
'''simple docstring'''




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()
lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						RobertaPreLayerNormConfig.from_pretrained(
        snake_case__		,				architectures=['''RobertaPreLayerNormForMaskedLM''']						)

    # convert state_dict
    A       :   int					=						torch.load(hf_hub_download(repo_id=snake_case__		,				filename='''pytorch_model.bin'''						)						)
    A       :   str					=						{}
    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.'''						):
            A       :   str					=						'''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

        A       :   Tuple					=						tensor_value

    A       :   Optional[int]					=						RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=snake_case__		,				config=snake_case__		,				state_dict=snake_case__						)
    model.save_pretrained(snake_case__						)

    # convert tokenizer
    A       :   Any					=						AutoTokenizer.from_pretrained(snake_case__						)
    tokenizer.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				int    		=			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.'
      )
      lowercase			:				Tuple    		=			parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset







@pytest.mark.parametrize('''dataset_size'''		,				[None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,				['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if input_in_memory_max_size != "default":
        monkeypatch.setattr(datasets.config		,				'''IN_MEMORY_MAX_SIZE'''		,				snake_case__						)
    A       :   Dict					=						datasets.config.IN_MEMORY_MAX_SIZE
    if input_in_memory_max_size == "default":
        assert in_memory_max_size == 0
    else:
        assert in_memory_max_size == input_in_memory_max_size
    if dataset_size and in_memory_max_size:
        A       :   Dict					=						dataset_size < in_memory_max_size
    else:
        A       :   Tuple					=						False
    A       :   int					=						is_small_dataset(snake_case__						)
    assert result == expected




