






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     a_   (__A				,       __A				,       __A				,       __A				,       __A      )				->						Optional[Any]:



					"""simple docstring"""
					# Load configuration defined in the metadata file
					with open(__A      ) as metadata_file:
										__a					:						Optional[int]      =					json.load(__A      )
					__a					:						Dict      =					LukeConfig(use_entity_aware_attention=__A				,       **metadata["model_config"]      )

					# Load in the weights from the checkpoint_path
					__a					:						str      =					torch.load(__A				,       map_location="cpu"      )

					# Load the entity vocab file
					__a					:						str      =					load_entity_vocab(__A      )

					__a					:						List[Any]      =					RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

					# Add special tokens to the token vocabulary for downstream tasks
					__a					:						Optional[int]      =					AddedToken("<ent>"				,       lstrip=__A				,       rstrip=__A      )
					__a					:						Dict      =					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					:						Any      =					LukeTokenizer.from_pretrained(__A      )

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					__a					:						int      =					state_dict["entity_embeddings.entity_embeddings.weight"]
					__a					:						Any      =					entity_emb[entity_vocab["[MASK]"]]

					__a					:						Any      =					LukeModel(config=__A      ).eval()

					__a      ,			__a					:						Tuple      =					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					:						Optional[int]      =					LukeTokenizer.from_pretrained(__A				,       task="entity_classification"      )

					__a					:						Dict      =					(
					    "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					:						Optional[int]      =					(39, 42)
					__a					:						List[str]      =					tokenizer(__A				,       entity_spans=[span]				,       add_prefix_space=__A				,       return_tensors="pt"      )

					__a					:						Optional[int]      =					model(**__A      )

					# Verify word hidden states
					if model_size == "large":
										__a					:						Union[str, Any]      =					torch.Size((1, 42, 1_024)      )
										__a					:						Dict      =					torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
					else:  # base
										__a					:						Any      =					torch.Size((1, 42, 768)      )
										__a					:						str      =					torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]      )

					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					:						Optional[int]      =					torch.Size((1, 1, 1_024)      )
										__a					:						List[Any]      =					torch.tensor([[0.0466, -0.0106, -0.0179]]      )
					else:  # base
										__a					:						Any      =					torch.Size((1, 1, 768)      )
										__a					:						Optional[int]      =					torch.tensor([[0.1457, 0.1044, 0.0174]]      )

					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     a_   (__A      )				->						Any:



					"""simple docstring"""
					__a					:						Any      =					{}
					with open(__A				,       "r"				,       encoding="utf-8"      ) as f:
										for index, line in enumerate(__A      ):
															__a      ,			__a					:						Any      =					line.rstrip().split("\t"      )
															__a					:						Dict      =					index

					return entity_vocab


if __name__ == "__main__":
			UpperCAmelCase__      			=						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__      			=						parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)













UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					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					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





