



import argparse

import torch

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

       return orig_key
def 	lowercase_							(A			:			List[str]		, A			:			Any				):
       for key in orig_state_dict.copy().keys():
              snake_case__							:     Any            =     orig_state_dict.pop(A				)

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     snake_case__							:     int            =     val

       snake_case__							:     str            =     orig_state_dict['cls.predictions.decoder.bias']
       snake_case__							:     int            =     torch.arange(A				).expand((1, -1)				) + 2

       return orig_state_dict
def 	lowercase_							(A			:			List[str]		, A			:			List[str]		, A			:			Union[str, Any]				):
       snake_case__							:     Any            =     torch.load(A		, map_location='cpu'				)['model_state_dict']
       snake_case__							:     List[Any]            =     MraConfig.from_json_file(A				)
       snake_case__							:     Union[str, Any]            =     MraForMaskedLM(A				)

       snake_case__							:     int            =     convert_checkpoint_helper(config.max_position_embeddings		, A				)

       print(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_       :int				  =				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."
 )
 a_       :Optional[int]				  =				parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











import math
def 	lowercase_							(A			:			int				):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5		, int(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






