






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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  # Load configuration defined in the metadata file
  with open(UpperCAmelCase__    ) as metadata_file:
    UpperCamelCase_:   Tuple        =							json.load(UpperCAmelCase__    )
  UpperCamelCase_:   List[str]        =							LukeConfig(use_entity_aware_attention=UpperCAmelCase__      ,       **metadata['model_config']    )

  # Load in the weights from the checkpoint_path
  UpperCamelCase_:   Optional[int]        =							torch.load(UpperCAmelCase__      ,       map_location='cpu'    )['module']

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

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

  # Add special tokens to the token vocabulary for downstream tasks
  UpperCamelCase_:   Any        =							AddedToken('<ent>'      ,       lstrip=UpperCAmelCase__      ,       rstrip=UpperCAmelCase__    )
  UpperCamelCase_:   Optional[Any]        =							AddedToken('<ent2>'      ,       lstrip=UpperCAmelCase__      ,       rstrip=UpperCAmelCase__    )
  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(UpperCAmelCase__    )
  with open(os.path.join(UpperCAmelCase__      ,       'tokenizer_config.json'    )      ,       'r'    ) as f:
    UpperCamelCase_:   Union[str, Any]        =							json.load(UpperCAmelCase__    )
  UpperCamelCase_:   str        =							'MLukeTokenizer'
  with open(os.path.join(UpperCAmelCase__      ,       'tokenizer_config.json'    )      ,       'w'    ) as f:
    json.dump(UpperCAmelCase__      ,       UpperCAmelCase__    )

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

  UpperCamelCase_:   Optional[Any]        =							MLukeTokenizer.from_pretrained(UpperCAmelCase__    )

  # Initialize the embeddings of the special tokens
  UpperCamelCase_:   Any        =							tokenizer.convert_tokens_to_ids(['@']    )[0]
  UpperCamelCase_:   List[str]        =							tokenizer.convert_tokens_to_ids(['#']    )[0]

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

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

  UpperCamelCase_:   List[Any]        =							LukeForMaskedLM(config=UpperCAmelCase__    ).eval()

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

  UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							model.load_state_dict(UpperCAmelCase__      ,       strict=UpperCAmelCase__    )

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

  UpperCamelCase_:   Tuple        =							'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
  UpperCamelCase_:   Optional[int]        =							(0, 9)
  UpperCamelCase_:   Union[str, Any]        =							tokenizer(UpperCAmelCase__      ,       entity_spans=[span]      ,       return_tensors='pt'    )

  UpperCamelCase_:   str        =							model(**UpperCAmelCase__    )

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    UpperCamelCase_:   int        =							torch.Size((1, 3_3, 7_6_8)    )
    UpperCamelCase_:   Tuple        =							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]      ,       UpperCAmelCase__      ,       atol=1E-4    ):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    UpperCamelCase_:   Dict        =							torch.Size((1, 1, 7_6_8)    )
    UpperCamelCase_:   Tuple        =							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]      ,       UpperCAmelCase__      ,       atol=1E-4    ):
    raise ValueError

  # Verify masked word/entity prediction
  UpperCamelCase_:   str        =							MLukeTokenizer.from_pretrained(UpperCAmelCase__    )
  UpperCamelCase_:   int        =							'Tokyo is the capital of <mask>.'
  UpperCamelCase_:   Dict        =							(2_4, 3_0)
  UpperCamelCase_:   int        =							tokenizer(UpperCAmelCase__      ,       entity_spans=[span]      ,       return_tensors='pt'    )

  UpperCamelCase_:   Dict        =							model(**UpperCAmelCase__    )

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

  UpperCamelCase_:   Union[str, Any]        =							outputs.entity_logits[0][0].argmax().item()
  UpperCamelCase_:   Optional[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(UpperCAmelCase__    )    )
  model.save_pretrained(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							int:
  UpperCamelCase_:   Optional[Any]        =							['[MASK]', '[PAD]', '[UNK]']

  UpperCamelCase_:   Any        =							[json.loads(UpperCAmelCase__    ) for line in open(UpperCAmelCase__    )]

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


if __name__ == "__main__":
   A_						:						Tuple						      =					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"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




