
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



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

					return orig_key



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					for key in orig_state_dict.copy().keys():
										lowercase		      =		orig_state_dict.pop(lowerCAmelCase__						)

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

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

					return orig_state_dict



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		torch.load(lowerCAmelCase__					,     map_location='''cpu'''						)["""model_state_dict"""]
					lowercase		      =		NystromformerConfig.from_json_file(lowerCAmelCase__						)
					lowercase		      =		NystromformerForMaskedLM(lowerCAmelCase__						)

					lowercase		      =		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__":
							lowercase__						: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."
							)
							lowercase__						:Tuple					    =       parser.parse_args()
							convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


