


'''simple docstring'''






from bisect import bisect
from itertools import accumulate







def 				UpperCamelCase_			(    A__					:   Any							,    A__					:   int							,    A__					:   List[Any]							,    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      Union[str, Any]			=   sorted(zip(__snake_case							,    __snake_case					)							,    key=lambda A__					: x[0] / x[1]							,    reverse=__snake_case					)
  lowerCAmelCase_,							lowerCAmelCase_			:      List[Any]			=   [i[0] for i in r], [i[1] for i in r]
  lowerCAmelCase_			:      Tuple			=   list(accumulate(__snake_case					)					)
  lowerCAmelCase_			:      List[str]			=   bisect(__snake_case							,    __snake_case					)
  return (
      0
      if k == 0
      else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]					)
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





"""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__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





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

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

					# Load the entity vocab file
					_UpperCamelCase							 =  load_entity_vocab(__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=__snake_case,     rstrip=__snake_case	)
					_UpperCamelCase							 =  AddedToken('''<ent2>''',     lstrip=__snake_case,     rstrip=__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(__snake_case	)
					with open(os.path.join(__snake_case,     LukeTokenizer.vocab_files_names['''entity_vocab_file''']	),     '''w'''	) as f:
										json.dump(__snake_case,     __snake_case	)

					_UpperCamelCase							 =  LukeTokenizer.from_pretrained(__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=__snake_case	).eval()

					_UpperCamelCase ,  _UpperCamelCase							 =  model.load_state_dict(__snake_case,     strict=__snake_case	)
					if not (len(__snake_case	) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(F'''Missing keys {", ".join(__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(__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(__snake_case,     entity_spans=[span],     add_prefix_space=__snake_case,     return_tensors='''pt'''	)

					_UpperCamelCase							 =  model(**__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],     __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],     __snake_case,     atol=1e-4	):
										raise ValueError

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



def       lowerCamelCase__		(			__snake_case	)							->		Union[str, Any]:


					"""simple docstring"""





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

					return entity_vocab


if __name__ == "__main__":
				_a														=		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														=		parser.parse_args()
				convert_luke_checkpoint(
				    args.checkpoint_path,
				    args.metadata_path,
				    args.entity_vocab_path,
				    args.pytorch_dump_folder_path,
				    args.model_size,
				)






