





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_				=			logging.get_logger(__name__)






def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  str       )      ->				int:
   __snake_case							       =   RobertaPreLayerNormConfig.from_pretrained(
       snake_case_       ,      architectures=['''RobertaPreLayerNormForMaskedLM''']       )

   # convert state_dict
   __snake_case							       =   torch.load(hf_hub_download(repo_id=snake_case_       ,      filename='''pytorch_model.bin'''       )       )
   __snake_case							       =   {}
   for tensor_key, tensor_value in original_state_dict.items():
      # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
      if tensor_key.startswith('''roberta.'''       ):
         __snake_case							       =   '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''       ) :]

      # The original implementation contains weights which are not used, remove them from the state_dict
      if tensor_key.endswith('''.self.LayerNorm.weight'''       ) or tensor_key.endswith('''.self.LayerNorm.bias'''       ):
         continue

      __snake_case							       =   tensor_value

   __snake_case							       =   RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=snake_case_       ,      config=snake_case_       ,      state_dict=snake_case_       )
   model.save_pretrained(snake_case_       )

   # convert tokenizer
   __snake_case							       =   AutoTokenizer.from_pretrained(snake_case_       )
   tokenizer.save_pretrained(snake_case_       )


if __name__ == "__main__":
    snake_case_				=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint-repo',
        default=None,
        type=str,
        required=True,
        help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    snake_case_				=			parser.parse_args()
    convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)








import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case_				=			'\nimport os\n'

snake_case_				=			'\ndef foo():\n    import os\n    return False\n'

snake_case_				=			'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case_				=			'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case_				=			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]






@pytest.mark.parametrize('''case'''       ,      snake_case_       )
def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  Optional[int]       )      ->				Dict:
   __snake_case							       =   os.path.join(snake_case_       ,      '''test_file.py'''       )
   with open(snake_case_       ,      '''w'''       ) as _tmp_file:
      _tmp_file.write(snake_case_       )

   __snake_case							       =   get_imports(snake_case_       )
   assert parsed_imports == ["os"]

