






"""simple docstring"""





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    with open(SCREAMING_SNAKE_CASE					) as metadata_file:
        UpperCamelCase__         =    json.load(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE ,	**metadata['model_config']					)

    # Load in the weights from the checkpoint_path
    UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE ,	map_location='cpu'					)

    # Load the entity vocab file
    UpperCamelCase__         =    load_entity_vocab(SCREAMING_SNAKE_CASE					)

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

    # Add special tokens to the token vocabulary for downstream tasks
    UpperCamelCase__         =    AddedToken('<ent>' ,	lstrip=SCREAMING_SNAKE_CASE ,	rstrip=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    AddedToken('<ent2>' ,	lstrip=SCREAMING_SNAKE_CASE ,	rstrip=SCREAMING_SNAKE_CASE					)
    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(SCREAMING_SNAKE_CASE					)
    with open(os.path.join(SCREAMING_SNAKE_CASE ,	LukeTokenizer.vocab_files_names['entity_vocab_file']					) ,	'w'					) as f:
        json.dump(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE					)

    # Initialize the embeddings of the special tokens
    UpperCamelCase__         =    state_dict['embeddings.word_embeddings.weight']
    UpperCamelCase__         =    word_emb[tokenizer.convert_tokens_to_ids(['@']					)[0]].unsqueeze(0					)
    UpperCamelCase__         =    word_emb[tokenizer.convert_tokens_to_ids(['#']					)[0]].unsqueeze(0					)
    UpperCamelCase__         =    torch.cat([word_emb, ent_emb, enta_emb]					)

    # 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]']]

    UpperCamelCase__         =    LukeModel(config=SCREAMING_SNAKE_CASE					).eval()

    UpperCamelCase__       ,						UpperCamelCase__         =    model.load_state_dict(SCREAMING_SNAKE_CASE ,	strict=SCREAMING_SNAKE_CASE					)
    if not (len(SCREAMING_SNAKE_CASE					) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(SCREAMING_SNAKE_CASE					)}. Expected only missing embeddings.position_ids'					)
    if not (all(key.startswith('entity_predictions'					) or key.startswith('lm_head'					) for key in unexpected_keys					)):
        raise ValueError(
            'Unexpected keys'
            F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"					) or key.startswith("lm_head"					))]					)}'					)

    # Check outputs
    UpperCamelCase__         =    LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE ,	task='entity_classification'					)

    UpperCamelCase__         =    (
        'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
        ' new world number one avoid a humiliating second- round exit at Wimbledon .'
    )
    UpperCamelCase__         =    (39, 42)
    UpperCamelCase__         =    tokenizer(SCREAMING_SNAKE_CASE ,	entity_spans=[span] ,	add_prefix_space=SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

    UpperCamelCase__         =    model(**SCREAMING_SNAKE_CASE					)

    # Verify word hidden states
    if model_size == "large":
        UpperCamelCase__         =    torch.Size((1, 42, 10_24)					)
        UpperCamelCase__         =    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]					)
    else:  # base
        UpperCamelCase__         =    torch.Size((1, 42, 7_68)					)
        UpperCamelCase__         =    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]					)

    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] ,	SCREAMING_SNAKE_CASE ,	atol=1E-4					):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        UpperCamelCase__         =    torch.Size((1, 1, 10_24)					)
        UpperCamelCase__         =    torch.tensor([[0.0466, -0.0106, -0.0179]]					)
    else:  # base
        UpperCamelCase__         =    torch.Size((1, 1, 7_68)					)
        UpperCamelCase__         =    torch.tensor([[0.1457, 0.1044, 0.0174]]					)

    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] ,	SCREAMING_SNAKE_CASE ,	atol=1E-4					):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE					)					)
    model.save_pretrained(SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    {}
    with open(SCREAMING_SNAKE_CASE ,	'r' ,	encoding='utf-8'					) as f:
        for index, line in enumerate(SCREAMING_SNAKE_CASE					):
            UpperCamelCase__       ,						UpperCamelCase__         =    line.rstrip().split('\t'					)
            UpperCamelCase__         =    index

    return entity_vocab


if __name__ == "__main__":
  A__					: Optional[int]=	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__					: Optional[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"""





from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    return DownloadCommand(args.model ,	args.cache_dir ,	args.force ,	args.trust_remote_code					)





class    __lowerCamelCase    (  _a  ):

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    parser.add_parser('download'      )
       download_parser.add_argument(
           '--cache-dir' ,				type=snake_case_ ,				default=snake_case_ ,				help='Path to location to store the models'      )
       download_parser.add_argument(
           '--force' ,				action='store_true' ,				help='Force the model to be download even if already in cache-dir'      )
       download_parser.add_argument(
           '--trust-remote-code' ,				action='store_true' ,				help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' ,				)
       download_parser.add_argument('model' ,				type=snake_case_ ,				help='Name of the model to download'      )
       download_parser.set_defaults(func=snake_case_      )

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       str:
       UpperCamelCase__         =    model
       UpperCamelCase__         =    cache
       UpperCamelCase__         =    force
       UpperCamelCase__         =    trust_remote_code







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )
       AutoTokenizer.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )




