"""simple docstring"""







import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





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

	return orig_key


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		_lowercase			:     Optional[int]      					=				orig_state_dict.pop(__UpperCAmelCase      )

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

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

	return orig_state_dict


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     List[str]      					=				torch.load(__UpperCAmelCase  ,map_location='cpu'      )['model_state_dict']
	_lowercase			:     List[str]      					=				NystromformerConfig.from_json_file(__UpperCAmelCase      )
	_lowercase			:     int      					=				NystromformerForMaskedLM(__UpperCAmelCase      )

	_lowercase			:     List[Any]      					=				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__":
						SCREAMING_SNAKE_CASE						=    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.'
						)
						SCREAMING_SNAKE_CASE						=    parser.parse_args()
						convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE						=    TypeVar('T', bound=bool)



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     T      					=				value
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None







				@property
				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					return self._value







				@staticmethod
				def 				__UpperCAmelCase       (			lowerCamelCase_     :							RandomizedHeapNode[T] | None      ,       lowerCamelCase_     :							RandomizedHeapNode[T] | None    ):

					"""simple docstring"""






					if not roota:
						return roota

					if not roota:
						return roota

					if roota.value > roota.value:
						_lowercase					,   _lowercase			:     List[Any]      					=				roota, roota

					if random.choice([True, False]    ):
						_lowercase					,   _lowercase			:     Optional[int]      					=				roota.right, roota.left

					_lowercase			:     Tuple      					=				RandomizedHeapNode.merge(roota.left      ,       lowerCamelCase_    )

					return roota



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							Iterable[T] | None = ()    ):

					"""simple docstring"""






					_lowercase			:     RandomizedHeapNode[T] | None      					=				None

					if data:
						for item in data:
							self.insert(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     str      					=				RandomizedHeapNode.merge(self._root      ,       RandomizedHeapNode(lowerCamelCase_    )    )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				self.top()

					if self._root is None:
						return None

					_lowercase			:     List[Any]      					=				RandomizedHeapNode.merge(self._root.left      ,       self._root.right    )

					return result







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					if not self._root:
						raise IndexError('Can\'t get top element for the empty heap.'    )
					return self._root.value







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				None







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				[]
					while self:
						result.append(self.pop()    )

					return result







				def __bool__(			self     :							List[str]    ):

					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()



