code: 
  
  
  """simple docstring"""
  
  
  
  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 OwlViTImageProcessor
  
  
  
  
  class 			UpperCAmelCase_						(		unittest.TestCase							):
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: Any			,				snake_case_		: str=7			,				snake_case_		: Any=3			,				snake_case_		: Optional[Any]=18			,				snake_case_		: Any=30			,				snake_case_		: List[Any]=400			,				snake_case_		: str=True			,				snake_case_		: Any=None			,				snake_case_		: Dict=True			,				snake_case_		: Optional[Any]=None			,				snake_case_		: Optional[Any]=True			,				snake_case_		: List[str]=[0.4814_5466, 0.457_8275, 0.4082_1073]			,				snake_case_		: Union[str, Any]=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,				snake_case_		: List[str]=True			,				)   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__ =       parent
       A__ =       batch_size
       A__ =       num_channels
       A__ =       image_size
       A__ =       min_resolution
       A__ =       max_resolution
       A__ =       do_resize
       A__ =       size if size is not None else {"height": 18, "width": 18}
       A__ =       do_center_crop
       A__ =       crop_size if crop_size is not None else {"height": 18, "width": 18}
       A__ =       do_normalize
       A__ =       image_mean
       A__ =       image_std
       A__ =       do_convert_rgb
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			UpperCAmelCase_						(		UpperCAmelCase_,     unittest.TestCase							):
      lowercase__		      =   OwlViTImageProcessor if is_vision_available() else None
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       OwlViTImageProcessingTester(self   )
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Union[str, Any]   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
      def 							__magic_name__							(					self		: Dict   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"do_resize"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"size"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"do_center_crop"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"center_crop"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"do_normalize"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"image_mean"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"image_std"   )   )
       self.assertTrue(hasattr(__UpperCamelCase			,				"do_convert_rgb"   )   )
  
  
  
  
      def 							__magic_name__							(					self		: Tuple   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size			,				{"height": 18, "width": 18}   )
       self.assertEqual(image_processor.crop_size			,				{"height": 18, "width": 18}   )
  
       A__ =       self.image_processing_class.from_dict(self.image_processor_dict			,				size=42			,				crop_size=84   )
       self.assertEqual(image_processor.size			,				{"height": 42, "width": 42}   )
       self.assertEqual(image_processor.crop_size			,				{"height": 84, "width": 84}   )
  
  
  
  
      def 							__magic_name__							(					self		: Union[str, Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       A__ =       prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__UpperCamelCase   )
  
       for image in image_inputs:
        self.assertIsInstance(__UpperCamelCase			,				Image.Image   )
  
       # Test not batched input
       A__ =       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__ =       image_processing(__UpperCamelCase			,				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 							__magic_name__							(					self		: int   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       A__ =       prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__UpperCamelCase			,				numpify=__UpperCamelCase   )
       for image in image_inputs:
        self.assertIsInstance(__UpperCamelCase			,				np.ndarray   )
  
       # Test not batched input
       A__ =       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__ =       image_processing(__UpperCamelCase			,				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 							__magic_name__							(					self		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       A__ =       prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__UpperCamelCase			,				torchify=__UpperCamelCase   )
       for image in image_inputs:
        self.assertIsInstance(__UpperCamelCase			,				torch.Tensor   )
  
       # Test not batched input
       A__ =       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__ =       image_processing(__UpperCamelCase			,				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: 
  
  
  
  
  """simple docstring"""
  
  
  
  from manim import *
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
  
  
  
  
      def 							__magic_name__							(					self		: Any   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       Rectangle(height=0.5			,				width=0.5   )
       A__ =       Rectangle(height=0.46			,				width=0.46   ).set_stroke(width=0   )
       A__ =       Rectangle(height=0.25			,				width=0.25   )
  
       A__ =       [mem.copy() for i in range(6   )]
       A__ =       [mem.copy() for i in range(6   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       Text("CPU"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       cpu.move_to([-2.5, -0.5, 0]   )
       self.add(snake_case_   )
  
       A__ =       [mem.copy() for i in range(4   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       Text("GPU"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       gpu.move_to([-1, -1, 0]   )
       self.add(snake_case_   )
  
       A__ =       [mem.copy() for i in range(6   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
  
       A__ =       Text("Model"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       model.move_to([3, -1.0, 0]   )
       self.add(snake_case_   )
  
       A__ =       []
       A__ =       []
  
       for i, rect in enumerate(snake_case_   ):
        rect.set_stroke(snake_case_   )
  
        A__ =       Rectangle(height=0.46 / 4			,				width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(snake_case_			,				opacity=0.7   )
  
        if i == 0:
         cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )			,				buff=0.02			,				direction=snake_case_   )
         cpu_target.set_x(cpu_target.get_x() + 0.1   )
        elif i == 3:
         cpu_target.next_to(model_cpu_arr[0]			,				direction=snake_case_			,				buff=0.0   )
        else:
         cpu_target.next_to(model_cpu_arr[i - 1]			,				direction=snake_case_			,				buff=0.0   )
        self.add(snake_case_   )
        model_cpu_arr.append(snake_case_   )
  
       self.add(*snake_case_			,				*snake_case_   )
  
       A__ =       [meta_mem.copy() for i in range(6   )]
       A__ =       [meta_mem.copy() for i in range(6   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       Text("Disk"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       disk.move_to([-4.0, -1.25, 0]   )
       self.add(snake_case_			,				snake_case_   )
  
       A__ =       []
  
       for i in range(6   ):
        A__ =       fill.copy().set_fill(snake_case_			,				opacity=0.8   )
        target.move_to(disk_left_col_base[i]   ).scale(0.5   )
        cpu_disk_arr.append(snake_case_   )
  
       self.add(*snake_case_   )
  
       A__ =       Square(side_length=2.2   )
       key.move_to([-5, 2, 0]   )
  
       A__ =       MarkupText(
           F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""			,				font_size=18			,				)
  
       key_text.move_to([-5, 2.4, 0]   )
  
       self.add(snake_case_			,				snake_case_   )
  
       A__ =       MarkupText(
           F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""			,				font_size=18			,				)
  
       blue_text.next_to(snake_case_			,				DOWN * 2.4			,				aligned_edge=key_text.get_left()   )
       self.add(snake_case_   )
  
       A__ =       MarkupText(F"""The offloaded weights are all sent to the CPU."""			,				font_size=24   )
       step_a.move_to([2, 2, 0]   )
  
       self.play(Write(snake_case_			,				run_time=3   )   )
  
       for i in range(6   ):
        A__ =       cpu_disk_arr[i]
        A__ =       rect.copy().set_fill(snake_case_			,				opacity=0.8   ).scale(2.0   )
        cpa.generate_target()
        cpa.target.move_to(model_base[i]   )
  
        if i == 0:
         rect.set_fill(snake_case_			,				opacity=0.8   )
         rect.generate_target()
         rect.target.move_to(cpu_left_col_base[0]   ).scale(2.0   )
  
         self.remove(
             *snake_case_			,				*snake_case_			,				)
  
        else:
         rect.generate_target()
         rect.target.move_to(cpu_left_col_base[i]   ).scale(2.0   )
        self.play(MoveToTarget(snake_case_   )			,				MoveToTarget(snake_case_   )			,				model_base[i].animate.set_stroke(snake_case_   )   )
       self.play(FadeOut(snake_case_   )   )
  
       A__ =       MarkupText(
           F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""			,				font_size=24			,				)
       step_a.move_to([2, 2, 0]   )
  
       self.play(Write(snake_case_			,				run_time=3   )   )
  
       A__ =       []
       A__ =       []
       for i in range(6   ):
        A__ =       Arrow(start=snake_case_			,				end=snake_case_			,				color=snake_case_			,				buff=0.5   )
        a.next_to(model_base[i].get_left()			,				snake_case_			,				buff=0.2   )
        arrows.append(snake_case_   )
        animations.append(Write(snake_case_   )   )
       self.play(*snake_case_   )
       self.wait()
  
  
  
  
  
  