


"""simple docstring"""




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





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

			return orig_key





def 			a_   (  lowercase__   :Optional[Any], lowercase__   :Dict    ):
			for key in orig_state_dict.copy().keys():
						__lowerCamelCase        =			orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

						if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
									continue
						else:
									__lowerCamelCase        =			val

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

			return orig_state_dict





def 			a_   (  lowercase__   :Tuple, lowercase__   :Dict, lowercase__   :int    ):
			__lowerCamelCase        =			torch.load(_SCREAMING_SNAKE_CASE, map_location="""cpu"""    )['model_state_dict']
			__lowerCamelCase        =			NystromformerConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
			__lowerCamelCase        =			NystromformerForMaskedLM(_SCREAMING_SNAKE_CASE    )

			__lowerCamelCase        =			convert_checkpoint_helper(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE    )

			model.load_state_dict(_SCREAMING_SNAKE_CASE    )
			model.eval()
			model.save_pretrained(_SCREAMING_SNAKE_CASE    )

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


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








"""simple docstring"""




def 			a_   (  lowercase__   :str, lowercase__   :int = 13    ):
			__lowerCamelCase        =			""""""
			for c in s:
						if "A" <= c <= "Z":
									out += chr(ord("""A"""    ) + (ord(lowercase__    ) - ord("""A"""    ) + n) % 26    )
						elif "a" <= c <= "z":
									out += chr(ord("""a"""    ) + (ord(lowercase__    ) - ord("""a"""    ) + n) % 26    )
						else:
									out += c
			return out





def 			a_   (  ):
			__lowerCamelCase        =			input("""Enter message: """    )

			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
			print("""Encryption:""", lowercase__    )

			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
			print("""Decryption: """, lowercase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




