






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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  with open(lowercase				) as metadata_file:
    SCREAMING_SNAKE_CASE       :      int							=    json.load(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    LukeConfig(use_entity_aware_attention=lowercase						,     **metadata["model_config"]				)

  # Load in the weights from the checkpoint_path
  SCREAMING_SNAKE_CASE       :      Any							=    torch.load(lowercase						,     map_location="cpu"				)["module"]

  # Load the entity vocab file
  SCREAMING_SNAKE_CASE       :      List[Any]							=    load_original_entity_vocab(lowercase				)
  # add an entry for [MASK2]
  SCREAMING_SNAKE_CASE       :      Dict							=    max(entity_vocab.values()				) + 1
  config.entity_vocab_size += 1

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]				)

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

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

  SCREAMING_SNAKE_CASE       :      str							=    MLukeTokenizer.from_pretrained(lowercase				)

  # Initialize the embeddings of the special tokens
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.convert_tokens_to_ids(["@"]				)[0]
  SCREAMING_SNAKE_CASE       :      int							=    tokenizer.convert_tokens_to_ids(["#"]				)[0]

  SCREAMING_SNAKE_CASE       :      int							=    state_dict["embeddings.word_embeddings.weight"]
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    word_emb[ent_init_index].unsqueeze(0				)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    word_emb[enta_init_index].unsqueeze(0				)
  SCREAMING_SNAKE_CASE       :      List[Any]							=    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"]:
    SCREAMING_SNAKE_CASE       :      int							=    state_dict[bias_name]
    SCREAMING_SNAKE_CASE       :      Dict							=    decoder_bias[ent_init_index].unsqueeze(0				)
    SCREAMING_SNAKE_CASE       :      Any							=    decoder_bias[enta_init_index].unsqueeze(0				)
    SCREAMING_SNAKE_CASE       :      Tuple							=    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"]:
      SCREAMING_SNAKE_CASE       :      Tuple							=    F'''encoder.layer.{layer_index}.attention.self.'''
      SCREAMING_SNAKE_CASE       :      int							=    state_dict[prefix + matrix_name]
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    state_dict[prefix + matrix_name]
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    state_dict[prefix + matrix_name]

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

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    LukeForMaskedLM(config=lowercase				).eval()

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

  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model.load_state_dict(lowercase						,     strict=lowercase				)

  if set(lowercase				) != {"luke.embeddings.position_ids"}:
    raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''				)
  if set(lowercase				) != {
      "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
  SCREAMING_SNAKE_CASE       :      int							=    MLukeTokenizer.from_pretrained(lowercase						,     task="entity_classification"				)

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

  SCREAMING_SNAKE_CASE       :      List[str]							=    model(**lowercase				)

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.Size((1, 33, 768)				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]				)

  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":
    raise NotImplementedError
  else:  # base
    SCREAMING_SNAKE_CASE       :      Dict							=    torch.Size((1, 1, 768)				)
    SCREAMING_SNAKE_CASE       :      int							=    torch.tensor([[-0.1482, 0.0609, 0.0322]]				)

  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

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

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(**lowercase				)

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

  SCREAMING_SNAKE_CASE       :      Dict							=    outputs.entity_logits[0][0].argmax().item()
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [
      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(lowercase				)				)
  model.save_pretrained(lowercase				)


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    ["[MASK]", "[PAD]", "[UNK]"]

  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [json.loads(lowercase				) for line in open(lowercase				)]

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


if __name__ == "__main__":
   snake_case    				=		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."""
   )
   snake_case    				=		parser.parse_args()
   convert_luke_checkpoint(
       args.checkpoint_path,
       args.metadata_path,
       args.entity_vocab_path,
       args.pytorch_dump_folder_path,
       args.model_size,
   )














def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    [0] * len(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    []
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
  SCREAMING_SNAKE_CASE       :      Any							=    0

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowercase				)				):
    if indegree[i] == 0:
      queue.append(lowercase				)

  while queue:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    queue.pop(0				)
    cnt += 1
    topo.append(lowercase				)
    for x in graph[vertex]:
      indegree[x] -= 1
      if indegree[x] == 0:
        queue.append(lowercase				)

  if cnt != len(lowercase				):
    print("Cycle exists"				)
  else:
    print(lowercase				)


# Adjacency List of Graph
snake_case    				=		{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)






