code: 
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Tuple      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if "model" in orig_key:
  														a_       :   Union[str, Any]			=	orig_key.replace("""model."""	,	""""""      )
  							if "norm1" in orig_key:
  														a_       :   Dict			=	orig_key.replace("""norm1"""	,	"""attention.output.LayerNorm"""      )
  							if "norm2" in orig_key:
  														a_       :   str			=	orig_key.replace("""norm2"""	,	"""output.LayerNorm"""      )
  							if "norm" in orig_key:
  														a_       :   List[str]			=	orig_key.replace("""norm"""	,	"""LayerNorm"""      )
  							if "transformer" in orig_key:
  														a_       :   Any			=	orig_key.split("""."""      )[0].split("""_"""      )[-1]
  														a_       :   Dict			=	orig_key.replace(F"""transformer_{layer_num}"""	,	F"""encoder.layer.{layer_num}"""      )
  							if "mha.attn" in orig_key:
  														a_       :   Dict			=	orig_key.replace("""mha.attn"""	,	"""attention.self"""      )
  							if "mha" in orig_key:
  														a_       :   Union[str, Any]			=	orig_key.replace("""mha"""	,	"""attention"""      )
  							if "W_q" in orig_key:
  														a_       :   str			=	orig_key.replace("""W_q"""	,	"""self.query"""      )
  							if "W_k" in orig_key:
  														a_       :   Any			=	orig_key.replace("""W_k"""	,	"""self.key"""      )
  							if "W_v" in orig_key:
  														a_       :   Any			=	orig_key.replace("""W_v"""	,	"""self.value"""      )
  							if "ff.0" in orig_key:
  														a_       :   str			=	orig_key.replace("""ff.0"""	,	"""intermediate.dense"""      )
  							if "ff.2" in orig_key:
  														a_       :   Any			=	orig_key.replace("""ff.2"""	,	"""output.dense"""      )
  							if "ff" in orig_key:
  														a_       :   Dict			=	orig_key.replace("""ff"""	,	"""output.dense"""      )
  							if "mlm_class" in orig_key:
  														a_       :   Tuple			=	orig_key.replace("""mlm.mlm_class"""	,	"""cls.predictions.decoder"""      )
  							if "mlm" in orig_key:
  														a_       :   List[str]			=	orig_key.replace("""mlm"""	,	"""cls.predictions.transform"""      )
  							if "backbone.backbone.encoders" in orig_key:
  														a_       :   Any			=	orig_key.replace("""backbone.backbone.encoders"""	,	"""encoder.layer"""      )
  							if "cls" not in orig_key:
  														a_       :   Tuple			=	"""mra.""" + orig_key
  
  							return orig_key
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: Optional[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							for key in orig_state_dict.copy().keys():
  														a_       :   Dict			=	orig_state_dict.pop(SCREAMING_SNAKE_CASE_      )
  
  														if ("pooler" in key) or ("sen_class" in key):
  																					continue
  														else:
  																					a_       :   List[Any]			=	val
  
  							a_       :   Optional[int]			=	orig_state_dict["""cls.predictions.decoder.bias"""]
  							a_       :   Union[str, Any]			=	torch.arange(SCREAMING_SNAKE_CASE_      ).expand((1, -1)      ) + 2
  
  							return orig_state_dict
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: List[Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[Any]			=	torch.load(SCREAMING_SNAKE_CASE_	,	map_location="""cpu"""      )["""model_state_dict"""]
  							a_       :   List[Any]			=	MraConfig.from_json_file(SCREAMING_SNAKE_CASE_      )
  							a_       :   List[str]			=	MraForMaskedLM(SCREAMING_SNAKE_CASE_      )
  
  							a_       :   List[str]			=	convert_checkpoint_helper(config.max_position_embeddings	,	SCREAMING_SNAKE_CASE_      )
  
  							print(model.load_state_dict(SCREAMING_SNAKE_CASE_      )      )
  							model.eval()
  							model.save_pretrained(SCREAMING_SNAKE_CASE_      )
  
  							print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
  
  
  if __name__ == "__main__":
  	SCREAMING_SNAKE_CASE : Dict          =    argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
  	)
  	parser.add_argument(
  	    "--config_file",
  	    default=None,
  	    type=str,
  	    required=True,
  	    help="The json file for Mra model config.",
  	)
  	parser.add_argument(
  	    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  	)
  	SCREAMING_SNAKE_CASE : Dict          =    parser.parse_args()
  	convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
style_context: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : str          =    {
      "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
      "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
      "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
      "apple/deeplabv3-mobilevit-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-x-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-xx-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							List[str]         = """mobilevit"""
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(**UpperCamelCase_							)
  
  								a_       :   Optional[int]			=	num_channels
  								a_       :   Optional[Any]			=	image_size
  								a_       :   int			=	patch_size
  								a_       :   List[str]			=	hidden_sizes
  								a_       :   Optional[int]			=	neck_hidden_sizes
  								a_       :   List[Any]			=	num_attention_heads
  								a_       :   Union[str, Any]			=	mlp_ratio
  								a_       :   List[str]			=	expand_ratio
  								a_       :   str			=	hidden_act
  								a_       :   Union[str, Any]			=	conv_kernel_size
  								a_       :   Optional[Any]			=	output_stride
  								a_       :   Union[str, Any]			=	hidden_dropout_prob
  								a_       :   List[Any]			=	attention_probs_dropout_prob
  								a_       :   List[Any]			=	classifier_dropout_prob
  								a_       :   Union[str, Any]			=	initializer_range
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   str			=	qkv_bias
  
  								# decode head attributes for semantic segmentation
  								a_       :   Optional[Any]			=	aspp_out_channels
  								a_       :   Any			=	atrous_rates
  								a_       :   Union[str, Any]			=	aspp_dropout_prob
  								a_       :   Any			=	semantic_loss_ignore_index
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.task == "image-classification":
  															return OrderedDict([("""logits""", {0: """batch"""})]							)
  								else:
  															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			float:
  								"""simple docstring"""
  
  
  
  
  
  
  								return 1e-4
  
  
  