


'''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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	List[Any]:




    with open(_UpperCamelCase						) as metadata_file:
        UpperCAmelCase__			:		int													=				json.load(_UpperCamelCase						)
    UpperCAmelCase__			:		Optional[Any]													=				LukeConfig(use_entity_aware_attention=_UpperCamelCase  ,					**metadata['''model_config''']						)

    # Load in the weights from the checkpoint_path
    UpperCAmelCase__			:		str													=				torch.load(_UpperCamelCase  ,					map_location='''cpu'''						)

    # Load the entity vocab file
    UpperCAmelCase__			:		Union[str, Any]													=				load_entity_vocab(_UpperCamelCase						)

    UpperCAmelCase__			:		List[Any]													=				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']						)

    # Add special tokens to the token vocabulary for downstream tasks
    UpperCAmelCase__			:		Union[str, Any]													=				AddedToken('''<ent>'''  ,					lstrip=_UpperCamelCase  ,					rstrip=_UpperCamelCase						)
    UpperCAmelCase__			:		Union[str, 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__			:		List[Any]													=				LukeTokenizer.from_pretrained(_UpperCamelCase						)

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

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

    UpperCAmelCase__			:		Tuple													=				LukeModel(config=_UpperCamelCase						).eval()

    UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				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__			:		Dict													=				LukeTokenizer.from_pretrained(_UpperCamelCase  ,					task='''entity_classification'''						)

    UpperCAmelCase__			:		Union[str, Any]													=				(
        '''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__			:		str													=				(39, 42)
    UpperCAmelCase__			:		Dict													=				tokenizer(_UpperCamelCase  ,					entity_spans=[span]  ,					add_prefix_space=_UpperCamelCase  ,					return_tensors='''pt'''						)

    UpperCAmelCase__			:		Dict													=				model(**_UpperCamelCase						)

    # Verify word hidden states
    if model_size == "large":
        UpperCAmelCase__			:		Tuple													=				torch.Size((1, 42, 10_24)						)
        UpperCAmelCase__			:		Tuple													=				torch.tensor(
            [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]						)
    else:  # base
        UpperCAmelCase__			:		int													=				torch.Size((1, 42, 7_68)						)
        UpperCAmelCase__			:		Any													=				torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]						)

    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__			:		List[str]													=				torch.Size((1, 1, 10_24)						)
        UpperCAmelCase__			:		Union[str, Any]													=				torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]						)
    else:  # base
        UpperCAmelCase__			:		Any													=				torch.Size((1, 1, 7_68)						)
        UpperCAmelCase__			:		List[str]													=				torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]						)

    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 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		List[Any]													=				{}
    with open(_UpperCamelCase  ,					'''r'''  ,					encoding='''utf-8'''						) as f:
        for index, line in enumerate(_UpperCamelCase						):
            UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				line.rstrip().split('''\t'''						)
            UpperCAmelCase__			:		Dict													=				index

    return entity_vocab


if __name__ == "__main__":
  UpperCamelCase__    					=      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__    					=      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"""


def _lowerCamelCase	(		_UpperCamelCase = 6008_5147_5143      ):

			'''simple docstring'''




			try:
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."      )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."      )
			__lowerCAmelCase				    =			2
			__lowerCAmelCase				    =			0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__lowerCAmelCase				    =			i
						while n % i == 0:
									__lowerCAmelCase				    =			n // i
						i += 1
			return int(_UpperCamelCase      )


if __name__ == "__main__":
	print(f'''{solution() = }''')




