code: 
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				"hybrid-clip"
       __SCREAMING_SNAKE_CASE						    =				True
  
  
       def __init__(		self				,		__lowerCamelCase=5_1_2				,		**__lowerCamelCase)    ->       str:
              super().__init__(**__lowerCamelCase)
  
              if "text_config" not in kwargs:
                     raise ValueError("`text_config` can not be `None`.")
  
              if "vision_config" not in kwargs:
                     raise ValueError("`vision_config` can not be `None`.")
  
              _A   : Optional[int]       					=				kwargs.pop("text_config")
              _A   : Union[str, Any]       					=				kwargs.pop("vision_config")
  
              _A   : Dict       					=				text_config.pop("model_type")
              _A   : Optional[int]       					=				vision_config.pop("model_type")
  
              from transformers import AutoConfig
  
              _A   : List[Any]       					=				AutoConfig.for_model(__lowerCamelCase				,		**__lowerCamelCase)
  
              if vision_model_type == "clip":
                     _A   : Any       					=				AutoConfig.for_model(__lowerCamelCase				,		**__lowerCamelCase).vision_config
              elif vision_model_type == "clip_vision_model":
                     from transformers import CLIPVisionConfig
  
                     _A   : Tuple       					=				CLIPVisionConfig(**__lowerCamelCase)
              else:
                     _A   : List[str]       					=				AutoConfig.for_model(__lowerCamelCase				,		**__lowerCamelCase)
  
              _A   : int       					=				projection_dim
              _A   : str       					=				1.0
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       Union[str, Any]:
              return cls(text_config=text_config.to_dict()				,		vision_config=vision_config.to_dict()				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       int:
              _A   : Dict       					=				copy.deepcopy(self.__dict__)
              _A   : Union[str, Any]       					=				self.text_config.to_dict()
              _A   : Dict       					=				self.vision_config.to_dict()
              _A   : List[Any]       					=				self.__class__.model_type
              return output
  
style_context: 
  
  
  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"],
                  )				,		)
  