help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
							)
							parser.add_argument(
							    '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
							)
							__A	:	Tuple     =     parser.parse_args()
							convert_luke_checkpoint(
							    args.checkpoint_path,
							    args.metadata_path,
							    args.entity_vocab_path,
							    args.pytorch_dump_folder_path,
							    args.model_size,
							)



'''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() = }')