"""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 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):






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

		# Load in the weights from the checkpoint_path
		UpperCAmelCase__    =							torch.load(a_      ,    map_location='cpu' )['''module''']

		# Load the entity vocab file
		UpperCAmelCase__    =							load_original_entity_vocab(a_ )
		# add an entry for [MASK2]
		UpperCAmelCase__    =							max(entity_vocab.values() ) + 1
		config.entity_vocab_size += 1

		UpperCAmelCase__    =							XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )

		# Add special tokens to the token vocabulary for downstream tasks
		UpperCAmelCase__    =							AddedToken('<ent>'      ,    lstrip=a_      ,    rstrip=a_ )
		UpperCAmelCase__    =							AddedToken('<ent2>'      ,    lstrip=a_      ,    rstrip=a_ )
		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(a_ )
		with open(os.path.join(a_      ,    'tokenizer_config.json' )      ,    'r' ) as f:
				UpperCAmelCase__    =							json.load(a_ )
		UpperCAmelCase__    =							'''MLukeTokenizer'''
		with open(os.path.join(a_      ,    'tokenizer_config.json' )      ,    'w' ) as f:
				json.dump(a_      ,    a_ )

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

		UpperCAmelCase__    =							MLukeTokenizer.from_pretrained(a_ )

		# Initialize the embeddings of the special tokens
		UpperCAmelCase__    =							tokenizer.convert_tokens_to_ids(['@'] )[0]
		UpperCAmelCase__    =							tokenizer.convert_tokens_to_ids(['#'] )[0]

		UpperCAmelCase__    =							state_dict['''embeddings.word_embeddings.weight''']
		UpperCAmelCase__    =							word_emb[ent_init_index].unsqueeze(0 )
		UpperCAmelCase__    =							word_emb[enta_init_index].unsqueeze(0 )
		UpperCAmelCase__    =							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"]:
				UpperCAmelCase__    =							state_dict[bias_name]
				UpperCAmelCase__    =							decoder_bias[ent_init_index].unsqueeze(0 )
				UpperCAmelCase__    =							decoder_bias[enta_init_index].unsqueeze(0 )
				UpperCAmelCase__    =							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"]:
						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]''']].unsqueeze(0 )
		UpperCAmelCase__    =							torch.cat([entity_emb, entity_mask_emb] )
		# add [MASK2] for 'entity_predictions.bias'
		UpperCAmelCase__    =							state_dict['''entity_predictions.bias''']
		UpperCAmelCase__    =							entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
		UpperCAmelCase__    =							torch.cat([entity_prediction_bias, entity_mask_bias] )

		UpperCAmelCase__    =							LukeForMaskedLM(config=a_ ).eval()

		state_dict.pop('entity_predictions.decoder.weight' )
		state_dict.pop('lm_head.decoder.weight' )
		state_dict.pop('lm_head.decoder.bias' )
		UpperCAmelCase__    =							OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
						UpperCAmelCase__    =							state_dict[key]
				else:
						UpperCAmelCase__    =							state_dict[key]

		UpperCAmelCase__    =							model.load_state_dict(a_      ,    strict=a_ )

		if set(a_ ) != {"luke.embeddings.position_ids"}:
				raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}''' )
		if set(a_ ) != {
		    "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
		UpperCAmelCase__    =							MLukeTokenizer.from_pretrained(a_      ,    task='entity_classification' )

		UpperCAmelCase__    =							'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
		UpperCAmelCase__    =							(0, 9)
		UpperCAmelCase__    =							tokenizer(a_      ,    entity_spans=[span]      ,    return_tensors='pt' )

		UpperCAmelCase__    =							model(**a_ )

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				UpperCAmelCase__    =							torch.Size((1, 3_3, 7_6_8) )
				UpperCAmelCase__    =							torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )

		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]      ,    a_      ,    atol=1e-4 ):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				UpperCAmelCase__    =							torch.Size((1, 1, 7_6_8) )
				UpperCAmelCase__    =							torch.tensor([[-0.1482, 0.0609, 0.0322]] )

		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]      ,    a_      ,    atol=1e-4 ):
				raise ValueError

		# Verify masked word/entity prediction
		UpperCAmelCase__    =							MLukeTokenizer.from_pretrained(a_ )
		UpperCAmelCase__    =							'''Tokyo is the capital of <mask>.'''
		UpperCAmelCase__    =							(2_4, 3_0)
		UpperCAmelCase__    =							tokenizer(a_      ,    entity_spans=[span]      ,    return_tensors='pt' )

		UpperCAmelCase__    =							model(**a_ )

		UpperCAmelCase__    =							encoding['''input_ids'''][0].tolist()
		UpperCAmelCase__    =							input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
		UpperCAmelCase__    =							outputs.logits[0][mask_position_id].argmax(dim=-1 )
		assert "Japan" == tokenizer.decode(a_ )

		UpperCAmelCase__    =							outputs.entity_logits[0][0].argmax().item()
		UpperCAmelCase__    =							[
		    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(a_ ) )
		model.save_pretrained(a_ )



def 						a_		(      lowerCamelCase ):
		UpperCAmelCase__    =							['''[MASK]''', '''[PAD]''', '''[UNK]''']

		UpperCAmelCase__    =							[json.loads(a_ ) for line in open(a_ )]

		UpperCAmelCase__    =							{}
		for entry in data:
				UpperCAmelCase__    =							entry['''id''']
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								UpperCAmelCase__    =							entity_id
								break
						UpperCAmelCase__    =							f'''{language}:{entity_name}'''
						UpperCAmelCase__    =							entity_id
		return new_mapping


if __name__ == "__main__":
						lowerCAmelCase__						:						Dict		  =			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__						:						List[Any]		  =			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"""






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCAmelCase__						:						str		  =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__						:						list[int]		  =			[ord(letter) for letter in string.ascii_lowercase]
lowerCAmelCase__						:						set[int]		  =			{ord(char) for char in VALID_CHARS}

lowerCAmelCase__						:						list[str]		  =			["the", "be", "to", "of", "and", "in", "that", "have"]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							""
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42

				for keychar, cipherchar in zip(cycle(lowerCamelCase )      ,    lowerCamelCase ):
								UpperCAmelCase__    =							cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(lowerCamelCase )

				return decoded



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for key in product(lowerCamelCase      ,    repeat=3 ):
								UpperCAmelCase__    =							try_key(lowerCamelCase      ,    lowerCamelCase )
								if encoded is not None:
												possibles.append(lowerCamelCase )
				return possibles



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return [possible for possible in possibles if common_word in possible.lower()]



def 						a_		(      lowerCamelCase = "p059_cipher.txt" ):
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							Path(lowerCamelCase ).parent.joinpath(lowerCamelCase ).read_text(encoding='utf-8' )

				UpperCAmelCase__    =							[int(lowerCamelCase ) for number in data.strip().split(',' )]

				UpperCAmelCase__    =							filter_valid_chars(lowerCamelCase )
				for common_word in COMMON_WORDS:
								UpperCAmelCase__    =							filter_common_word(lowerCamelCase      ,    lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												break

				UpperCAmelCase__    =							possibles[0]
				return sum(ord(lowerCamelCase ) for char in decoded_text )


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



