import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



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

       return orig_key



def A__  (			lowercase:     List[Any],		lowercase:     str					)				->		str:
       for key in orig_state_dict.copy().keys():
              A						:     Any         =orig_state_dict.pop(lowercase					)

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     A						:     List[Any]         =val

       A						:     Optional[Any]         =orig_state_dict['cls.predictions.decoder.bias']
       A						:     Any         =torch.arange(lowercase					).expand((1, -1)					) + 2

       return orig_state_dict



def A__  (			lowercase:     List[Any],		lowercase:     int,		lowercase:     Dict					)				->		Union[str, Any]:
       A						:     int         =torch.load(lowercase,		map_location='cpu'					)['model_state_dict']
       A						:     List[Any]         =YosoConfig.from_json_file(lowercase					)
       A						:     Tuple         =YosoForMaskedLM(lowercase					)

       A						:     Tuple         =convert_checkpoint_helper(config.max_position_embeddings,		lowercase					)

       print(model.load_state_dict(lowercase					)					)
       model.eval()
       model.save_pretrained(lowercase					)

       print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'					)


if __name__ == "__main__":
    _lowercase							:      Tuple      							=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The json file for YOSO model config.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowercase							:      List[str]      							=parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







import math



def A__  (			lowercase:     int					)				->		list:
       A						:     Optional[Any]         =[True] * n
       A						:     Tuple         =False
       A						:     List[Any]         =False
       A						:     Dict         =True

       for i in range(3,		int(n**0.5 + 1					),		2					):
              A						:     Dict         =i * 2
              while index < n:
                     A						:     Dict         =False
                     A						:     Dict         =index + i

       A						:     Tuple         =[2]

       for i in range(3,		lowercase,		2					):
              if is_prime[i]:
                     primes.append(lowercase					)

       return primes



def A__  (			lowercase:     int = 999_966_663_333					)				->		int:
       A						:     Optional[int]         =math.floor(math.sqrt(lowercase					)					) + 100
       A						:     Optional[int]         =prime_sieve(lowercase					)

       A						:     Optional[Any]         =0
       A						:     List[Any]         =0
       A						:     Union[str, Any]         =primes[prime_index]

       while (last_prime**2) <= limit:
              A						:     Tuple         =primes[prime_index + 1]

              A						:     Optional[int]         =last_prime**2
              A						:     Tuple         =next_prime**2

              # Get numbers divisible by lps(current)
              A						:     int         =lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A						:     List[Any]         =upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A						:     Any         =0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A						:     List[str]         =next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
    print(solution())






