





'''simple docstring'''






import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def      lowercase  (  lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


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

				return orig_key





def      lowercase  (  lowerCAmelCase :						Union[str, Any]   , lowerCAmelCase :						List[str]):



				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								_A :				Dict				=  orig_state_dict.pop(lowerCAmelCase)

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

				_A :				Any				=  orig_state_dict['''cls.predictions.decoder.bias''']
				_A :				int				=  (
				    torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
				)

				return orig_state_dict





def      lowercase  (  lowerCAmelCase :						List[Any]   , lowerCAmelCase :						List[str]   , lowerCAmelCase :						str):



				"""simple docstring"""


				_A :				List[str]				=  torch.load(lowerCAmelCase   , map_location='''cpu''')['''model_state_dict''']
				_A :				Any				=  NystromformerConfig.from_json_file(lowerCAmelCase)
				_A :				str				=  NystromformerForMaskedLM(lowerCAmelCase)

				_A :				List[Any]				=  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      :   List[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      :   Optional[int]								=     parser.parse_args()
	convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Optional[int]:
					super().__init__(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A :				Union[str, Any]				=  (
					    next_prime(value % self.size_table			)
					    if not is_prime(value % self.size_table			)
					    else value % self.size_table
					)  # gt = bigger than
					return next_prime_gt - (data % next_prime_gt)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					return (increment * self.__hash_function_a(UpperCAmelCase__   ,     UpperCAmelCase__			)) % self.size_table




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:
					_A :				Optional[int]				=  1
					_A :				Optional[int]				=  self.hash_function(UpperCAmelCase__			)

					while self.values[new_key] is not None and self.values[new_key] != key:
									_A :				int				=  (
									    self.__hash_double_function(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)
									    if self.balanced_factor() >= self.lim_charge
									    else None
									)
									if new_key is None:
													break
									else:
													i += 1

					return new_key
