





'''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      __A						(							a_						:  int      ,a_						:  Union[str, Any]      ,a_						:  str      ,a_						:  Dict      ,a_						:  Tuple	):
  # Load configuration defined in the metadata file
  with open(a_	) as metadata_file:
    lowerCAmelCase     :    List[str]						=   json.load(a_	)
  lowerCAmelCase     :    Optional[Any]						=   LukeConfig(use_entity_aware_attention=a_      ,**metadata["model_config"]	)

  # Load in the weights from the checkpoint_path
  lowerCAmelCase     :    Dict						=   torch.load(a_      ,map_location="cpu"	)["module"]

  # Load the entity vocab file
  lowerCAmelCase     :    Tuple						=   load_original_entity_vocab(a_	)
  # add an entry for [MASK2]
  lowerCAmelCase     :    Union[str, Any]						=   max(entity_vocab.values()	) + 1
  config.entity_vocab_size += 1

  lowerCAmelCase     :    Optional[Any]						=   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]	)

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

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

  lowerCAmelCase     :    str						=   MLukeTokenizer.from_pretrained(a_	)

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

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

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

  lowerCAmelCase     :    int						=   LukeForMaskedLM(config=a_	).eval()

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

  lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   model.load_state_dict(a_      ,strict=a_	)

  if set(a_	) != {"luke.embeddings.position_ids"}:
    raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''	)
  if set(a_	) != {
      "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
  lowerCAmelCase     :    Optional[int]						=   MLukeTokenizer.from_pretrained(a_      ,task="entity_classification"	)

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

  lowerCAmelCase     :    Dict						=   model(**a_	)

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase     :    Any						=   torch.Size((1, 3_3, 7_6_8)	)
    lowerCAmelCase     :    Any						=   torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]	)

  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":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase     :    Dict						=   torch.Size((1, 1, 7_6_8)	)
    lowerCAmelCase     :    List[str]						=   torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]	)

  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

  # Verify masked word/entity prediction
  lowerCAmelCase     :    Optional[int]						=   MLukeTokenizer.from_pretrained(a_	)
  lowerCAmelCase     :    Any						=   "Tokyo is the capital of <mask>."
  lowerCAmelCase     :    List[Any]						=   (2_4, 3_0)
  lowerCAmelCase     :    Dict						=   tokenizer(a_      ,entity_spans=[span]      ,return_tensors="pt"	)

  lowerCAmelCase     :    Any						=   model(**a_	)

  lowerCAmelCase     :    Dict						=   encoding["input_ids"][0].tolist()
  lowerCAmelCase     :    int						=   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"	)	)
  lowerCAmelCase     :    str						=   outputs.logits[0][mask_position_id].argmax(dim=-1	)
  assert "Japan" == tokenizer.decode(a_	)

  lowerCAmelCase     :    List[Any]						=   outputs.entity_logits[0][0].argmax().item()
  lowerCAmelCase     :    str						=   [
      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(a_	)	)
  model.save_pretrained(a_	)






def      __A						(							a_						:  Any	):
  lowerCAmelCase     :    Optional[int]						=   ["[MASK]", "[PAD]", "[UNK]"]

  lowerCAmelCase     :    List[str]						=   [json.loads(a_	) for line in open(a_	)]

  lowerCAmelCase     :    int						=   {}
  for entry in data:
    lowerCAmelCase     :    Union[str, Any]						=   entry["id"]
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        lowerCAmelCase     :    List[Any]						=   entity_id
        break
      lowerCAmelCase     :    Union[str, Any]						=   f'''{language}:{entity_name}'''
      lowerCAmelCase     :    List[Any]						=   entity_id
  return new_mapping


if __name__ == "__main__":
     lowerCAmelCase       		=      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       		=      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						(							a_						:  int      ,a_						:  int	):
		return abs(a_	) if a == 0 else greatest_common_divisor(b % a      ,a_	)






def      __A						(							a_						:  int      ,a_						:  int	):
		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   y, x % y
		return abs(a_	)






def      __A						(							):
		try:
				lowerCAmelCase     :    Tuple						=   input("Enter two integers separated by comma (,): "	).split(","	)
				lowerCAmelCase     :    Tuple						=   int(nums[0]	)
				lowerCAmelCase     :    Tuple						=   int(nums[1]	)
				print(
				    f'''greatest_common_divisor({num_a}, {num_a}) = '''
				    f'''{greatest_common_divisor(a_      ,a_	)}'''	)
				print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(a_      ,a_	)}'''	)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"	)


if __name__ == "__main__":
					main()



