import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Any:






      '''simple docstring'''





      if "model" in orig_key:
            UpperCAmelCase     :			str     	=orig_key.replace('''model.'''		,       ''''''    )
      if "norm1" in orig_key:
            UpperCAmelCase     :			Dict     	=orig_key.replace('''norm1'''		,       '''attention.output.LayerNorm'''    )
      if "norm2" in orig_key:
            UpperCAmelCase     :			Dict     	=orig_key.replace('''norm2'''		,       '''output.LayerNorm'''    )
      if "norm" in orig_key:
            UpperCAmelCase     :			Union[str, Any]     	=orig_key.replace('''norm'''		,       '''LayerNorm'''    )
      if "transformer" in orig_key:
            UpperCAmelCase     :			int     	=orig_key.split('''.'''    )[0].split('''_'''    )[-1]
            UpperCAmelCase     :			str     	=orig_key.replace(f'''transformer_{layer_num}'''		,       f'''encoder.layer.{layer_num}'''    )
      if "mha.attn" in orig_key:
            UpperCAmelCase     :			int     	=orig_key.replace('''mha.attn'''		,       '''attention.self'''    )
      if "mha" in orig_key:
            UpperCAmelCase     :			Dict     	=orig_key.replace('''mha'''		,       '''attention'''    )
      if "W_q" in orig_key:
            UpperCAmelCase     :			Union[str, Any]     	=orig_key.replace('''W_q'''		,       '''self.query'''    )
      if "W_k" in orig_key:
            UpperCAmelCase     :			List[Any]     	=orig_key.replace('''W_k'''		,       '''self.key'''    )
      if "W_v" in orig_key:
            UpperCAmelCase     :			Optional[Any]     	=orig_key.replace('''W_v'''		,       '''self.value'''    )
      if "ff.0" in orig_key:
            UpperCAmelCase     :			int     	=orig_key.replace('''ff.0'''		,       '''intermediate.dense'''    )
      if "ff.2" in orig_key:
            UpperCAmelCase     :			List[str]     	=orig_key.replace('''ff.2'''		,       '''output.dense'''    )
      if "ff" in orig_key:
            UpperCAmelCase     :			int     	=orig_key.replace('''ff'''		,       '''output.dense'''    )
      if "mlm_class" in orig_key:
            UpperCAmelCase     :			List[str]     	=orig_key.replace('''mlm.mlm_class'''		,       '''cls.predictions.decoder'''    )
      if "mlm" in orig_key:
            UpperCAmelCase     :			Any     	=orig_key.replace('''mlm'''		,       '''cls.predictions.transform'''    )
      if "backbone.backbone.encoders" in orig_key:
            UpperCAmelCase     :			str     	=orig_key.replace('''backbone.backbone.encoders'''		,       '''encoder.layer'''    )
      if "cls" not in orig_key:
            UpperCAmelCase     :			Optional[Any]     	='''mra.''' + orig_key

      return orig_key





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	int:






      '''simple docstring'''





      for key in orig_state_dict.copy().keys():
            UpperCAmelCase     :			Optional[int]     	=orig_state_dict.pop(__UpperCAmelCase    )

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  UpperCAmelCase     :			str     	=val

      UpperCAmelCase     :			Optional[int]     	=orig_state_dict['''cls.predictions.decoder.bias''']
      UpperCAmelCase     :			int     	=torch.arange(__UpperCAmelCase    ).expand((1, -1)    ) + 2

      return orig_state_dict





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	int:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	=torch.load(__UpperCAmelCase		,       map_location='''cpu'''    )['''model_state_dict''']
      UpperCAmelCase     :			Union[str, Any]     	=MraConfig.from_json_file(__UpperCAmelCase    )
      UpperCAmelCase     :			int     	=MraForMaskedLM(__UpperCAmelCase    )

      UpperCAmelCase     :			int     	=convert_checkpoint_helper(config.max_position_embeddings		,       __UpperCAmelCase    )

      print(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__":
     __snake_case         =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for Mra model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     __snake_case         =     parser.parse_args()
     convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

'''simple docstring'''
import math







def 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	Optional[int]:
  lowercase				:							Tuple									=   0
  if n % 2 == 0:
    count += 1
    while n % 2 == 0:
      lowercase				:							Optional[int]									=   int(n / 2			)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

  lowercase				:							Dict									=   3

  while i <= int(math.sqrt(__UpperCAmelCase			)			):
    if n % i == 0:
      count += 1
      while n % i == 0:
        lowercase				:							Dict									=   int(n / i			)
    lowercase				:							List[Any]									=   i + 2

  # this condition checks the prime
  # number n is greater than 2

  if n > 2:
    count += 1
  return count


if __name__ == "__main__":
 _A		:      List[Any]						   =5_1_2_4_2_1_8_3
 print(f"""The number of distinct prime factors is/are {exact_prime_factor_count(n)}""")
 print(f"""The value of log(log(n)) is {math.log(math.log(n)):.4f}""")