




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_ : Union[str, Any]    		=       logging.get_logger(__name__)


def    A						(__A   :    str   ,				__A   :    Optional[Any]       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							RobertaPreLayerNormConfig.from_pretrained(
         __A   ,				architectures=['''RobertaPreLayerNormForMaskedLM''']       )

     # convert state_dict
     UpperCAmelCase_          =							torch.load(hf_hub_download(repo_id=__A   ,				filename='''pytorch_model.bin'''       )       )
     UpperCAmelCase_          =							{}
     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.'''       ):
               UpperCAmelCase_          =							'''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

          UpperCAmelCase_          =							tensor_value

     UpperCAmelCase_          =							RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__A   ,				config=__A   ,				state_dict=__A       )
     model.save_pretrained(__A       )

     # convert tokenizer
     UpperCAmelCase_          =							AutoTokenizer.from_pretrained(__A       )
     tokenizer.save_pretrained(__A       )


if __name__ == "__main__":
    snake_case_ : List[str]    		=       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_ : Union[str, Any]    		=       parser.parse_args()
    convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase       =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TapasForMaskedLM',
			    'TapasForQuestionAnswering',
			    'TapasForSequenceClassification',
			    'TapasModel',
			    'TapasPreTrainedModel',
			    'load_tf_weights_in_tapas',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFTapasForMaskedLM',
			    'TFTapasForQuestionAnswering',
			    'TFTapasForSequenceClassification',
			    'TFTapasModel',
			    'TFTapasPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
			from .tokenization_tapas import TapasTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tapas import (
						    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TapasForMaskedLM,
						    TapasForQuestionAnswering,
						    TapasForSequenceClassification,
						    TapasModel,
						    TapasPreTrainedModel,
						    load_tf_weights_in_tapas,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_tapas import (
						    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTapasForMaskedLM,
						    TFTapasForQuestionAnswering,
						    TFTapasForSequenceClassification,
						    TFTapasModel,
						    TFTapasPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


