

'''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__      ,    a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     with open(a__						) as metadata_file:
          __SCREAMING_SNAKE_CASE     =      json.load(a__						)
     __SCREAMING_SNAKE_CASE     =      LukeConfig(use_entity_aware_attention=a__      ,    **metadata["""model_config"""]						)

     # Load in the weights from the checkpoint_path
     __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    map_location="""cpu"""						)["""module"""]

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

     __SCREAMING_SNAKE_CASE     =      XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]						)

     # Add special tokens to the token vocabulary for downstream tasks
     __SCREAMING_SNAKE_CASE     =      AddedToken("""<ent>"""      ,    lstrip=a__      ,    rstrip=a__						)
     __SCREAMING_SNAKE_CASE     =      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:
          __SCREAMING_SNAKE_CASE     =      json.load(a__						)
     __SCREAMING_SNAKE_CASE     =      """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__						)

     __SCREAMING_SNAKE_CASE     =      MLukeTokenizer.from_pretrained(a__						)

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

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

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

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

     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      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
     __SCREAMING_SNAKE_CASE     =      MLukeTokenizer.from_pretrained(a__      ,    task="""entity_classification"""						)

     __SCREAMING_SNAKE_CASE     =      """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     =      (0, 9)
     __SCREAMING_SNAKE_CASE     =      tokenizer(a__      ,    entity_spans=[span]      ,    return_tensors="""pt"""						)

     __SCREAMING_SNAKE_CASE     =      model(**a__						)

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          __SCREAMING_SNAKE_CASE     =      torch.Size((1, 33, 7_68)						)
          __SCREAMING_SNAKE_CASE     =      torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]						)

     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
          __SCREAMING_SNAKE_CASE     =      torch.Size((1, 1, 7_68)						)
          __SCREAMING_SNAKE_CASE     =      torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]						)

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

     __SCREAMING_SNAKE_CASE     =      model(**a__						)

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

     __SCREAMING_SNAKE_CASE     =      outputs.entity_logits[0][0].argmax().item()
     __SCREAMING_SNAKE_CASE     =      [
         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__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      ["""[MASK]""", """[PAD]""", """[UNK]"""]

     __SCREAMING_SNAKE_CASE     =      [json.loads(a__						) for line in open(a__						)]

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


if __name__ == "__main__":
  UpperCAmelCase :       str			   =     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.'
  )
  UpperCAmelCase :       int			   =     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 numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    input_matrix.conj().T						)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    a__						)
          # Normalize the resulting output vector.
          __SCREAMING_SNAKE_CASE     =      w / np.linalg.norm(a__						)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __SCREAMING_SNAKE_CASE     =      vector.conj().T if is_complex else vector.T
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    np.dot(a__      ,    a__						)						)

          # Check convergence.
          __SCREAMING_SNAKE_CASE     =      np.abs(lambda_ - lambda_previous						) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __SCREAMING_SNAKE_CASE     =      True

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __SCREAMING_SNAKE_CASE     =      real_input_matrix
               __SCREAMING_SNAKE_CASE     =      real_vector
          elif problem_type == "complex":
               __SCREAMING_SNAKE_CASE     =      complex_input_matrix
               __SCREAMING_SNAKE_CASE     =      complex_vector

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






