"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Any	)       ->      int:
  # Load configuration defined in the metadata file
  with open(_lowerCamelCase	) as metadata_file:
    _lowerCAmelCase						:       Tuple  			=     json.load(_lowerCamelCase	)
  _lowerCAmelCase						:       Dict  			=     LukeConfig(use_entity_aware_attention=_lowerCamelCase			,**metadata["""model_config"""]	)

  # Load in the weights from the checkpoint_path
  _lowerCAmelCase						:       List[Any]  			=     torch.load(_lowerCamelCase			,map_location="""cpu"""	)

  # Load the entity vocab file
  _lowerCAmelCase						:       Optional[int]  			=     load_entity_vocab(_lowerCamelCase	)

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

  # Add special tokens to the token vocabulary for downstream tasks
  _lowerCAmelCase						:       Optional[Any]  			=     AddedToken("""<ent>"""			,lstrip=_lowerCamelCase			,rstrip=_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     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	)

  _lowerCAmelCase						:       List[Any]  			=     LukeTokenizer.from_pretrained(_lowerCamelCase	)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
  _lowerCAmelCase						:       Union[str, Any]  			=     state_dict["""entity_embeddings.entity_embeddings.weight"""]
  _lowerCAmelCase						:       Dict  			=     entity_emb[entity_vocab["""[MASK]"""]]

  _lowerCAmelCase						:       int  			=     LukeModel(config=_lowerCamelCase	).eval()

  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     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
  _lowerCAmelCase						:       List[Any]  			=     LukeTokenizer.from_pretrained(_lowerCamelCase			,task="""entity_classification"""	)

  _lowerCAmelCase						:       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 ."""
  )
  _lowerCAmelCase						:       Dict  			=     (39, 42)
  _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(_lowerCamelCase			,entity_spans=[span]			,add_prefix_space=_lowerCamelCase			,return_tensors="""pt"""	)

  _lowerCAmelCase						:       List[str]  			=     model(**_lowerCamelCase	)

  # Verify word hidden states
  if model_size == "large":
    _lowerCAmelCase						:       List[str]  			=     torch.Size((1, 42, 1024)	)
    _lowerCAmelCase						:       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
    _lowerCAmelCase						:       int  			=     torch.Size((1, 42, 768)	)
    _lowerCAmelCase						:       Tuple  			=     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]			,_lowerCamelCase			,atol=1e-4	):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    _lowerCAmelCase						:       Optional[int]  			=     torch.Size((1, 1, 1024)	)
    _lowerCAmelCase						:       Tuple  			=     torch.tensor([[0.04_66, -0.01_06, -0.01_79]]	)
  else:  # base
    _lowerCAmelCase						:       Optional[int]  			=     torch.Size((1, 1, 768)	)
    _lowerCAmelCase						:       Dict  			=     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]			,_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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Dict:
  _lowerCAmelCase						:       List[str]  			=     {}
  with open(_lowerCamelCase			,"""r"""			,encoding="""utf-8"""	) as f:
    for index, line in enumerate(_lowerCamelCase	):
      _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     line.rstrip().split("""\t"""	)
      _lowerCAmelCase						:       Any  			=     index

  return entity_vocab


if __name__ == "__main__":
     _a      :		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.'
     )
     _a      :		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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




