import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			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  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			for key in orig_state_dict.copy().keys():
						_snake_case														=   orig_state_dict.pop(lowercase_	)

						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  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   torch.load(lowercase_				,						map_location="cpu"	)["model_state_dict"]
			_snake_case														=   NystromformerConfig.from_json_file(lowercase_	)
			_snake_case														=   NystromformerForMaskedLM(lowercase_	)

			_snake_case														=   convert_checkpoint_helper(lowercase_				,						lowercase_	)

			model.load_state_dict(lowercase_	)
			model.eval()
			model.save_pretrained(lowercase_	)

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


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    Dict     						=							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.'''
			)
			_SCREAMING_SNAKE_CASE:    List[str]     						=							parser.parse_args()
			convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









from __future__ import annotations

import re





def 						A_					(						lowercase_			)				->list[str]:







 """simple docstring"""

 def alphanum_key(lowercase_			):
  return [int(lowercase_			) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'	,  lowercase_			)]

 return sorted(lowercase_	,  key=lowercase_			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




