code: 
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
      import torch
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import MobileNetVaImageProcessor
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=7				,		__lowerCamelCase=3				,		__lowerCamelCase=1_8				,		__lowerCamelCase=3_0				,		__lowerCamelCase=4_0_0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
              _A   : int       					=				size if size is not None else {"shortest_edge": 2_0}
              _A   : Tuple       					=				crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
              _A   : str       					=				parent
              _A   : Any       					=				batch_size
              _A   : Dict       					=				num_channels
              _A   : int       					=				image_size
              _A   : Union[str, Any]       					=				min_resolution
              _A   : Tuple       					=				max_resolution
              _A   : Any       					=				do_resize
              _A   : Optional[int]       					=				size
              _A   : Tuple       					=				do_center_crop
              _A   : List[str]       					=				crop_size
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
              }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class   lowerCAmelCase__ (       a  ,					unittest.TestCase):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				MobileNetVaImageProcessor if is_vision_available() else None
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              _A   : Dict       					=				MobileNetVaImageProcessingTester(self)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Dict:
              return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_resize"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"size"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_center_crop"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"center_crop"))
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : int       					=				self.image_processing_class.from_dict(self.image_processor_dict)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 2_0})
              self.assertEqual(image_processor.crop_size				,		{"height": 1_8, "width": 1_8})
  
              _A   : Any       					=				self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2				,		crop_size=8_4)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 4_2})
              self.assertEqual(image_processor.crop_size				,		{"height": 8_4, "width": 8_4})
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              pass
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              # Initialize image_processing
              _A   : Optional[int]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PIL images
              _A   : List[Any]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		Image.Image)
  
              # Test not batched input
              _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Dict       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              # Initialize image_processing
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random numpy tensors
              _A   : int       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		numpify=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		np.ndarray)
  
              # Test not batched input
              _A   : Tuple       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : str       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              # Initialize image_processing
              _A   : str       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PyTorch tensors
              _A   : List[str]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		torchify=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		torch.Tensor)
  
              # Test not batched input
              _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Any       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
                if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				argparse.ArgumentParser()
  
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
          type=str,
          required=False,
          help='Path to the checkpoint to convert.',
      )
  
      parser.add_argument(
          '--config_file',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
          type=str,
          required=False,
          help='The config json file corresponding to the architecture.',
      )
  
      parser.add_argument(
          '--dump_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
          type=str,
          required=False,
          help='Path to the output model.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  