import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        a_	( _A					)     ->     Optional[Any]:







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

				return orig_key
def        a_	( _A ,				_A					)     ->     int:







				"""simple docstring"""
				for key in orig_state_dict.copy().keys():
								snake_case__													=       orig_state_dict.pop(_A					)

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

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

				return orig_state_dict
def        a_	( _A ,				_A ,				_A					)     ->     int:







				"""simple docstring"""
				snake_case__													=       torch.load(_A ,				map_location='cpu'					)['model_state_dict']
				snake_case__													=       NystromformerConfig.from_json_file(_A					)
				snake_case__													=       NystromformerForMaskedLM(_A					)

				snake_case__													=       convert_checkpoint_helper(_A ,				_A					)

				model.load_state_dict(_A					)
				model.eval()
				model.save_pretrained(_A					)

				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[Any]							=  parser.parse_args()
							convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




