






from ..utils import DummyObject, requires_backends







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Dict           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Tuple           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Any:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Union[str, Any]           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Dict           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      List[Any]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      List[Any]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					List[str]           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Dict           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Dict:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Dict           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      List[str]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Any:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					List[str]           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Any:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					str           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					int           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Dict:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					int           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Any           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Any:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Dict:
          requires_backends(cls    ,				['''flax'''] )







class 			UpperCAmelCase_						(					metaclass=lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					int           =['flax']
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          requires_backends(self    ,				['''flax'''] )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          requires_backends(cls    ,				['''flax'''] )




      @classmethod
      def     UpperCAmelCase						(				cls    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Any:
          requires_backends(cls    ,				['''flax'''] )










"""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_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[Any]							):
    # Load configuration defined in the metadata file
    with open(UpperCamelCase__							) as metadata_file:
        _UpperCAmelCase							:  Dict											=    json.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    LukeConfig(use_entity_aware_attention=UpperCamelCase__     ,       **metadata['''model_config''']							)

    # Load in the weights from the checkpoint_path
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    # Load the entity vocab file
    _UpperCAmelCase							:  Optional[int]											=    load_entity_vocab(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

    # Add special tokens to the token vocabulary for downstream tasks
    _UpperCAmelCase							:  int											=    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__     ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)     ,       '''w'''							) as f:
        json.dump(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Any											=    LukeTokenizer.from_pretrained(UpperCamelCase__							)

    # Initialize the embeddings of the special tokens
    _UpperCAmelCase							:  str											=    state_dict['''embeddings.word_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Union[str, Any]											=    word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Tuple											=    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							:  List[Any]											=    F'encoder.layer.{layer_index}.attention.self.'
            _UpperCAmelCase							:  Optional[Any]											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  Tuple											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  str											=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _UpperCAmelCase							:  Any											=    state_dict['''entity_embeddings.entity_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    entity_emb[entity_vocab['''[MASK]''']]

    _UpperCAmelCase							:  Optional[int]											=    LukeModel(config=UpperCamelCase__							).eval()

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if not (len(UpperCamelCase__							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(UpperCamelCase__							)}. 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							:  Optional[int]											=    LukeTokenizer.from_pretrained(UpperCamelCase__     ,       task='''entity_classification'''							)

    _UpperCAmelCase							:  List[str]											=    (
        '''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							:  Dict											=    (39, 42)
    _UpperCAmelCase							:  Any											=    tokenizer(UpperCamelCase__     ,       entity_spans=[span]     ,       add_prefix_space=UpperCamelCase__     ,       return_tensors='''pt'''							)

    _UpperCAmelCase							:  List[Any]											=    model(**UpperCamelCase__							)

    # Verify word hidden states
    if model_size == "large":
        _UpperCAmelCase							:  str											=    torch.Size((1, 42, 1024)							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
    else:  # base
        _UpperCAmelCase							:  Optional[Any]											=    torch.Size((1, 42, 768)							)
        _UpperCAmelCase							:  str											=    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]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _UpperCAmelCase							:  int											=    torch.Size((1, 1, 1024)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0466, -0.0106, -0.0179]]							)
    else:  # base
        _UpperCAmelCase							:  List[str]											=    torch.Size((1, 1, 768)							)
        _UpperCAmelCase							:  List[Any]											=    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]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

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





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Any											=    {}
    with open(UpperCamelCase__     ,       '''r'''     ,       encoding='''utf-8'''							) as f:
        for index, line in enumerate(UpperCamelCase__							):
            _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    line.rstrip().split('''\t'''							)
            _UpperCAmelCase							:  Tuple											=    index

    return entity_vocab


if __name__ == "__main__":
  _lowerCAmelCase    :List[Any]     =					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    :Any     =					parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )


