
"""simple docstring"""

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


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

		return orig_key
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCamelCase	:     Tuple            =       orig_state_dict.pop(a_     )

				if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
						continue
				else:
						lowerCamelCase	:     Optional[Any]            =       val

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

		return orig_state_dict
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[Any]            =       torch.load(a_,		map_location='cpu'     )['model_state_dict']
		lowerCamelCase	:     int            =       NystromformerConfig.from_json_file(a_     )
		lowerCamelCase	:     int            =       NystromformerForMaskedLM(a_     )

		lowerCamelCase	:     str            =       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__":
					_A				=					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				=					parser.parse_args()
					convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








"""simple docstring"""

def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if not isinstance(a_,		a_     ):
				raise TypeError('recursive_lucas_number accepts only integer arguments.'     )
		if n_th_number == 0:
				return 2
		if n_th_number == 1:
				return 1

		return recursive_lucas_number(n_th_number - 1     ) + recursive_lucas_number(
		    n_th_number - 2     )
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if not isinstance(a_,		a_     ):
				raise TypeError('dynamic_lucas_number accepts only integer arguments.'     )
		lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       2, 1
		for _ in range(a_     ):
				lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       b, a + b
		return a


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					_A				=					int(input('Enter the number of terms in lucas series:\n').strip())
					print('Using recursive function to calculate lucas series:')
					print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
					print('\nUsing dynamic function to calculate lucas series:')
					print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))






