


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 ViTImageProcessor





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=13     ,							__UpperCamelCase=3     ,							__UpperCamelCase=224     ,							__UpperCamelCase=30     ,							__UpperCamelCase=400     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							__UpperCamelCase=True     ,							__UpperCamelCase=[0.5, 0.5, 0.5]     ,							__UpperCamelCase=[0.5, 0.5, 0.5]     ,							)       ->    Dict:
          _a						=		size if size is not None else {"height": 18, "width": 18}
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		image_size
          _a						=		min_resolution
          _a						=		max_resolution
          _a						=		do_resize
          _a						=		size
          _a						=		do_normalize
          _a						=		image_mean
          _a						=		image_std






     def  a_					(			self		)       ->    List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }







@require_torch
@require_vision
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				ViTImageProcessor if is_vision_available() else None






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		EfficientFormerImageProcessorTester(self		)






     @property
     def  a_					(			self		)       ->    Optional[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()






     def  a_					(			self		)       ->    List[str]:
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"image_mean"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"image_std"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"do_normalize"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"do_resize"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"size"		)		)






     def  a_					(			self		)       ->    Dict:
          pass






     def  a_					(			self		)       ->    Dict:
          # Initialize image_processor
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          _a						=		prepare_image_inputs(self.image_proc_tester     ,							equal_resolution=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							Image.Image		)

          # Test not batched input
          _a						=		image_processor(image_inputs[0]     ,							return_tensors="pt"		).pixel_values
          self.assertEqual(
              encoded_images.shape     ,							(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )     ,							)

          # Test batched
          _a						=		image_processor(__UpperCamelCase     ,							return_tensors="pt"		).pixel_values
          self.assertEqual(
              encoded_images.shape     ,							(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )     ,							)






     def  a_					(			self		)       ->    str:
          # Initialize image_processor
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          _a						=		prepare_image_inputs(self.image_proc_tester     ,							equal_resolution=__UpperCamelCase     ,							numpify=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							np.ndarray		)

          # Test not batched input
          _a						=		image_processor(image_inputs[0]     ,							return_tensors="pt"		).pixel_values
          self.assertEqual(
              encoded_images.shape     ,							(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )     ,							)

          # Test batched
          _a						=		image_processor(__UpperCamelCase     ,							return_tensors="pt"		).pixel_values
          self.assertEqual(
              encoded_images.shape     ,							(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )     ,							)






     def  a_					(			self		)       ->    List[str]:
          # Initialize image_processor
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          _a						=		prepare_image_inputs(self.image_proc_tester     ,							equal_resolution=__UpperCamelCase     ,							torchify=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							torch.Tensor		)

          # Test not batched input
          _a						=		image_processor(image_inputs[0]     ,							return_tensors="pt"		).pixel_values
          self.assertEqual(
              encoded_images.shape     ,							(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )     ,							)

          # Test batched
          _a						=		image_processor(__UpperCamelCase     ,							return_tensors="pt"		).pixel_values
          self.assertEqual(
              encoded_images.shape     ,							(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )     ,							)





'''simple docstring'''

from manim import *





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def  a_					(			self		)       ->    str:
          _a						=		Rectangle(height=0.5     ,							width=0.5		)
          _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0		)

          _a						=		[mem.copy() for i in range(6		)]
          _a						=		[mem.copy() for i in range(6		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("CPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          cpu.move_to([-2.5, -0.5, 0]		)
          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(1		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("GPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          gpu.align_to(__UpperCamelCase     ,							__UpperCamelCase		)
          gpu.set_x(gpu.get_x() - 1		)

          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(6		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)

          _a						=		Text("Model"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          model.move_to([3, -1.0, 0]		)

          self.play(
              Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							)

          _a						=		MarkupText(
              f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."     ,							font_size=24     ,							)

          _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]		)

          step_a.move_to([2, 2, 0]		)
          self.play(Write(__UpperCamelCase     ,							run_time=2.5		)     ,							Write(__UpperCamelCase		)     ,							Write(__UpperCamelCase		)		)

          self.add(__UpperCamelCase		)

          _a						=		[]
          _a						=		[]
          _a						=		[]
          for i, rect in enumerate(__UpperCamelCase		):
               _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0.0		).set_fill(__UpperCamelCase     ,							opacity=0.7		)
               cpu_target.move_to(__UpperCamelCase		)
               cpu_target.generate_target()
               _a						=		0.46 / 4
               _a						=		0.46 / 3

               if i == 0:
                    cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)     ,							buff=0.02     ,							direction=__UpperCamelCase		)
                    cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
               elif i == 3:
                    cpu_target.target.next_to(cpu_targs[0].target     ,							direction=__UpperCamelCase     ,							buff=0.0		)
               else:
                    cpu_target.target.next_to(cpu_targs[i - 1].target     ,							direction=__UpperCamelCase     ,							buff=0.0		)
               cpu_targs.append(__UpperCamelCase		)

               first_animations.append(rect.animate(run_time=0.5		).set_stroke(__UpperCamelCase		)		)
               second_animations.append(MoveToTarget(__UpperCamelCase     ,							run_time=1.5		)		)

          self.play(*__UpperCamelCase		)
          self.play(*__UpperCamelCase		)

          self.wait()

