




"""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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				# Load configuration defined in the metadata file
				with open(__snake_case	) as metadata_file:
								_lowerCamelCase  :    int					=					json.load(__snake_case	)
				_lowerCamelCase  :    Optional[Any]					=					LukeConfig(use_entity_aware_attention=__snake_case							,					**metadata['model_config']	)

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

				# Load the entity vocab file
				_lowerCamelCase  :    Any					=					load_entity_vocab(__snake_case	)

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

				# Add special tokens to the token vocabulary for downstream tasks
				_lowerCamelCase  :    Union[str, Any]					=					AddedToken('<ent>'							,					lstrip=__snake_case							,					rstrip=__snake_case	)
				_lowerCamelCase  :    Any					=					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	)

				_lowerCamelCase  :    Dict					=					LukeTokenizer.from_pretrained(__snake_case	)

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

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

				_lowerCamelCase  :    int					=					LukeModel(config=__snake_case	).eval()

				_lowerCamelCase,				_lowerCamelCase  :    Any					=					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
				_lowerCamelCase  :    int					=					LukeTokenizer.from_pretrained(__snake_case							,					task='entity_classification'	)

				_lowerCamelCase  :    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 .'
				)
				_lowerCamelCase  :    List[Any]					=					(39, 42)
				_lowerCamelCase  :    Optional[int]					=					tokenizer(__snake_case							,					entity_spans=[span]							,					add_prefix_space=__snake_case							,					return_tensors='pt'	)

				_lowerCamelCase  :    List[str]					=					model(**__snake_case	)

				# Verify word hidden states
				if model_size == "large":
								_lowerCamelCase  :    Dict					=					torch.Size((1, 42, 1024)	)
								_lowerCamelCase  :    Any					=					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
								_lowerCamelCase  :    List[str]					=					torch.Size((1, 42, 768)	)
								_lowerCamelCase  :    str					=					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]							,					__snake_case							,					atol=1E-4	):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_lowerCamelCase  :    Dict					=					torch.Size((1, 1, 1024)	)
								_lowerCamelCase  :    Any					=					torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]	)
				else:  # base
								_lowerCamelCase  :    List[Any]					=					torch.Size((1, 1, 768)	)
								_lowerCamelCase  :    int					=					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]							,					__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   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					{}
				with open(__snake_case							,					'r'							,					encoding='utf-8'	) as f:
								for index, line in enumerate(__snake_case	):
												_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					line.rstrip().split('\t'	)
												_lowerCamelCase  :    Optional[Any]					=					index

				return entity_vocab


if __name__ == "__main__":
			lowercase__  		=		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."""
			)
			lowercase__  		=		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   _snake_case    (       lowercase__ = 1000	):
				_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					1, 1
				_lowerCamelCase  :    int					=					[]
				for i in range(1							,					n + 1	):
								_lowerCamelCase  :    Tuple					=					prev_numerator + 2 * prev_denominator
								_lowerCamelCase  :    str					=					prev_numerator + prev_denominator
								if len(str(lowercase__	)	) > len(str(lowercase__	)	):
												result.append(lowercase__	)
								_lowerCamelCase  :    Dict					=					numerator
								_lowerCamelCase  :    Optional[int]					=					denominator

				return len(lowercase__	)


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