





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






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

		return orig_key






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							Optional[Any]			):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE__								=					orig_state_dict.pop(UpperCamelCase__			)

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

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

		return orig_state_dict






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Any    ,      UpperCamelCase__:							Tuple    ,      UpperCamelCase__:							Optional[Any]			):
		SCREAMING_SNAKE_CASE__								=					torch.load(UpperCamelCase__    ,      map_location="""cpu"""			)["""model_state_dict"""]
		SCREAMING_SNAKE_CASE__								=					NystromformerConfig.from_json_file(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					NystromformerForMaskedLM(UpperCamelCase__			)

		SCREAMING_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__":
	_lowerCamelCase       =	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.'
	)
	_lowerCamelCase       =	parser.parse_args()
	convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					SCREAMING_SNAKE_CASE__								=					0
		for letter in data:
				SCREAMING_SNAKE_CASE__								=					(hash_ << 4) + ord(UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					hash_ & 0XF_0_0_0_0_0_0_0
				if x != 0:
						hash_ ^= x >> 24
				hash_ &= ~x
		return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()