

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

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

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

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

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

       A_		   = MLukeTokenizer.from_pretrained(UpperCAmelCase__			)

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

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

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

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

       A_   ,     A_		   = 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
       A_		   = MLukeTokenizer.from_pretrained(UpperCAmelCase__,    task="""entity_classification"""			)

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

       A_		   = model(**UpperCAmelCase__			)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_		   = torch.Size((1, 33, 7_68)			)
              A_		   = 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],    UpperCAmelCase__,    atol=1e-4			):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_		   = torch.Size((1, 1, 7_68)			)
              A_		   = 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],    UpperCAmelCase__,    atol=1e-4			):
              raise ValueError

       # Verify masked word/entity prediction
       A_		   = MLukeTokenizer.from_pretrained(UpperCAmelCase__			)
       A_		   = """Tokyo is the capital of <mask>."""
       A_		   = (24, 30)
       A_		   = tokenizer(UpperCAmelCase__,    entity_spans=[span],    return_tensors="""pt"""			)

       A_		   = model(**UpperCAmelCase__			)

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

       A_		   = outputs.entity_logits[0][0].argmax().item()
       A_		   = [
           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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = ["""[MASK]""", """[PAD]""", """[UNK]"""]

       A_		   = [json.loads(UpperCAmelCase__			) for line in open(UpperCAmelCase__			)]

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







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = git.Repo(search_parent_directories=UpperCAmelCase__			)
       A_		   = {
           """repo_id""": str(UpperCAmelCase__			),
           """repo_sha""": str(repo.head.object.hexsha			),
           """repo_branch""": str(repo.active_branch			),
       }

       with open(os.path.join(UpperCAmelCase__,    """git_log.json"""			),    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__,    indent=4			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       if params.n_gpu <= 0:
              A_		   = 0
              A_		   = -1
              A_		   = True
              A_		   = False
              return

       assert torch.cuda.is_available()

       logger.info("""Initializing GPUs"""			)
       if params.n_gpu > 1:
              assert params.local_rank != -1

              A_		   = int(os.environ["""WORLD_SIZE"""]			)
              A_		   = int(os.environ["""N_GPU_NODE"""]			)
              A_		   = int(os.environ["""RANK"""]			)

              # number of nodes / node ID
              A_		   = params.world_size // params.n_gpu_per_node
              A_		   = params.global_rank // params.n_gpu_per_node
              A_		   = True

              assert params.n_nodes == int(os.environ["""N_NODES"""]			)
              assert params.node_id == int(os.environ["""NODE_RANK"""]			)

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              A_		   = 1
              A_		   = 0
              A_		   = 0
              A_		   = 0
              A_		   = 1
              A_		   = 1
              A_		   = False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       A_		   = params.node_id == 0 and params.local_rank == 0
       A_		   = params.n_nodes > 1

       # summary
       A_		   = F'''--- Global rank: {params.global_rank} - '''
       logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes			)
       logger.info(PREFIX + """Node ID        : %i""" % params.node_id			)
       logger.info(PREFIX + """Local rank     : %i""" % params.local_rank			)
       logger.info(PREFIX + """World size     : %i""" % params.world_size			)
       logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node			)
       logger.info(PREFIX + """Master         : %s""" % str(params.is_master			)			)
       logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node			)			)
       logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu			)			)
       logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()			)

       # set GPU device
       torch.cuda.set_device(params.local_rank			)

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("""Initializing PyTorch distributed"""			)
              torch.distributed.init_process_group(
                  init_method="""env://""",    backend="""nccl""",    )







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)
