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  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A							):
     # Load configuration defined in the metadata file
     with open(A							) as metadata_file:
          UpperCamelCase__										=      json.load(A							)
     UpperCamelCase__										=      LukeConfig(use_entity_aware_attention=A ,							**metadata['''model_config''']							)

     # Load in the weights from the checkpoint_path
     UpperCamelCase__										=      torch.load(A ,							map_location='''cpu'''							)['''module''']

     # Load the entity vocab file
     UpperCamelCase__										=      load_original_entity_vocab(A							)
     # add an entry for [MASK2]
     UpperCamelCase__										=      max(entity_vocab.values()							) + 1
     config.entity_vocab_size += 1

     UpperCamelCase__										=      XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

     # Add special tokens to the token vocabulary for downstream tasks
     UpperCamelCase__										=      AddedToken('''<ent>''' ,							lstrip=A ,							rstrip=A							)
     UpperCamelCase__										=      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:
          UpperCamelCase__										=      json.load(A							)
     UpperCamelCase__										=      '''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							)

     UpperCamelCase__										=      MLukeTokenizer.from_pretrained(A							)

     # Initialize the embeddings of the special tokens
     UpperCamelCase__										=      tokenizer.convert_tokens_to_ids(['''@''']							)[0]
     UpperCamelCase__										=      tokenizer.convert_tokens_to_ids(['''#''']							)[0]

     UpperCamelCase__										=      state_dict['''embeddings.word_embeddings.weight''']
     UpperCamelCase__										=      word_emb[ent_init_index].unsqueeze(0							)
     UpperCamelCase__										=      word_emb[enta_init_index].unsqueeze(0							)
     UpperCamelCase__										=      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__										=      state_dict[bias_name]
          UpperCamelCase__										=      decoder_bias[ent_init_index].unsqueeze(0							)
          UpperCamelCase__										=      decoder_bias[enta_init_index].unsqueeze(0							)
          UpperCamelCase__										=      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__										=      f"encoder.layer.{layer_index}.attention.self."
               UpperCamelCase__										=      state_dict[prefix + matrix_name]
               UpperCamelCase__										=      state_dict[prefix + matrix_name]
               UpperCamelCase__										=      state_dict[prefix + matrix_name]

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

     UpperCamelCase__										=      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'''							)
     UpperCamelCase__										=      OrderedDict()
     for key, value in state_dict.items():
          if not (key.startswith('''lm_head'''							) or key.startswith('''entity_predictions'''							)):
               UpperCamelCase__										=      state_dict[key]
          else:
               UpperCamelCase__										=      state_dict[key]

     UpperCamelCase__						,       UpperCamelCase__										=      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
     UpperCamelCase__										=      MLukeTokenizer.from_pretrained(A ,							task='''entity_classification'''							)

     UpperCamelCase__										=      '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
     UpperCamelCase__										=      (0, 9)
     UpperCamelCase__										=      tokenizer(A ,							entity_spans=[span] ,							return_tensors='''pt'''							)

     UpperCamelCase__										=      model(**A							)

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          UpperCamelCase__										=      torch.Size((1, 33, 768)							)
          UpperCamelCase__										=      torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]							)

     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
          UpperCamelCase__										=      torch.Size((1, 1, 768)							)
          UpperCamelCase__										=      torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]							)

     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
     UpperCamelCase__										=      MLukeTokenizer.from_pretrained(A							)
     UpperCamelCase__										=      '''Tokyo is the capital of <mask>.'''
     UpperCamelCase__										=      (24, 30)
     UpperCamelCase__										=      tokenizer(A ,							entity_spans=[span] ,							return_tensors='''pt'''							)

     UpperCamelCase__										=      model(**A							)

     UpperCamelCase__										=      encoding['''input_ids'''][0].tolist()
     UpperCamelCase__										=      input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''							)							)
     UpperCamelCase__										=      outputs.logits[0][mask_position_id].argmax(dim=-1							)
     assert "Japan" == tokenizer.decode(A							)

     UpperCamelCase__										=      outputs.entity_logits[0][0].argmax().item()
     UpperCamelCase__										=      [
         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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      ['''[MASK]''', '''[PAD]''', '''[UNK]''']

     UpperCamelCase__										=      [json.loads(A							) for line in open(A							)]

     UpperCamelCase__										=      {}
     for entry in data:
          UpperCamelCase__										=      entry['''id''']
          for entity_name, language in entry["entities"]:
               if entity_name in SPECIAL_TOKENS:
                    UpperCamelCase__										=      entity_id
                    break
               UpperCamelCase__										=      f"{language}:{entity_name}"
               UpperCamelCase__										=      entity_id
     return new_mapping


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




from __future__ import annotations







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      get_failure_array(A							)

     # 2) Step through text searching for pattern
     UpperCamelCase__						,       UpperCamelCase__										=      0, 0  # index into text, pattern
     while i < len(A							):
          if pattern[j] == text[i]:
               if j == (len(A							) - 1):
                    return True
               j += 1

          # if this is a prefix in our pattern
          # just go back far enough to continue
          elif j > 0:
               UpperCamelCase__										=      failure[j - 1]
               continue
          i += 1
     return False







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      [0]
     UpperCamelCase__										=      0
     UpperCamelCase__										=      1
     while j < len(A							):
          if pattern[i] == pattern[j]:
               i += 1
          elif i > 0:
               UpperCamelCase__										=      failure[i - 1]
               continue
          j += 1
          failure.append(A							)
     return failure


if __name__ == "__main__":
     # Test 1)
     __magic_name__								='''abc1abc12'''
     __magic_name__								='''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     __magic_name__								='''alskfjaldsk23adsfabcabc'''
     assert kmp(pattern, texta) and not kmp(pattern, texta)

     # Test 2)
     __magic_name__								='''ABABX'''
     __magic_name__								='''ABABZABABYABABX'''
     assert kmp(pattern, text)

     # Test 3)
     __magic_name__								='''AAAB'''
     __magic_name__								='''ABAAAAAB'''
     assert kmp(pattern, text)

     # Test 4)
     __magic_name__								='''abcdabcy'''
     __magic_name__								='''abcxabcdabxabcdabcdabcy'''
     assert kmp(pattern, text)

     # Test 5)
     __magic_name__								='''aabaabaaa'''
     assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]



