





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      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:Dict  ,						_UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Dict  ,						_UpperCAmelCase							:int				)	->				List[str]:
     '''simple docstring'''


     with open(_lowercase				) as metadata_file:
          A_          =       json.load(_lowercase				)
     A_          =       LukeConfig(use_entity_aware_attention=_lowercase  ,						**metadata['''model_config''']				)

     # Load in the weights from the checkpoint_path
     A_          =       torch.load(_lowercase  ,						map_location='''cpu'''				)

     # Load the entity vocab file
     A_          =       load_entity_vocab(_lowercase				)

     A_          =       RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']				)

     # Add special tokens to the token vocabulary for downstream tasks
     A_          =       AddedToken('''<ent>'''  ,						lstrip=_lowercase  ,						rstrip=_lowercase				)
     A_          =       AddedToken('''<ent2>'''  ,						lstrip=_lowercase  ,						rstrip=_lowercase				)
     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(_lowercase				)
     with open(os.path.join(_lowercase  ,						LukeTokenizer.vocab_files_names['''entity_vocab_file''']				)  ,						'''w'''				) as f:
          json.dump(_lowercase  ,						_lowercase				)

     A_          =       LukeTokenizer.from_pretrained(_lowercase				)

     # Initialize the embeddings of the special tokens
     A_          =       state_dict['embeddings.word_embeddings.weight']
     A_          =       word_emb[tokenizer.convert_tokens_to_ids(['''@''']				)[0]].unsqueeze(0				)
     A_          =       word_emb[tokenizer.convert_tokens_to_ids(['''#''']				)[0]].unsqueeze(0				)
     A_          =       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"]:
               A_          =       f'encoder.layer.{layer_index}.attention.self.'
               A_          =       state_dict[prefix + matrix_name]
               A_          =       state_dict[prefix + matrix_name]
               A_          =       state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
     A_          =       state_dict['entity_embeddings.entity_embeddings.weight']
     A_          =       entity_emb[entity_vocab['[MASK]']]

     A_          =       LukeModel(config=_lowercase				).eval()

     A_          =       model.load_state_dict(_lowercase  ,						strict=_lowercase				)
     if not (len(_lowercase				) == 1 and missing_keys[0] == "embeddings.position_ids"):
          raise ValueError(f'Missing keys {", ".join(_lowercase				)}. 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
     A_          =       LukeTokenizer.from_pretrained(_lowercase  ,						task='''entity_classification'''				)

     A_          =       (
         '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 .'
     )
     A_          =       (39, 42)
     A_          =       tokenizer(_lowercase  ,						entity_spans=[span]  ,						add_prefix_space=_lowercase  ,						return_tensors='''pt'''				)

     A_          =       model(**_lowercase				)

     # Verify word hidden states
     if model_size == "large":
          A_          =       torch.Size((1, 42, 1024)				)
          A_          =       torch.tensor(
              [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]				)
     else:  # base
          A_          =       torch.Size((1, 42, 768)				)
          A_          =       torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]				)

     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]  ,						_lowercase  ,						atol=1e-4				):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          A_          =       torch.Size((1, 1, 1024)				)
          A_          =       torch.tensor([[0.0466, -0.0106, -0.0179]]				)
     else:  # base
          A_          =       torch.Size((1, 1, 768)				)
          A_          =       torch.tensor([[0.1457, 0.1044, 0.0174]]				)

     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]  ,						_lowercase  ,						atol=1e-4				):
          raise ValueError

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				List[str]:
     '''simple docstring'''


     A_          =       {}
     with open(_lowercase  ,						'''r'''  ,						encoding='''utf-8'''				) as f:
          for index, line in enumerate(_lowercase				):
               A_          =       line.rstrip().split('''\t'''				)
               A_          =       index

     return entity_vocab


if __name__ == "__main__":
      a__      :     Tuple         =		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__      :     Optional[int]         =		parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )










from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


