





'''simple docstring'''

# Lint as: python3

import hashlib
from typing import Union


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				# Data already in bytes, returns as it as
				return hash_data
		elif isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				# We keep the data as it as for it ot be later encoded to UTF-8
				# However replace `\\` with `/` for Windows compatibility
				__UpperCAmelCase	:							Tuple     =     hash_data.replace("\\",       "/"  )
		elif isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				__UpperCAmelCase	:							Dict     =     str(_UpperCAmelCase  )
		else:
				# If data is not of the required type, raise error
				raise InvalidKeyError(_UpperCAmelCase  )

		return hash_data.encode("utf-8"  )


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      List[Any]		,	UpperCAmelCase_       :      Any		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
							__UpperCAmelCase	:							int     =     f"\nFound Key {hash_data} of type {type(UpperCAmelCase_		)}"
							__UpperCAmelCase	:							int     =     "\nKeys should be either str, int or bytes type"
							super().__init__(f"{self.prefix}{self.err_msg}{self.suffix}"		)






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      str		,	UpperCAmelCase_       :      Dict		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      int=""		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     key
							__UpperCAmelCase	:							Dict     =     duplicate_key_indices
							__UpperCAmelCase	:							str     =     fix_msg
							__UpperCAmelCase	:							Union[str, Any]     =     "Found multiple examples generated with the same key"
							if len(UpperCAmelCase_		) <= 20:
									__UpperCAmelCase	:							Tuple     =     f"\nThe examples at index {', '.join(UpperCAmelCase_		)} have the key {key}"
							else:
									__UpperCAmelCase	:							Any     =     f"\nThe examples at index {', '.join(duplicate_key_indices[:20]		)}... ({len(UpperCAmelCase_		) - 20} more) have the key {key}"
							__UpperCAmelCase	:							int     =     "\n" + fix_msg if fix_msg else ""
							super().__init__(f"{self.prefix}{self.err_msg}{self.suffix}"		)






class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     hashlib.mda(_as_bytes(UpperCAmelCase_		)		)






					def 		lowerCamelCase_   (   self       :      Dict		,	UpperCAmelCase_       :      Union[str, int, bytes]		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     self._split_mda.copy()
							__UpperCAmelCase	:							Optional[Any]     =     _as_bytes(UpperCAmelCase_		)
							mda.update(UpperCAmelCase_		)
							# Convert to integer with hexadecimal conversion
							return int(mda.hexdigest()		,	16		)










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			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        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

						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        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       convert_checkpoint_helper(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			model.load_state_dict(SCREAMING_SNAKE_CASE )
			model.eval()
			model.save_pretrained(SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
		__A    :				List[str]    						=	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."""
		)
		__A    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






