


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

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

							# Load the entity vocab file
							a__      =load_entity_vocab(__a     )

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

							# Add special tokens to the token vocabulary for downstream tasks
							a__      =AddedToken('<ent>'    ,     lstrip=__a    ,     rstrip=__a     )
							a__      =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    ,     LukeTokenizer.vocab_files_names['entity_vocab_file']     )    ,     'w'     ) as f:
														json.dump(__a    ,     __a     )

							a__      =LukeTokenizer.from_pretrained(__a     )

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

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

							a__      =LukeModel(config=__a     ).eval()

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

							a__      =(
							    '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 .'
							)
							a__      =(39, 42)
							a__      =tokenizer(__a    ,     entity_spans=[span]    ,     add_prefix_space=__a    ,     return_tensors='pt'     )

							a__      =model(**__a     )

							# Verify word hidden states
							if model_size == "large":
														a__      =torch.Size((1, 42, 1024)     )
														a__      =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
														a__      =torch.Size((1, 42, 768)     )
														a__      =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]    ,     __a    ,     atol=1e-4     ):
														raise ValueError

							# Verify entity hidden states
							if model_size == "large":
														a__      =torch.Size((1, 1, 1024)     )
														a__      =torch.tensor([[0.04_66, -0.01_06, -0.01_79]]     )
							else:  # base
														a__      =torch.Size((1, 1, 768)     )
														a__      =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]    ,     __a    ,     atol=1e-4     ):
														raise ValueError

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






def   _lowercase(					__a				:						int     ):
							a__      ={}
							with open(__a    ,     'r'    ,     encoding='utf-8'     ) as f:
														for index, line in enumerate(__a     ):
																					a__    ,     a__      =line.rstrip().split('\t'     )
																					a__      =index

							return entity_vocab


if __name__ == "__main__":
	_lowerCAmelCase:						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.'
	)
	_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,
	)





_lowerCAmelCase:						List[str]   				=       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def   _lowercase(					):
							a__      =input('Enter message: '     )
							a__      =input('Enter key [alphanumeric]: '     )
							a__      =input('Encrypt/Decrypt [e/d]: '     )

							if mode.lower().startswith('e'     ):
														a__      ='encrypt'
														a__      =encrypt_message(__a    ,     __a     )
							elif mode.lower().startswith('d'     ):
														a__      ='decrypt'
														a__      =decrypt_message(__a    ,     __a     )

							print(f"""\n{mode.title()}ed message:"""     )
							print(__a     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'encrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'decrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						str     ):
							a__      =[]
							a__      =0
							a__      =key.upper()

							for symbol in message:
														a__      =LETTERS.find(symbol.upper()     )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]     )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]     )

																					num %= len(__a     )

																					if symbol.isupper():
																												translated.append(LETTERS[num]     )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()     )

																					key_index += 1
																					if key_index == len(__a     ):
																												a__      =0
														else:
																					translated.append(__a     )
							return "".join(__a     )


if __name__ == "__main__":
	main()

