



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



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

					return orig_key



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					for key in orig_state_dict.copy().keys():
										UpperCamelCase_			= orig_state_dict.pop(_lowerCAmelCase)

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

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

					return orig_state_dict



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= torch.load(_lowerCAmelCase ,						map_location="cpu")["model_state_dict"]
					UpperCamelCase_			= NystromformerConfig.from_json_file(_lowerCAmelCase)
					UpperCamelCase_			= NystromformerForMaskedLM(_lowerCAmelCase)

					UpperCamelCase_			= convert_checkpoint_helper(_lowerCAmelCase ,						_lowerCAmelCase)

					model.load_state_dict(_lowerCAmelCase)
					model.eval()
					model.save_pretrained(_lowerCAmelCase)

					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 :				Union[str, Any]							=parser.parse_args()
	convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		lowerCamelCase :			int    		=[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		lowerCamelCase :			List[Any]    		=1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(SCREAMING_SNAKE_CASE_				,							n + 1		):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

