






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      :      Any             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  RobertaPreLayerNormConfig.from_pretrained(
						    UpperCamelCase_      ,    architectures=['RobertaPreLayerNormForMaskedLM']						)

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

												lowerCAmelCase__        =  tensor_value

						lowerCAmelCase__        =  RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=UpperCamelCase_      ,    config=UpperCamelCase_      ,    state_dict=UpperCamelCase_						)
						model.save_pretrained(UpperCamelCase_						)

						# convert tokenizer
						lowerCAmelCase__        =  AutoTokenizer.from_pretrained(UpperCamelCase_						)
						tokenizer.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case      :      Tuple             =       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      :      Dict             =       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_tokenizers_available,
    is_torch_available,
)


__snake_case      :      Union[str, Any]             =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      List[str]             =       ["""ConvBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Optional[Any]             =       [
					    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvBertForMaskedLM""",
					    """ConvBertForMultipleChoice""",
					    """ConvBertForQuestionAnswering""",
					    """ConvBertForSequenceClassification""",
					    """ConvBertForTokenClassification""",
					    """ConvBertLayer""",
					    """ConvBertModel""",
					    """ConvBertPreTrainedModel""",
					    """load_tf_weights_in_convbert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Union[str, Any]             =       [
					    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFConvBertForMaskedLM""",
					    """TFConvBertForMultipleChoice""",
					    """TFConvBertForQuestionAnswering""",
					    """TFConvBertForSequenceClassification""",
					    """TFConvBertForTokenClassification""",
					    """TFConvBertLayer""",
					    """TFConvBertModel""",
					    """TFConvBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

					__snake_case      :      Dict             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





