






"""simple docstring"""



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any							)->						Any:






     '''simple docstring'''



     if "model" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				orig_key.replace("model."       , ""							)
     if "norm1" in orig_key:
          UpperCAmelCase__		:  List[Any]				=				orig_key.replace("norm1"       , "attention.output.LayerNorm"							)
     if "norm2" in orig_key:
          UpperCAmelCase__		:  Tuple				=				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__		:  Optional[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__		:  List[Any]				=				orig_key.replace("W_q"       , "self.query"							)
     if "W_k" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				orig_key.replace("W_k"       , "self.key"							)
     if "W_v" in orig_key:
          UpperCAmelCase__		:  Any				=				orig_key.replace("W_v"       , "self.value"							)
     if "ff.0" in orig_key:
          UpperCAmelCase__		:  str				=				orig_key.replace("ff.0"       , "intermediate.dense"							)
     if "ff.2" in orig_key:
          UpperCAmelCase__		:  List[Any]				=				orig_key.replace("ff.2"       , "output.dense"							)
     if "ff" in orig_key:
          UpperCAmelCase__		:  str				=				orig_key.replace("ff"       , "output.dense"							)
     if "mlm_class" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				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__		:  List[Any]				=				orig_key.replace("backbone.backbone.encoders"       , "encoder.layer"							)
     if "cls" not in orig_key:
          UpperCAmelCase__		:  Tuple				=				"mra." + orig_key

     return orig_key


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[str]       , snake_case    :      Union[str, Any]							)->						Optional[int]:






     '''simple docstring'''



     for key in orig_state_dict.copy().keys():
          UpperCAmelCase__		:  Dict				=				orig_state_dict.pop(snake_case							)

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

     UpperCAmelCase__		:  Tuple				=				orig_state_dict["cls.predictions.decoder.bias"]
     UpperCAmelCase__		:  List[str]				=				torch.arange(snake_case							).expand((1, -1)							) + 2

     return orig_state_dict


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int       , snake_case    :      Union[str, Any]       , snake_case    :      List[Any]							)->						Any:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[int]				=				torch.load(snake_case       , map_location="cpu"							)["model_state_dict"]
     UpperCAmelCase__		:  Any				=				MraConfig.from_json_file(snake_case							)
     UpperCAmelCase__		:  List[Any]				=				MraForMaskedLM(snake_case							)

     UpperCAmelCase__		:  Dict				=				convert_checkpoint_helper(config.max_position_embeddings       , snake_case							)

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

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


if __name__ == "__main__":
 _lowerCAmelCase     :     Optional[Any]            =	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."""
 )
 _lowerCAmelCase     :     Any            =	parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if right <= left:
          return
     UpperCAmelCase__		:  str				=				left
     UpperCAmelCase__		:  List[str]				=				right
     UpperCAmelCase__		:  List[Any]				=				sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(snake_case       , snake_case       , a - 1							)
     quick_sort_apartition(snake_case       , b + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if left < right:
          UpperCAmelCase__		:  int				=				lomuto_partition(snake_case       , snake_case       , snake_case							)
          quick_sort_lomuto_partition(snake_case       , snake_case       , pivot_index - 1							)
          quick_sort_lomuto_partition(snake_case       , pivot_index + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				sorting[right]
     UpperCAmelCase__		:  Optional[Any]				=				left
     for i in range(snake_case       , snake_case							):
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[i], sorting[store_index]
               store_index += 1
     UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[store_index], sorting[right]
     return store_index


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list							)->						list:






     '''simple docstring'''



     if len(snake_case							) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

 _lowerCAmelCase     :     Optional[int]            =	input("""Enter numbers separated by a comma:\n""").strip()
 _lowerCAmelCase     :     str            =	[int(item) for item in user_input.split(""",""")]
 quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
 print(unsorted)


