




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 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : Dict      ,    UpperCamelCase_ : Tuple       ):
				# Load configuration defined in the metadata file
				with open(UpperCamelCase_       ) as metadata_file:
								_lowerCAmelCase    :    List[str]										=      json.load(UpperCamelCase_       )
				_lowerCAmelCase    :    int										=      LukeConfig(use_entity_aware_attention=UpperCamelCase_      ,    **metadata["""model_config"""]       )

				# Load in the weights from the checkpoint_path
				_lowerCAmelCase    :    Optional[int]										=      torch.load(UpperCamelCase_      ,    map_location="""cpu"""       )

				# Load the entity vocab file
				_lowerCAmelCase    :    List[Any]										=      load_entity_vocab(UpperCamelCase_       )

				_lowerCAmelCase    :    Tuple										=      RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]       )

				# Add special tokens to the token vocabulary for downstream tasks
				_lowerCAmelCase    :    Tuple										=      AddedToken("""<ent>"""      ,    lstrip=UpperCamelCase_      ,    rstrip=UpperCamelCase_       )
				_lowerCAmelCase    :    List[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_       )

				_lowerCAmelCase    :    Dict										=      LukeTokenizer.from_pretrained(UpperCamelCase_       )

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				_lowerCAmelCase    :    Union[str, Any]										=      state_dict["""entity_embeddings.entity_embeddings.weight"""]
				_lowerCAmelCase    :    str										=      entity_emb[entity_vocab["""[MASK]"""]]

				_lowerCAmelCase    :    List[str]										=      LukeModel(config=UpperCamelCase_       ).eval()

				_lowerCAmelCase    :    str										=      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
				_lowerCAmelCase    :    Any										=      LukeTokenizer.from_pretrained(UpperCamelCase_      ,    task="""entity_classification"""       )

				_lowerCAmelCase    :    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 ."""
				)
				_lowerCAmelCase    :    int										=      (39, 42)
				_lowerCAmelCase    :    Tuple										=      tokenizer(UpperCamelCase_      ,    entity_spans=[span]      ,    add_prefix_space=UpperCamelCase_      ,    return_tensors="""pt"""       )

				_lowerCAmelCase    :    Dict										=      model(**UpperCamelCase_       )

				# Verify word hidden states
				if model_size == "large":
								_lowerCAmelCase    :    Any										=      torch.Size((1, 42, 1024)       )
								_lowerCAmelCase    :    Any										=      torch.tensor(
								    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]       )
				else:  # base
								_lowerCAmelCase    :    Optional[Any]										=      torch.Size((1, 42, 768)       )
								_lowerCAmelCase    :    Tuple										=      torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]       )

				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":
								_lowerCAmelCase    :    Optional[Any]										=      torch.Size((1, 1, 1024)       )
								_lowerCAmelCase    :    Any										=      torch.tensor([[0.0_466, -0.0_106, -0.0_179]]       )
				else:  # base
								_lowerCAmelCase    :    int										=      torch.Size((1, 1, 768)       )
								_lowerCAmelCase    :    List[Any]										=      torch.tensor([[0.1_457, 0.1_044, 0.0_174]]       )

				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 							_UpperCAmelCase  (UpperCamelCase_ : Tuple       ):
				_lowerCAmelCase    :    int										=      {}
				with open(UpperCamelCase_      ,    """r"""      ,    encoding="""utf-8"""       ) as f:
								for index, line in enumerate(UpperCamelCase_       ):
												_lowerCAmelCase    :    Optional[Any]										=      line.rstrip().split("""\t"""       )
												_lowerCAmelCase    :    Union[str, Any]										=      index

				return entity_vocab


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







from math import loga




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				if a < 0:
								raise ValueError("""Input value must be a positive integer"""       )
				elif isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								raise TypeError("""Input value must be a 'int' type"""       )
				return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

