






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case__   :						Dict													=   {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						List[Any]													=   ["""EfficientFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Optional[int]													=   [
     """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """EfficientFormerForImageClassification""",
     """EfficientFormerForImageClassificationWithTeacher""",
     """EfficientFormerModel""",
     """EfficientFormerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   [
     """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFEfficientFormerForImageClassification""",
     """TFEfficientFormerForImageClassificationWithTeacher""",
     """TFEfficientFormerModel""",
     """TFEfficientFormerPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientformer import EfficientFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
      EfficientFormerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
      TFEfficientFormerPreTrainedModel,
  )

else:
 import sys

 snake_case__   :						Tuple													=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












"""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()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	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 :						List[Any]      =							"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 :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


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


