code: 
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile, rmtree
  from typing import Generator
  
  import datasets.config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.filelock import logger as fl_logger
  from datasets.utils.info_utils import VerificationMode
  from datasets.utils.logging import ERROR, get_logger
  
  
  SCREAMING_SNAKE_CASE_     				=   get_logger(__name__)
  def 			__UpperCAmelCase		(		a_			:		Optional[int]				)     ->       Optional[int]:
     return TestCommand(
         args.dataset    ,  args.name    ,  args.cache_dir    ,  args.data_dir    ,  args.all_configs    ,  args.save_info or args.save_infos    ,  args.ignore_verifications    ,  args.force_redownload    ,  args.clear_cache    ,  )
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
         _lowerCamelCase							:							Optional[Any]       =			False  # to tell pytest it's not a test class
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					parser.add_parser("test"							,help="Test dataset implementation."		)
            test_parser.add_argument("--name"							,type=lowerCamelCase_							,default=lowerCamelCase_							,help="Dataset processing name"		)
            test_parser.add_argument(
                "--cache_dir"							,type=lowerCamelCase_							,default=lowerCamelCase_							,help="Cache directory where the datasets are stored."							,)
            test_parser.add_argument(
                "--data_dir"							,type=lowerCamelCase_							,default=lowerCamelCase_							,help="Can be used to specify a manual directory to get the files from."							,)
            test_parser.add_argument("--all_configs"							,action="store_true"							,help="Test all dataset configurations"		)
            test_parser.add_argument(
                "--save_info"							,action="store_true"							,help="Save the dataset infos in the dataset card (README.md)"		)
            test_parser.add_argument(
                "--ignore_verifications"							,action="store_true"							,help="Run the test without checksums and splits checks."							,)
            test_parser.add_argument("--force_redownload"							,action="store_true"							,help="Force dataset redownload"		)
            test_parser.add_argument(
                "--clear_cache"							,action="store_true"							,help="Remove downloaded files and cached datasets after each config test"							,)
            # aliases
            test_parser.add_argument("--save_infos"							,action="store_true"							,help="alias to save_info"		)
            test_parser.add_argument("dataset"							,type=lowerCamelCase_							,help="Name of the dataset to download"		)
            test_parser.set_defaults(func=_test_command_factory		)
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					dataset
            _UpperCAmelCase   	=					name
            _UpperCAmelCase   	=					cache_dir
            _UpperCAmelCase   	=					data_dir
            _UpperCAmelCase   	=					all_configs
            _UpperCAmelCase   	=					save_infos
            _UpperCAmelCase   	=					ignore_verifications
            _UpperCAmelCase   	=					force_redownload
            _UpperCAmelCase   	=					clear_cache
            if clear_cache and not cache_dir:
               print(
                   "When --clear_cache is used, specifying a cache directory is mandatory.\n"
                   "The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
                   "Please provide a --cache_dir that will be used to test the dataset script."		)
               exit(1		)
            if save_infos:
               _UpperCAmelCase   	=					True
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            fl_logger().setLevel(lowerCamelCase_		)
            if self._name is not None and self._all_configs:
               print("Both parameters `config` and `all_configs` can't be used at once."		)
               exit(1		)
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self._dataset, self._name
            _UpperCAmelCase   	=					dataset_module_factory(lowerCamelCase_		)
            _UpperCAmelCase   	=					import_main_class(module.module_path		)
            _UpperCAmelCase   	=					len(builder_cls.BUILDER_CONFIGS		) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1
  
            def get_builders() -> Generator[DatasetBuilder, None, None]:
               if self._all_configs and builder_cls.BUILDER_CONFIGS:
                  for i, config in enumerate(builder_cls.BUILDER_CONFIGS		):
                     if "config_name" in module.builder_kwargs:
                        yield builder_cls(
                            cache_dir=self._cache_dir							,data_dir=self._data_dir							,**module.builder_kwargs							,)
                     else:
                        yield builder_cls(
                            config_name=config.name							,cache_dir=self._cache_dir							,data_dir=self._data_dir							,**module.builder_kwargs							,)
               else:
                  if "config_name" in module.builder_kwargs:
                     yield builder_cls(cache_dir=self._cache_dir							,data_dir=self._data_dir							,**module.builder_kwargs		)
                  else:
                     yield builder_cls(
                         config_name=lowerCamelCase_							,cache_dir=self._cache_dir							,data_dir=self._data_dir							,**module.builder_kwargs							,)
  
            for j, builder in enumerate(get_builders()		):
               print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''		)
               _UpperCAmelCase   	=					os.path.exists(
                   os.path.join(builder.get_imported_module_dir()							,datasets.config.DATASETDICT_INFOS_FILENAME		)		)  # record checksums only if we need to update a (deprecated) dataset_infos.json
               builder.download_and_prepare(
                   download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                   if not self._force_redownload
                   else DownloadMode.FORCE_REDOWNLOAD							,verification_mode=VerificationMode.NO_CHECKS
                   if self._ignore_verifications
                   else VerificationMode.ALL_CHECKS							,try_from_hf_gcs=lowerCamelCase_							,)
               builder.as_dataset()
               if self._save_infos:
                  builder._save_infos()
  
               # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
               # The dataset_infos are saved in the YAML part of the README.md
  
               # Let's move it to the original directory of the dataset script, to allow the user to
               # upload them on S3 at the same time afterwards.
               if self._save_infos:
                  _UpperCAmelCase   	=					os.path.join(builder_cls.get_imported_module_dir()							,"README.md"		)
                  _UpperCAmelCase   	=					Path(lowerCamelCase_		).name + ".py"
                  _UpperCAmelCase   	=					os.path.join(lowerCamelCase_							,lowerCamelCase_		)
                  if os.path.isfile(lowerCamelCase_		):
                     _UpperCAmelCase   	=					os.path.dirname(lowerCamelCase_		)
                  elif os.path.isfile(lowerCamelCase_		):
                     _UpperCAmelCase   	=					path
                  elif os.path.isdir(lowerCamelCase_		):  # for local directories containing only data files
                     _UpperCAmelCase   	=					path
                  else:  # in case of a remote dataset
                     _UpperCAmelCase   	=					None
                     print(f'''Dataset card saved at {dataset_readme_path}'''		)
  
                  # Move dataset_info back to the user
                  if dataset_dir is not None:
                     _UpperCAmelCase   	=					os.path.join(lowerCamelCase_							,"README.md"		)
                     copyfile(lowerCamelCase_							,lowerCamelCase_		)
                     print(f'''Dataset card saved at {user_dataset_readme_path}'''		)
  
              # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
               if self._clear_cache:
                  if os.path.isdir(builder._cache_dir		):
                     logger.warning(f'''Clearing cache at {builder._cache_dir}'''		)
                     rmtree(builder._cache_dir		)
                  _UpperCAmelCase   	=					os.path.join(self._cache_dir							,datasets.config.DOWNLOADED_DATASETS_DIR		)
                  if os.path.isdir(lowerCamelCase_		):
                     logger.warning(f'''Clearing cache at {download_dir}'''		)
                     rmtree(lowerCamelCase_		)
  
            print("Test successful."		)
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
     if "model" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
     if "norm1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
     if "norm2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
     if "norm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
        _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
     if "mha" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
     if "W_q" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
     if "W_k" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
     if "W_v" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
     if "ff.0" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
     if "ff.2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
     if "ff" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
     if "mlm_class" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
     if "mlm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
     if "backbone.backbone.encoders" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
     if "cls" not in orig_key:
        _UpperCAmelCase   	=					"mra." + orig_key
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
     for key in orig_state_dict.copy().keys():
        _UpperCAmelCase   	=					orig_state_dict.pop(a_				)
  
        if ("pooler" in key) or ("sen_class" in key):
           continue
        else:
           _UpperCAmelCase   	=					val
  
     _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
     _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2
  
     return orig_state_dict
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
     _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
     _UpperCAmelCase   	=					MraForMaskedLM(a_				)
  
     _UpperCAmelCase   	=					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__":
    SCREAMING_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.'
    )
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  