





'''simple docstring'''




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM




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

	return orig_key




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Optional[int]:
	for key in orig_state_dict.copy().keys():
		snake_case__  					=      orig_state_dict.pop(UpperCAmelCase_       )

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

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

	return orig_state_dict




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Any:
	snake_case__  					=      torch.load(UpperCAmelCase_				,							map_location='cpu'       )["model_state_dict"]
	snake_case__  					=      NystromformerConfig.from_json_file(UpperCAmelCase_       )
	snake_case__  					=      NystromformerForMaskedLM(UpperCAmelCase_       )

	snake_case__  					=      convert_checkpoint_helper(UpperCAmelCase_				,							UpperCAmelCase_       )

	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__":
			a__				: Optional[Any]									=   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__				: Optional[Any]									=   parser.parse_args()
			convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''




from typing import Callable




def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:

	def wrapper(UpperCAmelCase_       ):
		snake_case__  					=      function_with_docstring.__doc__
		return target_function

	return wrapper



