





"""simple docstring"""





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				UpperCamelCase =						orig_state_dict.pop(__UpperCAmelCase     )

				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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						torch.load(__UpperCAmelCase	,						map_location="cpu"     )['model_state_dict']
		UpperCamelCase =						NystromformerConfig.from_json_file(__UpperCAmelCase     )
		UpperCamelCase =						NystromformerForMaskedLM(__UpperCAmelCase     )

		UpperCamelCase =						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)







"""simple docstring"""







from __future__ import annotations

import math
import random


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[int]      					=				d * e - 1
	_lowercase			:     int      					=				0
	_lowercase			:     Union[str, Any]      					=				0
	while p == 0:
		_lowercase			:     int      					=				random.randint(2  ,n - 1      )
		_lowercase			:     str      					=				k
		while True:
			if t % 2 == 0:
				_lowercase			:     List[Any]      					=				t // 2
				_lowercase			:     int      					=				(g**t) % n
				_lowercase			:     Tuple      					=				math.gcd(x - 1  ,__UpperCAmelCase      )
				if x > 1 and y > 1:
					_lowercase			:     List[str]      					=				y
					_lowercase			:     str      					=				n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



