

"""simple docstring"""





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

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

	return orig_key

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	for key in orig_state_dict.copy().keys():
		lowerCAmelCase__			:		Dict						       =			orig_state_dict.pop(A_				)

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

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

	return orig_state_dict

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Union[str, Any]						       =			torch.load(A_		,  map_location='''cpu'''				)["model_state_dict"]
	lowerCAmelCase__			:		Dict						       =			NystromformerConfig.from_json_file(A_				)
	lowerCAmelCase__			:		Optional[Any]						       =			NystromformerForMaskedLM(A_				)

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







"""simple docstring"""





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			53_81
	for x in s:
		lowerCAmelCase__			:		Tuple						       =			((hash_value << 5) + hash_value) + ord(A_				)
	return hash_value & 0Xf_f_f_f_f_f_f_f




