
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def lowerCamelCase_       (			A			:      Union[str, Any]						):




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

      return orig_key
def lowerCamelCase_       (			A			:      List[str]		,					A			:      List[str]						):




      """simple docstring"""
      for key in orig_state_dict.copy().keys():
            lowerCAmelCase_						=      orig_state_dict.pop(A						)

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

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

      return orig_state_dict
def lowerCamelCase_       (			A			:      Optional[Any]		,					A			:      Any		,					A			:      Optional[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      torch.load(A		,					map_location='''cpu'''						)['''model_state_dict''']
      lowerCAmelCase_						=      MraConfig.from_json_file(A						)
      lowerCAmelCase_						=      MraForMaskedLM(A						)

      lowerCAmelCase_						=      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__":
 _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)





import math
def lowerCamelCase_       (			A			:      int						):




      """simple docstring"""

      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 lowerCamelCase_       (			A			:      int = 2_00_00_00						):




      """simple docstring"""
      return sum(num for num in range(3		,					A		,					2						) if is_prime(A						)						) + 2 if n > 2 else 0


if __name__ == "__main__":
 print(f'''{solution() = }''')



