

"""simple docstring"""


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Dict:
							if "model" in orig_key:
														__lowerCAmelCase: Dict					   =    orig_key.replace("model."      ,				""				)
							if "norm1" in orig_key:
														__lowerCAmelCase: str					   =    orig_key.replace("norm1"      ,				"attention.output.LayerNorm"				)
							if "norm2" in orig_key:
														__lowerCAmelCase: Optional[int]					   =    orig_key.replace("norm2"      ,				"output.LayerNorm"				)
							if "norm" in orig_key:
														__lowerCAmelCase: Dict					   =    orig_key.replace("norm"      ,				"LayerNorm"				)
							if "transformer" in orig_key:
														__lowerCAmelCase: Dict					   =    orig_key.split("."				)[0].split("_"				)[-1]
														__lowerCAmelCase: Dict					   =    orig_key.replace(F"transformer_{layer_num}"      ,				F"encoder.layer.{layer_num}"				)
							if "mha.attn" in orig_key:
														__lowerCAmelCase: Any					   =    orig_key.replace("mha.attn"      ,				"attention.self"				)
							if "mha" in orig_key:
														__lowerCAmelCase: Any					   =    orig_key.replace("mha"      ,				"attention"				)
							if "W_q" in orig_key:
														__lowerCAmelCase: str					   =    orig_key.replace("W_q"      ,				"self.query"				)
							if "W_k" in orig_key:
														__lowerCAmelCase: Tuple					   =    orig_key.replace("W_k"      ,				"self.key"				)
							if "W_v" in orig_key:
														__lowerCAmelCase: Tuple					   =    orig_key.replace("W_v"      ,				"self.value"				)
							if "ff1" in orig_key:
														__lowerCAmelCase: Optional[Any]					   =    orig_key.replace("ff1"      ,				"intermediate.dense"				)
							if "ff2" in orig_key:
														__lowerCAmelCase: Optional[int]					   =    orig_key.replace("ff2"      ,				"output.dense"				)
							if "ff" in orig_key:
														__lowerCAmelCase: int					   =    orig_key.replace("ff"      ,				"output.dense"				)
							if "mlm_class" in orig_key:
														__lowerCAmelCase: Union[str, Any]					   =    orig_key.replace("mlm.mlm_class"      ,				"cls.predictions.decoder"				)
							if "mlm" in orig_key:
														__lowerCAmelCase: List[str]					   =    orig_key.replace("mlm"      ,				"cls.predictions.transform"				)
							if "cls" not in orig_key:
														__lowerCAmelCase: Dict					   =    "nystromformer." + orig_key

							return orig_key






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
							for key in orig_state_dict.copy().keys():
														__lowerCAmelCase: Tuple					   =    orig_state_dict.pop(__SCREAMING_SNAKE_CASE				)

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

							__lowerCAmelCase: Optional[Any]					   =    orig_state_dict["cls.predictions.decoder.bias"]
							__lowerCAmelCase: str					   =    (
							    torch.arange(config.max_position_embeddings				).expand((1, -1)				) + 2
							)

							return orig_state_dict






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			List[Any]:
							__lowerCAmelCase: int					   =    torch.load(__SCREAMING_SNAKE_CASE      ,				map_location="cpu"				)["model_state_dict"]
							__lowerCAmelCase: Union[str, Any]					   =    NystromformerConfig.from_json_file(__SCREAMING_SNAKE_CASE				)
							__lowerCAmelCase: Dict					   =    NystromformerForMaskedLM(__SCREAMING_SNAKE_CASE				)

							__lowerCAmelCase: Tuple					   =    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__":
			__A		     =    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."
			)
			__A		     =    parser.parse_args()
			convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""


import math






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			list:
		if n <= 0:
				raise ValueError("Only positive integers have prime factors"				)
		__lowerCAmelCase: Tuple					   =    []
		while n % 2 == 0:
				pf.append(2				)
				__lowerCAmelCase: str					   =    int(n / 2				)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				while n % i == 0:
						pf.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: List[Any]					   =    int(n / i				)
		if n > 2:
				pf.append(__SCREAMING_SNAKE_CASE				)
		return pf






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: int					   =    1
		__lowerCAmelCase: str					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Dict					   =    int(n / 2				)
		div *= temp
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: int					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: Tuple					   =    int(n / i				)
				div *= temp
		if n > 1:
				div *= 2
		return div






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: Dict					   =    1
		__lowerCAmelCase: int					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Tuple					   =    int(n / 2				)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: List[Any]					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: List[str]					   =    int(n / i				)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Optional[int]					   =    n
		for x in set(prime_factors(__SCREAMING_SNAKE_CASE				)				):
				s *= (x - 1) / x
		return int(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


