"""simple docstring"""




import argparse

import torch

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

				return orig_key
def 			UpperCamelCase__						(    A__      ,			A__   )       -> Union[str, Any]:
				for key in orig_state_dict.copy().keys():
								snake_case__ :     str 							=				orig_state_dict.pop(A__   )

								if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
												continue
								else:
												snake_case__ :     Optional[Any] 							=				val

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

				return orig_state_dict
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__   )       -> Dict:
				snake_case__ :     List[Any] 							=				torch.load(A__      ,			map_location='cpu'   )['model_state_dict']
				snake_case__ :     Tuple 							=				NystromformerConfig.from_json_file(A__   )
				snake_case__ :     Union[str, Any] 							=				NystromformerForMaskedLM(A__   )

				snake_case__ :     int 							=				convert_checkpoint_helper(A__      ,			A__   )

				model.load_state_dict(A__   )
				model.eval()
				model.save_pretrained(A__   )

				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )


if __name__ == "__main__":
				lowerCAmelCase__					:       int		=       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.'''
				)
				lowerCAmelCase__					:       Tuple		=       parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



def 			UpperCamelCase__						(    A__   )       -> int:
				if number != int(A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if number < 0:
								raise ValueError('factorial() not defined for negative values'   )
				snake_case__ :     Optional[Any] 							=				1
				for i in range(1      ,			number + 1   ):
								value *= i
				return value
def 			UpperCamelCase__						(    A__   )       -> int:
				if not isinstance(A__      ,			A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if n < 0:
								raise ValueError('factorial() not defined for negative values'   )
				return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__					:       Any		=       int(input('''Enter a positive integer: ''').strip() or 0)
				print(F'''factorial{n} is {factorial(n)}''')


