





'''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()
_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  str				):
			__lowercase										=							RobertaPreLayerNormConfig.from_pretrained(
			    lowerCamelCase_		,       architectures=['''RobertaPreLayerNormForMaskedLM''']				)

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

						__lowercase										=							tensor_value

			__lowercase										=							RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=lowerCamelCase_		,       config=lowerCamelCase_		,       state_dict=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)

			# convert tokenizer
			__lowercase										=							AutoTokenizer.from_pretrained(lowerCamelCase_				)
			tokenizer.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_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.'''
	)
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					=    {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''VanForImageClassification''',
	    '''VanModel''',
	    '''VanPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





