






from string import ascii_uppercase

lowerCAmelCase_           = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_           = dict(enumerate(ascii_uppercase))







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      0
				while True:
								if x == i:
												A	 =      0
								if len(UpperCAmelCase   ) == len(UpperCAmelCase   ):
												break
								key += key[i]
								i += 1
				return key







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				A	 =      ''
				A	 =      0
				for letter in message:
								if letter == " ":
												cipher_text += " "
								else:
												A	 =      (dicta[letter] - dicta[key_new[i]]) % 26
												i += 1
												cipher_text += dicta[x]
				return cipher_text







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				A	 =      ''
				A	 =      0
				for letter in cipher_text:
								if letter == " ":
												or_txt += " "
								else:
												A	 =      (dicta[letter] + dicta[key_new[i]] + 26) % 26
												i += 1
												or_txt += dicta[x]
				return or_txt







def      snake_case		(							):
				A	 =      'THE GERMAN ATTACK'
				A	 =      'SECRET'
				A	 =      generate_key(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      cipher_text(UpperCAmelCase,						UpperCAmelCase   )
				print(f'Encrypted Text = {s}'   )
				print(f'Original Text = {original_text(UpperCAmelCase,						UpperCAmelCase   )}'   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





