


'''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 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			str    ,      _lowercase      :			Optional[int]    ,      _lowercase      :			Tuple    ,      _lowercase      :			Any						):





     '''simple docstring'''






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

     # Load in the weights from the checkpoint_path
     __UpperCAmelCase			:				List[Any]													=    torch.load(_lowercase    ,      map_location='''cpu'''						)

     # Load the entity vocab file
     __UpperCAmelCase			:				Optional[int]													=    load_entity_vocab(_lowercase						)

     __UpperCAmelCase			:				List[Any]													=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']						)

     # Add special tokens to the token vocabulary for downstream tasks
     __UpperCAmelCase			:				List[str]													=    AddedToken('''<ent>'''    ,      lstrip=_lowercase    ,      rstrip=_lowercase						)
     __UpperCAmelCase			:				Any													=    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						)

     __UpperCAmelCase			:				List[str]													=    LukeTokenizer.from_pretrained(_lowercase						)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
     __UpperCAmelCase			:				Dict													=    state_dict['''entity_embeddings.entity_embeddings.weight''']
     __UpperCAmelCase			:				Optional[int]													=    entity_emb[entity_vocab['''[MASK]''']]

     __UpperCAmelCase			:				Dict													=    LukeModel(config=_lowercase						).eval()

     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    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
     __UpperCAmelCase			:				str													=    LukeTokenizer.from_pretrained(_lowercase    ,      task='''entity_classification'''						)

     __UpperCAmelCase			:				Optional[int]													=    (
         '''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 .'''
     )
     __UpperCAmelCase			:				Union[str, Any]													=    (39, 42)
     __UpperCAmelCase			:				Any													=    tokenizer(_lowercase    ,      entity_spans=[span]    ,      add_prefix_space=_lowercase    ,      return_tensors='''pt'''						)

     __UpperCAmelCase			:				List[str]													=    model(**_lowercase						)

     # Verify word hidden states
     if model_size == "large":
          __UpperCAmelCase			:				Optional[int]													=    torch.Size((1, 42, 1024)						)
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor(
              [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]						)
     else:  # base
          __UpperCAmelCase			:				List[Any]													=    torch.Size((1, 42, 768)						)
          __UpperCAmelCase			:				Any													=    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":
          __UpperCAmelCase			:				Optional[int]													=    torch.Size((1, 1, 1024)						)
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor([[0.0466, -0.0106, -0.0179]]						)
     else:  # base
          __UpperCAmelCase			:				Optional[Any]													=    torch.Size((1, 1, 768)						)
          __UpperCAmelCase			:				List[Any]													=    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 			_a			(		_lowercase      :			str						):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    {}
     with open(_lowercase    ,      '''r'''    ,      encoding='''utf-8'''						) as f:
          for index, line in enumerate(_lowercase						):
               __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    line.rstrip().split('''\t'''						)
               __UpperCAmelCase			:				List[str]													=    index

     return entity_vocab


if __name__ == "__main__":
      __UpperCAmelCase    :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."
      )
      __UpperCAmelCase    :List[str]				       =     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'''



def 			_a			(		_lowercase      :			int = 4000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    b, a + b
     return sum(_lowercase						)


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