




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MobileNetVaForImageClassification, MobileNetVaModel
   from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(_lowerCAmelCase				,				'tf_padding'						)						)
       self.parent.assertTrue(hasattr(_lowerCAmelCase				,				'depth_multiplier'						)						)





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Any=13				,				_lowerCAmelCase  :		Optional[int]=3				,				_lowerCAmelCase  :		Any=32				,				_lowerCAmelCase  :		Optional[Any]=0.25				,				_lowerCAmelCase  :		Dict=8				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		Dict=1_024				,				_lowerCAmelCase  :		Dict=32				,				_lowerCAmelCase  :		Tuple="relu6"				,				_lowerCAmelCase  :		str=0.1				,				_lowerCAmelCase  :		Optional[int]=0.02				,				_lowerCAmelCase  :		Optional[Any]=True				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		int=10				,				_lowerCAmelCase  :		List[Any]=None				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			num_channels
       SCREAMING_SNAKE_CASE_							=			image_size
       SCREAMING_SNAKE_CASE_							=			depth_multiplier
       SCREAMING_SNAKE_CASE_							=			min_depth
       SCREAMING_SNAKE_CASE_							=			tf_padding
       SCREAMING_SNAKE_CASE_							=			int(last_hidden_size * depth_multiplier						)
       SCREAMING_SNAKE_CASE_							=			output_stride
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			classifier_dropout_prob
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			num_labels
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			scope



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.num_labels						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.image_size, self.image_size]				,				self.num_labels						)

       SCREAMING_SNAKE_CASE_							=			self.get_config()

       return config, pixel_values, labels, pixel_labels



 def       lowerCAmelCase_  (							self  :		Dict						):
       return MobileNetVaConfig(
           num_channels=self.num_channels				,				image_size=self.image_size				,				depth_multiplier=self.depth_multiplier				,				min_depth=self.min_depth				,				tf_padding=self.tf_padding				,				hidden_act=self.hidden_act				,				classifier_dropout_prob=self.classifier_dropout_prob				,				initializer_range=self.initializer_range				,				)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			MobileNetVaModel(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(
           result.last_hidden_state.shape				,				(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )				,				)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			self.num_labels
       SCREAMING_SNAKE_CASE_							=			MobileNetVaForImageClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)						)




 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 lowercase_				    =    (
     {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowercase_				    =    False
 lowercase_				    =    False
 lowercase_				    =    False
 lowercase_				    =    False



 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			MobileNetVaModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			MobileNetVaConfigTester(self				,				config_class=_lowerCAmelCase				,				has_text_modality=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='MobileNetV1 does not use inputs_embeds'						)
 def       lowerCAmelCase_  (							self  :		Any						):
       pass



 @unittest.skip(reason='MobileNetV1 does not support input and output embeddings'						)
 def       lowerCAmelCase_  (							self  :		List[str]						):
       pass



 @unittest.skip(reason='MobileNetV1 does not output attentions'						)
 def       lowerCAmelCase_  (							self  :		int						):
       pass



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_							=			model_class(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			inspect.signature(model.forward						)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             SCREAMING_SNAKE_CASE_							=			[*signature.parameters.keys()]

             SCREAMING_SNAKE_CASE_							=			['pixel_values']
             self.assertListEqual(arg_names[:1]				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       def check_hidden_states_output(_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Dict						):
             SCREAMING_SNAKE_CASE_							=			model_class(_lowerCAmelCase						)
             model.to(_lowerCAmelCase						)
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE_							=			model(**self._prepare_for_class(_lowerCAmelCase				,				_lowerCAmelCase						)						)

             SCREAMING_SNAKE_CASE_							=			outputs.hidden_states

             SCREAMING_SNAKE_CASE_							=			26
             self.assertEqual(len(_lowerCAmelCase						)				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_							=			True
             check_hidden_states_output(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase						)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             SCREAMING_SNAKE_CASE_							=			True

             check_hidden_states_output(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)




 @slow
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_							=			MobileNetVaModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)


def 					UpperCAmelCase_							(      )					-> Any:
      SCREAMING_SNAKE_CASE_							=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





@require_torch
@require_vision
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @cached_property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224'						) if is_vision_available() else None
       )




 @slow
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224'						).to(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			self.default_image_processor
       SCREAMING_SNAKE_CASE_							=			prepare_img()
       SCREAMING_SNAKE_CASE_							=			image_processor(images=_lowerCAmelCase				,				return_tensors='pt'						).to(_lowerCAmelCase						)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_							=			model(**_lowerCAmelCase						)

       # verify the logits
       SCREAMING_SNAKE_CASE_							=			torch.Size((1, 1_001)						)
       self.assertEqual(outputs.logits.shape				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			torch.tensor([-4.1739, -1.1233, 3.1205]						).to(_lowerCAmelCase						)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,				_lowerCAmelCase				,				atol=1E-4						)						)





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	str=False							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i in range(config.num_hidden_layers							):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")							)
            rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")							)
            rename_keys.append(
                (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")							)
            rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")							)
            rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")							)
            rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")							)
            rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")							)
            rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")							)
            rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")							)
            rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('module.cls_token', 'vit.embeddings.cls_token'),
              ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
              ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
              ('module.pos_embed', 'vit.embeddings.position_embeddings'),
          ]							)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('module.norm.weight', 'layernorm.weight'),
                    ('module.norm.bias', 'layernorm.bias'),
                ]							)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            SCREAMING_SNAKE_CASE_							=			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'							) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('norm.weight', 'vit.layernorm.weight'),
                    ('norm.bias', 'vit.layernorm.bias'),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]							)

      return rename_keys

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Optional[Any]=False							)					-> Any:
      for i in range(config.num_hidden_layers							):
            if base_model:
                  SCREAMING_SNAKE_CASE_							=			''
            else:
                  SCREAMING_SNAKE_CASE_							=			'vit.'
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE_							=			state_dict.pop(f"module.blocks.{i}.attn.qkv.weight"							)
            SCREAMING_SNAKE_CASE_							=			state_dict.pop(f"module.blocks.{i}.attn.qkv.bias"							)
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE_							=			in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE_							=			in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE_							=			in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_							=			in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE_							=			in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_							=			in_proj_bias[-config.hidden_size :]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]							)					-> str:
      # projection head is used in the self-supervised pre-training in MSN,
      # for downstream task it's not needed.
      SCREAMING_SNAKE_CASE_							=			[
          'module.fc.fc1.weight',
          'module.fc.fc1.bias',
          'module.fc.bn1.weight',
          'module.fc.bn1.bias',
          'module.fc.bn1.running_mean',
          'module.fc.bn1.running_var',
          'module.fc.bn1.num_batches_tracked',
          'module.fc.fc2.weight',
          'module.fc.fc2.bias',
          'module.fc.bn2.weight',
          'module.fc.bn2.bias',
          'module.fc.bn2.running_mean',
          'module.fc.bn2.running_var',
          'module.fc.bn2.num_batches_tracked',
          'module.fc.fc3.weight',
          'module.fc.fc3.bias',
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	Any							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			dct.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			val

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	Tuple							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			ViTMSNConfig()
      SCREAMING_SNAKE_CASE_							=			10_00

      SCREAMING_SNAKE_CASE_							=			'datasets/huggingface/label-files'
      SCREAMING_SNAKE_CASE_							=			'imagenet-1k-id2label.json'
      SCREAMING_SNAKE_CASE_							=			json.load(open(hf_hub_download(__UpperCAmelCase			,			__UpperCAmelCase							)			,			'r'							)							)
      SCREAMING_SNAKE_CASE_							=			{int(__UpperCAmelCase							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_							=			idalabel
      SCREAMING_SNAKE_CASE_							=			{v: k for k, v in idalabel.items()}

      if "s16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			3_84
            SCREAMING_SNAKE_CASE_							=			15_36
            SCREAMING_SNAKE_CASE_							=			6
      elif "l16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			10_24
            SCREAMING_SNAKE_CASE_							=			40_96
            SCREAMING_SNAKE_CASE_							=			24
            SCREAMING_SNAKE_CASE_							=			16
            SCREAMING_SNAKE_CASE_							=			0.1
      elif "b4" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			4
      elif "l7" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			7
            SCREAMING_SNAKE_CASE_							=			10_24
            SCREAMING_SNAKE_CASE_							=			40_96
            SCREAMING_SNAKE_CASE_							=			24
            SCREAMING_SNAKE_CASE_							=			16
            SCREAMING_SNAKE_CASE_							=			0.1

      SCREAMING_SNAKE_CASE_							=			ViTMSNModel(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			torch.hub.load_state_dict_from_url(__UpperCAmelCase			,			map_location='cpu'							)['target_encoder']

      SCREAMING_SNAKE_CASE_							=			ViTImageProcessor(size=config.image_size							)

      remove_projection_head(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			create_rename_keys(__UpperCAmelCase			,			base_model=__UpperCAmelCase							)

      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							)
      read_in_q_k_v(__UpperCAmelCase			,			__UpperCAmelCase			,			base_model=__UpperCAmelCase							)

      model.load_state_dict(__UpperCAmelCase							)
      model.eval()

      SCREAMING_SNAKE_CASE_							=			'http://images.cocodataset.org/val2017/000000039769.jpg'

      SCREAMING_SNAKE_CASE_							=			Image.open(requests.get(__UpperCAmelCase			,			stream=__UpperCAmelCase							).raw							)
      SCREAMING_SNAKE_CASE_							=			ViTImageProcessor(
          size=config.image_size			,			image_mean=__UpperCAmelCase			,			image_std=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			image_processor(images=__UpperCAmelCase			,			return_tensors='pt'							)

      # forward pass
      torch.manual_seed(2							)
      SCREAMING_SNAKE_CASE_							=			model(**__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			outputs.last_hidden_state

      # The following Colab Notebook was used to generate these outputs:
      # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
      if "s16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]							)
      elif "b16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]							)
      elif "l16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]							)
      elif "b4" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]							)
      else:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]							)

      # verify logits
      assert torch.allclose(last_hidden_state[:, 0, :3]			,			__UpperCAmelCase			,			atol=1E-4							)

      print(f"Saving model to {pytorch_dump_folder_path}"							)
      model.save_pretrained(__UpperCAmelCase							)

      print(f"Saving image processor to {pytorch_dump_folder_path}"							)
      image_processor.save_pretrained(__UpperCAmelCase							)


if __name__ == "__main__":
   lowerCamelCase__ :   int					=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
       type=str,
       help='URL of the checkpoint you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   lowerCamelCase__ :   Union[str, Any]					=							parser.parse_args()
   convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)