






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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# Load configuration defined in the metadata file
			with open(_UpperCAmelCase) as metadata_file:
						SCREAMING_SNAKE_CASE						= json.load(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= LukeConfig(use_entity_aware_attention=_UpperCAmelCase							,       **metadata['model_config'])

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

			# Load the entity vocab file
			SCREAMING_SNAKE_CASE						= load_entity_vocab(_UpperCAmelCase)

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

			# Add special tokens to the token vocabulary for downstream tasks
			SCREAMING_SNAKE_CASE						= AddedToken('<ent>'							,       lstrip=_UpperCAmelCase							,       rstrip=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= AddedToken('<ent2>'							,       lstrip=_UpperCAmelCase							,       rstrip=_UpperCAmelCase)
			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(_UpperCAmelCase)
			with open(os.path.join(_UpperCAmelCase							,       LukeTokenizer.vocab_files_names['entity_vocab_file'])							,       'w') as f:
						json.dump(_UpperCAmelCase							,       _UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= LukeTokenizer.from_pretrained(_UpperCAmelCase)

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

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

			SCREAMING_SNAKE_CASE						= LukeModel(config=_UpperCAmelCase).eval()

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

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

			SCREAMING_SNAKE_CASE						= model(**_UpperCAmelCase)

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

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

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






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

			return entity_vocab


if __name__ == "__main__":
				a_		:						Any             =    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.'
				)
				a_		:						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,
				)












a_		:						Tuple             =    {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
a_		:						List[Any]             =    {value: key for key, value in MORSE_CODE_DICT.items()}






def 				lowerCamelCase__				(_UpperCAmelCase):
			return " ".join(MORSE_CODE_DICT[char] for char in message.upper())






def 				lowerCamelCase__				(_UpperCAmelCase):
			return "".join(REVERSE_DICT[char] for char in message.split())






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'Morse code here!'
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= encrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= decrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)


if __name__ == "__main__":
				main()




