



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


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

				return orig_key


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase						:  List[Any]     					=	orig_state_dict.pop(_UpperCamelCase			)

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

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

				return orig_state_dict


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  Optional[Any]     					=	torch.load(_UpperCamelCase      ,					map_location='''cpu'''			)['''model_state_dict''']
				_UpperCAmelCase						:  str     					=	NystromformerConfig.from_json_file(_UpperCamelCase			)
				_UpperCAmelCase						:  List[str]     					=	NystromformerForMaskedLM(_UpperCamelCase			)

				_UpperCAmelCase						:  Optional[int]     					=	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__":
					UpperCAmelCase__			:   Optional[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.'
					)
					UpperCAmelCase__			:   Tuple      			=    parser.parse_args()
					convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





UpperCAmelCase__			:   Optional[int]      			=    [0.4_85, 0.4_56, 0.4_06]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.2_29, 0.2_24, 0.2_25]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   Optional[Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   str      			=    [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase__			:   Dict      			=    [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
