
"""simple docstring"""




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = 32 ,			__UpperCamelCase = True ,			__UpperCamelCase = 1 / 2_55 ,			__UpperCamelCase = True ,			__UpperCamelCase = True ,			__UpperCamelCase = [0.4814_5466, 0.457_8275, 0.4082_1073] ,			__UpperCamelCase = [0.2686_2954, 0.2613_0258, 0.2757_7711] ,			__UpperCamelCase = True ,			__UpperCamelCase=7 ,			__UpperCamelCase=30 ,			__UpperCamelCase=4_00 ,			__UpperCamelCase=3 ,			)->					List[str]:
          UpperCAmelCase__							: List[str] 							=						parent
          UpperCAmelCase__							: Any 							=						do_resize
          UpperCAmelCase__							: Tuple 							=						size if size is not None else {"shortest_edge": 2_88}
          UpperCAmelCase__							: int 							=						size_divisor
          UpperCAmelCase__							: Optional[int] 							=						do_rescale
          UpperCAmelCase__							: int 							=						rescale_factor
          UpperCAmelCase__							: Optional[int] 							=						do_normalize
          UpperCAmelCase__							: List[Any] 							=						do_center_crop
          UpperCAmelCase__							: Optional[int] 							=						image_mean
          UpperCAmelCase__							: Any 							=						image_std
          UpperCAmelCase__							: Union[str, Any] 							=						do_pad
          UpperCAmelCase__							: str 							=						batch_size
          UpperCAmelCase__							: Union[str, Any] 							=						num_channels
          UpperCAmelCase__							: str 							=						min_resolution
          UpperCAmelCase__							: Optional[Any] 							=						max_resolution



     def  lowerCAmelCase__       (      self  )->					Tuple:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False  )->					Dict:
          if not batched:
               UpperCAmelCase__							: str 							=						self.size["shortest_edge"]
               UpperCAmelCase__							: List[Any] 							=						image_inputs[0]
               if isinstance(__UpperCamelCase ,			Image.Image  ):
                    UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						image.size
               else:
                    UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						image.shape[1], image.shape[2]
               UpperCAmelCase__							: List[str] 							=						size / min(__UpperCamelCase ,			__UpperCamelCase  )
               if h < w:
                    UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						size, scale * w
               else:
                    UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						scale * h, size

               UpperCAmelCase__							: List[Any] 							=						int((13_33 / 8_00) * size  )
               if max(__UpperCamelCase ,			__UpperCamelCase  ) > max_size:
                    UpperCAmelCase__							: int 							=						max_size / max(__UpperCamelCase ,			__UpperCamelCase  )
                    UpperCAmelCase__							: Any 							=						newh * scale
                    UpperCAmelCase__							: Union[str, Any] 							=						neww * scale

               UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						int(newh + 0.5  ), int(neww + 0.5  )
               UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

          else:
               UpperCAmelCase__							: Optional[Any] 							=						[]
               for image in image_inputs:
                    UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
               UpperCAmelCase__							: List[str] 							=						max(__UpperCamelCase ,			key=lambda __UpperCamelCase  : item[0]  )[0]
               UpperCAmelCase__							: Optional[Any] 							=						max(__UpperCamelCase ,			key=lambda __UpperCamelCase  : item[1]  )[1]

          return expected_height, expected_width







@require_torch
@require_vision
class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							BridgeTowerImageProcessor if is_vision_available() else None



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: str 							=						BridgeTowerImageProcessingTester(self  )



     @property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Optional[int] 							=						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"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"size_divisor"  )  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          pass



     def  lowerCAmelCase__       (      self  )->					Any:
          # Initialize image processor
          UpperCAmelCase__							: str 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          UpperCAmelCase__							: int 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__UpperCamelCase  )
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase ,			Image.Image  )

          # Test not batched input
          UpperCAmelCase__							: str 							=						image_processing(image_inputs[0] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase  )
          self.assertEqual(
              encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

          # Test batched
          UpperCAmelCase__							: Union[str, Any] 							=						image_processing(__UpperCamelCase ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase ,			batched=__UpperCamelCase  )
          self.assertEqual(
              encoded_images.shape ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,			)



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          # Initialize image processor
          UpperCAmelCase__							: Dict 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          UpperCAmelCase__							: Optional[int] 							=						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
          UpperCAmelCase__							: Dict 							=						image_processing(image_inputs[0] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase  )
          self.assertEqual(
              encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

          # Test batched
          UpperCAmelCase__							: Optional[int] 							=						image_processing(__UpperCamelCase ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase ,			batched=__UpperCamelCase  )
          self.assertEqual(
              encoded_images.shape ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,			)



     def  lowerCAmelCase__       (      self  )->					Any:
          # Initialize image processor
          UpperCAmelCase__							: Tuple 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          UpperCAmelCase__							: Any 							=						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
          UpperCAmelCase__							: Any 							=						image_processing(image_inputs[0] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase  )
          self.assertEqual(
              encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)

          # Test batched
          UpperCAmelCase__							: Optional[int] 							=						image_processing(__UpperCamelCase ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase ,			batched=__UpperCamelCase  )
          self.assertEqual(
              encoded_images.shape ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,			)





"""simple docstring"""




from manim import *







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: str 							=						Rectangle(height=0.5 ,			width=0.5  )
          UpperCAmelCase__							: List[str] 							=						Rectangle(height=0.46 ,			width=0.46  ).set_stroke(width=0  )

          UpperCAmelCase__							: List[Any] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: Union[str, Any] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: List[Any] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: int 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: Tuple 							=						VGroup(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: Tuple 							=						Text("CPU" ,			font_size=24  )
          UpperCAmelCase__							: Any 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          cpu.move_to([-2.5, -0.5, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						[mem.copy() for i in range(4  )]
          UpperCAmelCase__							: List[str] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: Union[str, Any] 							=						Text("GPU" ,			font_size=24  )
          UpperCAmelCase__							: Dict 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          gpu.move_to([-1, -1, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: List[str] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )

          UpperCAmelCase__							: Tuple 							=						Text("Model" ,			font_size=24  )
          UpperCAmelCase__							: Dict 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          model.move_to([3, -1.0, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						[]
          for i, rect in enumerate(__UpperCamelCase  ):
               rect.set_stroke(__UpperCamelCase  )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               UpperCAmelCase__							: int 							=						Rectangle(height=0.46 / 4 ,			width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(__UpperCamelCase ,			opacity=0.7  )

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

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

          UpperCAmelCase__							: Tuple 							=						Text("Loaded Checkpoint" ,			font_size=24  )
          UpperCAmelCase__							: Any 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			aligned_edge=__UpperCamelCase ,			buff=0.4  )
          checkpoint.move_to([3, 0.5, 0]  )

          UpperCAmelCase__							: Optional[Any] 							=						Square(side_length=2.2  )
          key.move_to([-5, 2, 0]  )

          UpperCAmelCase__							: Any 							=						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(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						MarkupText(
              F"<span fgcolor='{BLUE}'>●</span> Checkpoint" ,			font_size=18 ,			)

          blue_text.next_to(__UpperCamelCase ,			DOWN * 2.4 ,			aligned_edge=key_text.get_left()  )

          UpperCAmelCase__							: Optional[Any] 							=						MarkupText(
              F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." ,			font_size=24 ,			)
          step_a.move_to([2, 2, 0]  )
          self.play(Write(__UpperCamelCase  ) ,			Write(__UpperCamelCase  )  )

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

          UpperCAmelCase__							: Union[str, Any] 							=						[]
          UpperCAmelCase__							: List[str] 							=						[]
          for i, rect in enumerate(__UpperCamelCase  ):
               UpperCAmelCase__							: Optional[Any] 							=						fill.copy().set_fill(__UpperCamelCase ,			opacity=0.7  )
               target.move_to(__UpperCamelCase  )
               first_animations.append(GrowFromCenter(__UpperCamelCase ,			run_time=1  )  )

               UpperCAmelCase__							: List[str] 							=						target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
               second_animations.append(MoveToTarget(__UpperCamelCase ,			run_time=1.5  )  )

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



