





'''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						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Tuple				):
			# Load configuration defined in the metadata file
			with open(lowerCamelCase_				) as metadata_file:
						__lowercase										=							json.load(lowerCamelCase_				)
			__lowercase										=							LukeConfig(use_entity_aware_attention=lowerCamelCase_		,       **metadata['''model_config''']				)

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

			# Load the entity vocab file
			__lowercase										=							load_entity_vocab(lowerCamelCase_				)

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

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

			__lowercase										=							LukeTokenizer.from_pretrained(lowerCamelCase_				)

			# Initialize the embeddings of the special tokens
			__lowercase										=							state_dict['''embeddings.word_embeddings.weight''']
			__lowercase										=							word_emb[tokenizer.convert_tokens_to_ids(['''@''']				)[0]].unsqueeze(0				)
			__lowercase										=							word_emb[tokenizer.convert_tokens_to_ids(['''#''']				)[0]].unsqueeze(0				)
			__lowercase										=							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"]:
									__lowercase										=							f"encoder.layer.{layer_index}.attention.self."
									__lowercase										=							state_dict[prefix + matrix_name]
									__lowercase										=							state_dict[prefix + matrix_name]
									__lowercase										=							state_dict[prefix + matrix_name]

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

			__lowercase										=							LukeModel(config=lowerCamelCase_				).eval()

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

			__lowercase										=							(
			    '''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 .'''
			)
			__lowercase										=							(3_9, 4_2)
			__lowercase										=							tokenizer(lowerCamelCase_		,       entity_spans=[span]		,       add_prefix_space=lowerCamelCase_		,       return_tensors='''pt'''				)

			__lowercase										=							model(**lowerCamelCase_				)

			# Verify word hidden states
			if model_size == "large":
						__lowercase										=							torch.Size((1, 4_2, 1_0_2_4)				)
						__lowercase										=							torch.tensor(
						    [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]				)
			else:  # base
						__lowercase										=							torch.Size((1, 4_2, 7_6_8)				)
						__lowercase										=							torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]				)

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

			# Verify entity hidden states
			if model_size == "large":
						__lowercase										=							torch.Size((1, 1, 1_0_2_4)				)
						__lowercase										=							torch.tensor([[0.04_66, -0.01_06, -0.01_79]]				)
			else:  # base
						__lowercase										=							torch.Size((1, 1, 7_6_8)				)
						__lowercase										=							torch.tensor([[0.14_57, 0.10_44, 0.01_74]]				)

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

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any				):
			__lowercase										=							{}
			with open(lowerCamelCase_		,       '''r'''		,       encoding='''utf-8'''				) as f:
						for index, line in enumerate(lowerCamelCase_				):
									__lowercase						,		__lowercase										=							line.rstrip().split('''\t'''				)
									__lowercase										=							index

			return entity_vocab


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    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.'''
	)
	_SCREAMING_SNAKE_CASE					=    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'''



import math




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			assert isinstance(lowerCamelCase_		,       lowerCamelCase_				) and (
			    number >= 0
			), "'number' must been an int and positive"

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or not number % 2:
						# Negatives, 0, 1 and all even numbers are not primes
						return False

			__lowercase										=							range(3		,       int(math.sqrt(lowerCamelCase_				) + 1				)		,       2				)
			return not any(not number % i for i in odd_numbers				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Any=1		,       **lowerCamelCase_			:  Tuple				):
			__lowercase										=							factor * value
			__lowercase										=							value

			while not is_prime(lowerCamelCase_				):
						value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

			if value == first_value_val:
						return next_prime(value + 1		,       **lowerCamelCase_				)
			return value





