



'''simple docstring'''




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 				lowerCAmelCase_   (	_lowerCamelCase:   Any       ,      _lowerCamelCase:   Dict       ,      _lowerCamelCase:   Optional[Any]       ,      _lowerCamelCase:   Optional[int]       ,      _lowerCamelCase:   Any					):
		# Load configuration defined in the metadata file
		with open(_lowerCamelCase					) as metadata_file:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		json.load(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		LukeConfig(use_entity_aware_attention=_lowerCamelCase       ,      **metadata["""model_config"""]					)

		# Load in the weights from the checkpoint_path
		__SCREAMING_SNAKE_CASE       :		Dict           =		torch.load(_lowerCamelCase       ,      map_location="""cpu"""					)["""module"""]

		# Load the entity vocab file
		__SCREAMING_SNAKE_CASE       :		str           =		load_original_entity_vocab(_lowerCamelCase					)
		# add an entry for [MASK2]
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		max(entity_vocab.values()					) + 1
		config.entity_vocab_size += 1

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]					)

		# Add special tokens to the token vocabulary for downstream tasks
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		AddedToken("""<ent>"""       ,      lstrip=_lowerCamelCase       ,      rstrip=_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		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       ,      """tokenizer_config.json"""					)       ,      """r"""					) as f:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		json.load(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		str           =		"""MLukeTokenizer"""
		with open(os.path.join(_lowerCamelCase       ,      """tokenizer_config.json"""					)       ,      """w"""					) as f:
				json.dump(_lowerCamelCase       ,      _lowerCamelCase					)

		with open(os.path.join(_lowerCamelCase       ,      MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]					)       ,      """w"""					) as f:
				json.dump(_lowerCamelCase       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		MLukeTokenizer.from_pretrained(_lowerCamelCase					)

		# Initialize the embeddings of the special tokens
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tokenizer.convert_tokens_to_ids(["""@"""]					)[0]
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tokenizer.convert_tokens_to_ids(["""#"""]					)[0]

		__SCREAMING_SNAKE_CASE       :		List[str]           =		state_dict["""embeddings.word_embeddings.weight"""]
		__SCREAMING_SNAKE_CASE       :		Dict           =		word_emb[ent_init_index].unsqueeze(0					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		word_emb[enta_init_index].unsqueeze(0					)
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.cat([word_emb, ent_emb, enta_emb]					)
		# add special tokens for 'entity_predictions.bias'
		for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		state_dict[bias_name]
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		decoder_bias[ent_init_index].unsqueeze(0					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		decoder_bias[enta_init_index].unsqueeze(0					)
				__SCREAMING_SNAKE_CASE       :		Any           =		torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]					)

		# 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"]:
						__SCREAMING_SNAKE_CASE       :		int           =		F"encoder.layer.{layer_index}.attention.self."
						__SCREAMING_SNAKE_CASE       :		int           =		state_dict[prefix + matrix_name]
						__SCREAMING_SNAKE_CASE       :		List[str]           =		state_dict[prefix + matrix_name]
						__SCREAMING_SNAKE_CASE       :		List[str]           =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		state_dict["""entity_embeddings.entity_embeddings.weight"""]
		__SCREAMING_SNAKE_CASE       :		Tuple           =		entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		torch.cat([entity_emb, entity_mask_emb]					)
		# add [MASK2] for 'entity_predictions.bias'
		__SCREAMING_SNAKE_CASE       :		int           =		state_dict["""entity_predictions.bias"""]
		__SCREAMING_SNAKE_CASE       :		str           =		entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0					)
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.cat([entity_prediction_bias, entity_mask_bias]					)

		__SCREAMING_SNAKE_CASE       :		List[str]           =		LukeForMaskedLM(config=_lowerCamelCase					).eval()

		state_dict.pop("""entity_predictions.decoder.weight"""					)
		state_dict.pop("""lm_head.decoder.weight"""					)
		state_dict.pop("""lm_head.decoder.bias"""					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith("""lm_head"""					) or key.startswith("""entity_predictions"""					)):
						__SCREAMING_SNAKE_CASE       :		int           =		state_dict[key]
				else:
						__SCREAMING_SNAKE_CASE       :		Tuple           =		state_dict[key]

		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model.load_state_dict(_lowerCamelCase       ,      strict=_lowerCamelCase					)

		if set(_lowerCamelCase					) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"					)
		if set(_lowerCamelCase					) != {
		    "lm_head.decoder.weight",
		    "lm_head.decoder.bias",
		    "entity_predictions.decoder.weight",
		}:
				raise ValueError(F"Unexpected missing_keys: {missing_keys}"					)

		model.tie_weights()
		assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
		assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

		# Check outputs
		__SCREAMING_SNAKE_CASE       :		Dict           =		MLukeTokenizer.from_pretrained(_lowerCamelCase       ,      task="""entity_classification"""					)

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
		__SCREAMING_SNAKE_CASE       :		Tuple           =		(0, 9)
		__SCREAMING_SNAKE_CASE       :		Any           =		tokenizer(_lowerCamelCase       ,      entity_spans=[span]       ,      return_tensors="""pt"""					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model(**_lowerCamelCase					)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch.Size((1, 33, 7_68)					)
				__SCREAMING_SNAKE_CASE       :		Dict           =		torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]					)

		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":
				raise NotImplementedError
		else:  # base
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch.Size((1, 1, 7_68)					)
				__SCREAMING_SNAKE_CASE       :		Any           =		torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]					)

		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

		# Verify masked word/entity prediction
		__SCREAMING_SNAKE_CASE       :		List[str]           =		MLukeTokenizer.from_pretrained(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		"""Tokyo is the capital of <mask>."""
		__SCREAMING_SNAKE_CASE       :		str           =		(24, 30)
		__SCREAMING_SNAKE_CASE       :		str           =		tokenizer(_lowerCamelCase       ,      entity_spans=[span]       ,      return_tensors="""pt"""					)

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		model(**_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		encoding["""input_ids"""][0].tolist()
		__SCREAMING_SNAKE_CASE       :		Dict           =		input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""					)					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		outputs.logits[0][mask_position_id].argmax(dim=-1					)
		assert "Japan" == tokenizer.decode(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		outputs.entity_logits[0][0].argmax().item()
		__SCREAMING_SNAKE_CASE       :		Any           =		[
		    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
		]
		assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""					)][0] == "en:Japan"

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


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]					):
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		["""[MASK]""", """[PAD]""", """[UNK]"""]

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[json.loads(_lowerCamelCase					) for line in open(_lowerCamelCase					)]

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		{}
		for entry in data:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		entry["""id"""]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		entity_id
								break
						__SCREAMING_SNAKE_CASE       :		str           =		F"{language}:{entity_name}"
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		entity_id
		return new_mapping


if __name__ == "__main__":
						UpperCamelCase__       :	Optional[int]    = 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__       :	Optional[int]    = 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 string


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		""""""
		for i in sequence:
				__SCREAMING_SNAKE_CASE       :		Any           =		ord(_lowerCamelCase					)
				if 65 <= extract <= 90:
						output += chr(1_55 - extract					)
				elif 97 <= extract <= 1_22:
						output += chr(2_19 - extract					)
				else:
						output += i
		return output


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		string.ascii_letters
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(_lowerCamelCase					)] if c in letters else c for c in sequence					)


def 				lowerCAmelCase_   (	):
		from timeit import timeit

		print("""Running performance benchmarks..."""					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""from string import printable ; from __main__ import atbash, atbash_slow"""
		print(F"> atbash_slow(): {timeit('atbash_slow(printable)'       ,      setup=_lowerCamelCase					)} seconds"					)
		print(F">      atbash(): {timeit('atbash(printable)'       ,      setup=_lowerCamelCase					)} seconds"					)


if __name__ == "__main__":
						for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
												print(f"{example} encrypted in atbash: {atbash(example)}")
						benchmark()