




'''simple docstring'''



import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_    	=				logging.get_logger(__name__)




def   lowercase__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






      print('Loading config file...'    )

      def flatten_yaml_as_dict(__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Optional[Any]=""      ,__UpperCamelCase:					List[str]="."    ):
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
            for k, v in d.items():
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						parent_key + sep + k if parent_key else k
                  if isinstance(__UpperCamelCase      ,collections.abc.MutableMapping    ):
                        items.extend(flatten_yaml_as_dict(__UpperCamelCase      ,__UpperCamelCase      ,sep=__UpperCamelCase    ).items()    )
                  else:
                        items.append((new_key, v)    )
            return dict(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Tuple    					=						argparse.Namespace()
      with open(__UpperCamelCase      ,'r'    ) as yaml_file:
            try:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						yaml.load(__UpperCamelCase      ,Loader=yaml.FullLoader    )

                  SCREAMING_SNAKE_CASE   :      Any    					=						flatten_yaml_as_dict(__UpperCamelCase    )
                  for k, v in flat_cfg.items():
                        setattr(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
            except yaml.YAMLError as exc:
                  logger.error('Error while loading config file: {}. Error message: {}'.format(__UpperCamelCase      ,str(__UpperCamelCase    )    )    )
      return config




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						MobileViTVaConfig()

      SCREAMING_SNAKE_CASE   :      Any    					=						False

      # dataset
      if task_name.startswith('imagenet1k_'    ):
            SCREAMING_SNAKE_CASE   :      Dict    					=						10_00
            if int(task_name.strip().split('_'    )[-1]    ) == 3_84:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						3_84
            else:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						2_56
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'imagenet-1k-id2label.json'
      elif task_name.startswith('imagenet21k_to_1k_'    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						2_10_00
            if int(task_name.strip().split('_'    )[-1]    ) == 3_84:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						3_84
            else:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						2_56
            SCREAMING_SNAKE_CASE   :      Dict    					=						'imagenet-22k-id2label.json'
      elif task_name.startswith('ade20k_'    ):
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						1_51
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						5_12
            SCREAMING_SNAKE_CASE   :      Dict    					=						'ade20k-id2label.json'
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						True
      elif task_name.startswith('voc_'    ):
            SCREAMING_SNAKE_CASE   :      Tuple    					=						21
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						5_12
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'pascal-voc-id2label.json'
            SCREAMING_SNAKE_CASE   :      Dict    					=						True

      # orig_config
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						load_orig_config_file(__UpperCamelCase    )
      assert getattr(__UpperCamelCase      ,'model.classification.name'      ,-1    ) == "mobilevit_v2", "Invalid model"
      SCREAMING_SNAKE_CASE   :      Tuple    					=						getattr(__UpperCamelCase      ,'model.classification.mitv2.width_multiplier'      ,1.0    )
      assert (
          getattr(__UpperCamelCase      ,'model.classification.mitv2.attn_norm_layer'      ,-1    ) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      SCREAMING_SNAKE_CASE   :      int    					=						getattr(__UpperCamelCase      ,'model.classification.activation.name'      ,'swish'    )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						getattr(__UpperCamelCase      ,'model.segmentation.output_stride'      ,16    )
            if "_deeplabv3" in task_name:
                  SCREAMING_SNAKE_CASE   :      str    					=						getattr(__UpperCamelCase      ,'model.segmentation.deeplabv3.aspp_rates'      ,[12, 24, 36]    )
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						getattr(__UpperCamelCase      ,'model.segmentation.deeplabv3.aspp_out_channels'      ,5_12    )
                  SCREAMING_SNAKE_CASE   :      int    					=						getattr(__UpperCamelCase      ,'model.segmentation.deeplabv3.aspp_dropout'      ,0.1    )

    # id2label
      SCREAMING_SNAKE_CASE   :      List[str]    					=						'huggingface/label-files'
      SCREAMING_SNAKE_CASE   :      Dict    					=						json.load(open(hf_hub_download(__UpperCamelCase      ,__UpperCamelCase      ,repo_type='dataset'    )      ,'r'    )    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						{int(__UpperCamelCase    ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						idalabel
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{v: k for k, v in idalabel.items()}

      return config




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						dct.pop(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Any    					=						val




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[str]=False    ):

      """simple docstring"""






      if base_model:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						''
      else:
            SCREAMING_SNAKE_CASE   :      List[str]    					=						'mobilevitv2.'

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  SCREAMING_SNAKE_CASE   :      int    					=						k[8:]
            else:
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						k

            if ".block." in k:
                  SCREAMING_SNAKE_CASE   :      str    					=						k_new.replace('.block.'      ,'.'    )
            if ".conv." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('.conv.'      ,'.convolution.'    )
            if ".norm." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('.norm.'      ,'.normalization.'    )

            if "conv_1." in k:
                  SCREAMING_SNAKE_CASE   :      Any    					=						k_new.replace('conv_1.'      ,f"{model_prefix}conv_stem."    )
            for i in [1, 2]:
                  if f"layer_{i}." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}."      ,f"{model_prefix}encoder.layer.{i-1}.layer."    )
            if ".exp_1x1." in k:
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace('.exp_1x1.'      ,'.expand_1x1.'    )
            if ".red_1x1." in k:
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						k_new.replace('.red_1x1.'      ,'.reduce_1x1.'    )

            for i in [3, 4, 5]:
                  if f"layer_{i}.0." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}.0."      ,f"{model_prefix}encoder.layer.{i-1}.downsampling_layer."    )
                  if f"layer_{i}.1.local_rep.0." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}.1.local_rep.0."      ,f"{model_prefix}encoder.layer.{i-1}.conv_kxk."    )
                  if f"layer_{i}.1.local_rep.1." in k:
                        SCREAMING_SNAKE_CASE   :      Dict    					=						k_new.replace(f"layer_{i}.1.local_rep.1."      ,f"{model_prefix}encoder.layer.{i-1}.conv_1x1."    )

            for i in [3, 4, 5]:
                  if i == 3:
                        SCREAMING_SNAKE_CASE   :      int    					=						[0, 1]
                  elif i == 4:
                        SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[0, 1, 2, 3]
                  elif i == 5:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						[0, 1, 2]

                  for j in j_in:
                        if f"layer_{i}.1.global_rep.{j}." in k:
                              SCREAMING_SNAKE_CASE   :      Tuple    					=						k_new.replace(
                                  f"layer_{i}.1.global_rep.{j}."      ,f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."    )
                  if f"layer_{i}.1.global_rep.{j+1}." in k:
                        SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace(
                            f"layer_{i}.1.global_rep.{j+1}."      ,f"{model_prefix}encoder.layer.{i-1}.layernorm."    )

                  if f"layer_{i}.1.conv_proj." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}.1.conv_proj."      ,f"{model_prefix}encoder.layer.{i-1}.conv_projection."    )

            if "pre_norm_attn.0." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('pre_norm_attn.0.'      ,'layernorm_before.'    )
            if "pre_norm_attn.1." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('pre_norm_attn.1.'      ,'attention.'    )
            if "pre_norm_ffn.0." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('pre_norm_ffn.0.'      ,'layernorm_after.'    )
            if "pre_norm_ffn.1." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('pre_norm_ffn.1.'      ,'ffn.conv1.'    )
            if "pre_norm_ffn.3." in k:
                  SCREAMING_SNAKE_CASE   :      int    					=						k_new.replace('pre_norm_ffn.3.'      ,'ffn.conv2.'    )

            if "classifier.1." in k:
                  SCREAMING_SNAKE_CASE   :      int    					=						k_new.replace('classifier.1.'      ,'classifier.'    )

            if "seg_head." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('seg_head.'      ,'segmentation_head.'    )
            if ".aspp_layer." in k:
                  SCREAMING_SNAKE_CASE   :      Any    					=						k_new.replace('.aspp_layer.'      ,'.'    )
            if ".aspp_pool." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('.aspp_pool.'      ,'.'    )

            rename_keys.append((k, k_new)    )
      return rename_keys




def   lowercase__(			__UpperCamelCase:					Tuple    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
      for k in state_dict.keys():
            if k.startswith('seg_head.aux_head.'    ):
                  keys_to_ignore.append(__UpperCamelCase    )
      for k in keys_to_ignore:
            state_dict.pop(__UpperCamelCase      ,__UpperCamelCase    )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						'http://images.cocodataset.org/val2017/000000039769.jpg'
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Image.open(requests.get(__UpperCamelCase      ,stream=__UpperCamelCase    ).raw    )
      return im




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						get_mobilevitva_config(__UpperCamelCase      ,__UpperCamelCase    )

      # load original state_dict
      SCREAMING_SNAKE_CASE   :      Dict    					=						torch.load(__UpperCamelCase      ,map_location='cpu'    )

      # load huggingface model
      if task_name.startswith('ade20k_'    ) or task_name.startswith('voc_'    ):
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						MobileViTVaForSemanticSegmentation(__UpperCamelCase    ).eval()
            SCREAMING_SNAKE_CASE   :      List[str]    					=						False
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						MobileViTVaForImageClassification(__UpperCamelCase    ).eval()
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False

      # remove and rename some keys of load the original model
      SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint
      remove_unused_keys(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						create_rename_keys(__UpperCamelCase      ,base_model=__UpperCamelCase    )
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )

      # load modified state_dict
      model.load_state_dict(__UpperCamelCase    )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						MobileViTImageProcessor(crop_size=config.image_size      ,size=config.image_size + 32    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_processor(images=prepare_img()      ,return_tensors='pt'    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						model(**__UpperCamelCase    )

      # verify classification model
      if task_name.startswith('imagenet'    ):
            SCREAMING_SNAKE_CASE   :      Any    					=						outputs.logits
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						logits.argmax(-1    ).item()
            print('Predicted class:'      ,model.config.idalabel[predicted_class_idx]    )
            if task_name.startswith('imagenet1k_256'    ) and config.width_multiplier == 1.0:
                  # expected_logits for base variant
                  SCREAMING_SNAKE_CASE   :      Any    					=						torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]    )
                  assert torch.allclose(logits[0, :3]      ,__UpperCamelCase      ,atol=1e-4    )

      Path(__UpperCamelCase    ).mkdir(exist_ok=__UpperCamelCase    )
      print(f"Saving model {task_name} 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__":
      UpperCamelCase_    	=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--task",
          default="imagenet1k_256",
          type=str,
          help=(
              "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
              "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
          ),
          choices=[
              "imagenet1k_256",
              "imagenet1k_384",
              "imagenet21k_to_1k_256",
              "imagenet21k_to_1k_384",
              "ade20k_deeplabv3",
              "voc_deeplabv3",
          ],
      )

      parser.add_argument(
          "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
      )
      parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
      parser.add_argument(
          "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
      )

      UpperCamelCase_    	=				parser.parse_args()
      convert_mobilevitva_checkpoint(
          args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
      )








'''simple docstring'''



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCamelCase_    	=				get_tests_dir("fixtures")






class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						mock.Mock()
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						500
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      Any    					=						HTTPError
             SCREAMING_SNAKE_CASE   :      Any    					=						{}

             # Download this model to make sure it's in the cache.
             SCREAMING_SNAKE_CASE   :      str    					=						ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
             # Under the mock environment we get a 500 error when trying to reach the model.
             with mock.patch('requests.Session.request',							return_value=A  ) as mock_head:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
                   # This check we did call the fake head request
                   mock_head.assert_called()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						ViTImageProcessor.from_pretrained(
                 'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             with self.assertRaises(A  ):
                   # config is in subfolder, the following should not work without specifying the subfolder
                   SCREAMING_SNAKE_CASE   :      str    					=						AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/stable-diffusion-all-variants',							subfolder='feature_extractor'  )

             self.assertIsNotNone(A  )






@is_staging_test
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						TOKEN
             HfFolder.save_token(A  )







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             try:
                   delete_repo(token=cls._token,							repo_id='test-image-processor'  )
             except HTTPError:
                   pass

             try:
                   delete_repo(token=cls._token,							repo_id='valid_org/test-image-processor-org'  )
             except HTTPError:
                   pass

             try:
                   delete_repo(token=cls._token,							repo_id='test-dynamic-image-processor'  )
             except HTTPError:
                   pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ViTImageProcessor.from_pretrained(A  )
             image_processor.push_to_hub('test-image-processor',							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      int    					=						ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )

             # Reset repo
             delete_repo(token=self._token,							repo_id='test-image-processor'  )

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(
                       A,							repo_id='test-image-processor',							push_to_hub=A,							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						ViTImageProcessor.from_pretrained(A  )
             image_processor.push_to_hub('valid_org/test-image-processor',							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      str    					=						ViTImageProcessor.from_pretrained('valid_org/test-image-processor'  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )

             # Reset repo
             delete_repo(token=self._token,							repo_id='valid_org/test-image-processor'  )

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(
                       A,							repo_id='valid_org/test-image-processor-org',							push_to_hub=A,							use_auth_token=self._token  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'  )
             for k, v in image_processor.__dict__.items():
                   self.assertEqual(A,							getattr(A,							A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             CustomImageProcessor.register_for_auto_class()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CustomImageProcessor.from_pretrained(A  )

             image_processor.push_to_hub('test-dynamic-image-processor',							use_auth_token=self._token  )

             # This has added the proper auto_map field to the config
             self.assertDictEqual(
                 image_processor.auto_map,							{'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'},							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoImageProcessor.from_pretrained(
                 F"{USER}/test-dynamic-image-processor",							trust_remote_code=A  )
             # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
             self.assertEqual(new_image_processor.__class__.__name__,							'CustomImageProcessor'  )


