





"""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       __snake_case    (   __A			,__A			,__A			,__A			,__A     )		->					str:
   # Load configuration defined in the metadata file
   with open(A__     ) as metadata_file:
      lowercase     :      List[Any]	=							json.load(A__     )
   lowercase     :      str	=							LukeConfig(use_entity_aware_attention=A__			,**metadata["""model_config"""]     )

   # Load in the weights from the checkpoint_path
   lowercase     :      Tuple	=							torch.load(A__			,map_location="""cpu"""     )

   # Load the entity vocab file
   lowercase     :      Optional[Any]	=							load_entity_vocab(A__     )

   lowercase     :      List[Any]	=							RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]     )

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

   lowercase     :      Dict	=							LukeTokenizer.from_pretrained(A__     )

   # Initialize the embeddings of the special tokens
   lowercase     :      int	=							state_dict["""embeddings.word_embeddings.weight"""]
   lowercase     :      Union[str, Any]	=							word_emb[tokenizer.convert_tokens_to_ids(["""@"""]     )[0]].unsqueeze(0     )
   lowercase     :      List[str]	=							word_emb[tokenizer.convert_tokens_to_ids(["""#"""]     )[0]].unsqueeze(0     )
   lowercase     :      List[str]	=							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"]:
         lowercase     :      Any	=							F'''encoder.layer.{layer_index}.attention.self.'''
         lowercase     :      List[Any]	=							state_dict[prefix + matrix_name]
         lowercase     :      List[Any]	=							state_dict[prefix + matrix_name]
         lowercase     :      str	=							state_dict[prefix + matrix_name]

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

   lowercase     :      Dict	=							LukeModel(config=A__     ).eval()

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

   lowercase     :      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 ."""
   )
   lowercase     :      Dict	=							(39, 42)
   lowercase     :      Dict	=							tokenizer(A__			,entity_spans=[span]			,add_prefix_space=A__			,return_tensors="""pt"""     )

   lowercase     :      Optional[Any]	=							model(**A__     )

   # Verify word hidden states
   if model_size == "large":
      lowercase     :      List[Any]	=							torch.Size((1, 42, 1024)     )
      lowercase     :      List[str]	=							torch.tensor(
          [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]     )
   else:  # base
      lowercase     :      Tuple	=							torch.Size((1, 42, 768)     )
      lowercase     :      Dict	=							torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]     )

   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]			,A__			,atol=1E-4     ):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      lowercase     :      Optional[int]	=							torch.Size((1, 1, 1024)     )
      lowercase     :      Optional[Any]	=							torch.tensor([[0.04_66, -0.01_06, -0.01_79]]     )
   else:  # base
      lowercase     :      int	=							torch.Size((1, 1, 768)     )
      lowercase     :      Optional[Any]	=							torch.tensor([[0.14_57, 0.10_44, 0.01_74]]     )

   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]			,A__			,atol=1E-4     ):
      raise ValueError

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




def       __snake_case    (   __A     )		->					Optional[Any]:
   lowercase     :      List[Any]	=							{}
   with open(A__			,"""r"""			,encoding="""utf-8"""     ) as f:
      for index, line in enumerate(A__     ):
         lowercase	,       lowercase     :      Dict	=							line.rstrip().split("""\t"""     )
         lowercase     :      Tuple	=							index

   return entity_vocab


if __name__ == "__main__":
   lowerCAmelCase:						Optional[int]				   =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:						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,
   )





import os





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   os.path.join(os.path.dirname(A__       )			,  "num.txt"       )
    with open(A__       ) as file_hand:
        return str(sum(int(A__       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
    print(solution())