





'''simple docstring'''


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Union[str, Any]			):
   # Load configuration defined in the metadata file
   with open(__lowerCamelCase			) as metadata_file:
      lowercase_      :Any         =			json.load(__lowerCamelCase			)
   lowercase_      :str         =			LukeConfig(use_entity_aware_attention=__lowerCamelCase     ,**metadata["model_config"]			)

   # Load in the weights from the checkpoint_path
   lowercase_      :int         =			torch.load(__lowerCamelCase     ,map_location="cpu"			)["module"]

   # Load the entity vocab file
   lowercase_      :int         =			load_original_entity_vocab(__lowerCamelCase			)
   # add an entry for [MASK2]
   lowercase_      :str         =			max(entity_vocab.values()			) + 1
   config.entity_vocab_size += 1

   lowercase_      :Tuple         =			XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

   # Add special tokens to the token vocabulary for downstream tasks
   lowercase_      :Tuple         =			AddedToken("<ent>"     ,lstrip=__lowerCamelCase     ,rstrip=__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			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     ,"tokenizer_config.json"			)     ,"r"			) as f:
      lowercase_      :Optional[int]         =			json.load(__lowerCamelCase			)
   lowercase_      :Dict         =			"MLukeTokenizer"
   with open(os.path.join(__lowerCamelCase     ,"tokenizer_config.json"			)     ,"w"			) as f:
      json.dump(__lowerCamelCase     ,__lowerCamelCase			)

   with open(os.path.join(__lowerCamelCase     ,MLukeTokenizer.vocab_files_names["entity_vocab_file"]			)     ,"w"			) as f:
      json.dump(__lowerCamelCase     ,__lowerCamelCase			)

   lowercase_      :Any         =			MLukeTokenizer.from_pretrained(__lowerCamelCase			)

   # Initialize the embeddings of the special tokens
   lowercase_      :Dict         =			tokenizer.convert_tokens_to_ids(["@"]			)[0]
   lowercase_      :List[Any]         =			tokenizer.convert_tokens_to_ids(["#"]			)[0]

   lowercase_      :Dict         =			state_dict["embeddings.word_embeddings.weight"]
   lowercase_      :Any         =			word_emb[ent_init_index].unsqueeze(0			)
   lowercase_      :Union[str, Any]         =			word_emb[enta_init_index].unsqueeze(0			)
   lowercase_      :Optional[int]         =			torch.cat([word_emb, ent_emb, enta_emb]			)
   # add special tokens for 'entity_predictions.bias'
   for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
      lowercase_      :Any         =			state_dict[bias_name]
      lowercase_      :Tuple         =			decoder_bias[ent_init_index].unsqueeze(0			)
      lowercase_      :Optional[Any]         =			decoder_bias[enta_init_index].unsqueeze(0			)
      lowercase_      :Union[str, Any]         =			torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

   # 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"]:
         lowercase_      :Union[str, Any]         =			F'encoder.layer.{layer_index}.attention.self.'
         lowercase_      :int         =			state_dict[prefix + matrix_name]
         lowercase_      :int         =			state_dict[prefix + matrix_name]
         lowercase_      :List[str]         =			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   lowercase_      :Union[str, Any]         =			state_dict["entity_embeddings.entity_embeddings.weight"]
   lowercase_      :Dict         =			entity_emb[entity_vocab["[MASK]"]].unsqueeze(0			)
   lowercase_      :List[str]         =			torch.cat([entity_emb, entity_mask_emb]			)
   # add [MASK2] for 'entity_predictions.bias'
   lowercase_      :int         =			state_dict["entity_predictions.bias"]
   lowercase_      :str         =			entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0			)
   lowercase_      :str         =			torch.cat([entity_prediction_bias, entity_mask_bias]			)

   lowercase_      :str         =			LukeForMaskedLM(config=__lowerCamelCase			).eval()

   state_dict.pop("entity_predictions.decoder.weight"			)
   state_dict.pop("lm_head.decoder.weight"			)
   state_dict.pop("lm_head.decoder.bias"			)
   lowercase_      :str         =			OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith("lm_head"			) or key.startswith("entity_predictions"			)):
         lowercase_      :Optional[int]         =			state_dict[key]
      else:
         lowercase_      :str         =			state_dict[key]

   lowercase_      ,							lowercase_      :Optional[Any]         =			model.load_state_dict(__lowerCamelCase     ,strict=__lowerCamelCase			)

   if set(__lowerCamelCase			) != {"luke.embeddings.position_ids"}:
      raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
   if set(__lowerCamelCase			) != {
       "lm_head.decoder.weight",
       "lm_head.decoder.bias",
       "entity_predictions.decoder.weight",
   }:
      raise ValueError(F'Unexpected missing_keys: {missing_keys}'			)

   model.tie_weights()
   assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
   assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

   # Check outputs
   lowercase_      :Optional[Any]         =			MLukeTokenizer.from_pretrained(__lowerCamelCase     ,task="entity_classification"			)

   lowercase_      :Optional[Any]         =			"ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
   lowercase_      :Optional[Any]         =			(0, 9)
   lowercase_      :Dict         =			tokenizer(__lowerCamelCase     ,entity_spans=[span]     ,return_tensors="pt"			)

   lowercase_      :Tuple         =			model(**__lowerCamelCase			)

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowercase_      :Optional[Any]         =			torch.Size((1, 33, 7_68)			)
      lowercase_      :Optional[Any]         =			torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]			)

   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":
      raise NotImplementedError
   else:  # base
      lowercase_      :List[Any]         =			torch.Size((1, 1, 7_68)			)
      lowercase_      :Union[str, Any]         =			torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]			)

   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

   # Verify masked word/entity prediction
   lowercase_      :List[str]         =			MLukeTokenizer.from_pretrained(__lowerCamelCase			)
   lowercase_      :Dict         =			"Tokyo is the capital of <mask>."
   lowercase_      :List[Any]         =			(24, 30)
   lowercase_      :int         =			tokenizer(__lowerCamelCase     ,entity_spans=[span]     ,return_tensors="pt"			)

   lowercase_      :str         =			model(**__lowerCamelCase			)

   lowercase_      :List[Any]         =			encoding["input_ids"][0].tolist()
   lowercase_      :Any         =			input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
   lowercase_      :int         =			outputs.logits[0][mask_position_id].argmax(dim=-1			)
   assert "Japan" == tokenizer.decode(__lowerCamelCase			)

   lowercase_      :int         =			outputs.entity_logits[0][0].argmax().item()
   lowercase_      :Tuple         =			[
       entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
   ]
   assert [e for e in multilingual_predicted_entities if e.startswith("en:"			)][0] == "en:Japan"

   # Finally, save our PyTorch model and tokenizer
   print("Saving PyTorch model to {}".format(__lowerCamelCase			)			)
   model.save_pretrained(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : Dict			):
   lowercase_      :List[str]         =			["[MASK]", "[PAD]", "[UNK]"]

   lowercase_      :Optional[int]         =			[json.loads(__lowerCamelCase			) for line in open(__lowerCamelCase			)]

   lowercase_      :List[str]         =			{}
   for entry in data:
      lowercase_      :str         =			entry["id"]
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            lowercase_      :Tuple         =			entity_id
            break
         lowercase_      :Any         =			F'{language}:{entity_name}'
         lowercase_      :Tuple         =			entity_id
   return new_mapping


if __name__ == "__main__":
 lowerCAmelCase :					Union[str, 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.'''
 )
 lowerCAmelCase :					Dict 							=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

lowerCAmelCase :					Any 							={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}
class 				a_       :





     def __init__(			self		: List[Any]     ,				lowercase		: dict[str, list[str]]     ,				lowercase		: str						):





        """simple docstring"""




        lowercase_      :List[str]         =			graph
        # mapping node to its parent in resulting breadth first tree
        lowercase_      :dict[str, str | None]         =			{}
        lowercase_      :Dict         =			source_vertex





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			{self.source_vertex}
        lowercase_      :Tuple         =			None
        lowercase_      :Dict         =			[self.source_vertex]  # first in first out queue

        while queue:
           lowercase_      :int         =			queue.pop(0						)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(lowercase						)
                 lowercase_      :Optional[Any]         =			vertex
                 queue.append(lowercase						)







     def 							lowercase__				(			self		: Tuple     ,				lowercase		: str						):





        """simple docstring"""




        if target_vertex == self.source_vertex:
           return self.source_vertex

        lowercase_      :List[str]         =			self.parent.get(lowercase						)
        if target_vertex_parent is None:
           lowercase_      :Union[str, Any]         =			(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(lowercase						)

        return self.shortest_path(lowercase						) + F'->{target_vertex}'


if __name__ == "__main__":
 lowerCAmelCase :					Dict 							=Graph(graph, '''G''')
 g.breath_first_search()
 print(g.shortest_path('''D'''))
 print(g.shortest_path('''G'''))
 print(g.shortest_path('''Foo'''))


