


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor







class  snake_case_     (unittest.TestCase   ):
					def __init__(   self     :Optional[int]    ,__snake_case     :Optional[Any]    ,__snake_case     :str=7    ,__snake_case     :int=3    ,__snake_case     :Dict=30    ,__snake_case     :List[str]=4_00    ,__snake_case     :Optional[Any]=True    ,__snake_case     :List[Any]=None    ,__snake_case     :List[str]=True    ,__snake_case     :List[Any]=[0.5, 0.5, 0.5]    ,__snake_case     :List[str]=[0.5, 0.5, 0.5]    ,__snake_case     :Optional[int]=True    ,__snake_case     :str=1 / 2_55    ,__snake_case     :int=True    ,)      ->					Dict:
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												a__    =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
												a__    =  parent
												a__    =  batch_size
												a__    =  num_channels
												a__    =  min_resolution
												a__    =  max_resolution
												a__    =  do_resize
												a__    =  size
												a__    =  do_normalize
												a__    =  image_mean
												a__    =  image_std
												a__    =  do_rescale
												a__    =  rescale_factor
												a__    =  do_pad
					def 				lowerCamelCase__(   self     :Tuple   )      ->					int:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}



					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Dict    ,__snake_case     :List[str]=False   )      ->					List[Any]:
												if not batched:
																			a__    =  image_inputs[0]
																			if isinstance(__snake_case    ,Image.Image   ):
																										a__   ,      a__    =  image.size
																			else:
																										a__   ,      a__    =  image.shape[1], image.shape[2]
																			if w < h:
																										a__    =  int(self.size['shortest_edge'] * h / w   )
																										a__    =  self.size['shortest_edge']
																			elif w > h:
																										a__    =  self.size['shortest_edge']
																										a__    =  int(self.size['shortest_edge'] * w / h   )
																			else:
																										a__    =  self.size['shortest_edge']
																										a__    =  self.size['shortest_edge']

												else:
																			a__    =  []
																			for image in image_inputs:
																										a__   ,      a__    =  self.get_expected_values([image]   )
																										expected_values.append((expected_height, expected_width)   )
																			a__    =  max(__snake_case    ,key=lambda __snake_case   : item[0]   )[0]
																			a__    =  max(__snake_case    ,key=lambda __snake_case   : item[1]   )[1]

												return expected_height, expected_width







@require_torch
@require_vision
class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					List[Any]					   =    ConditionalDetrImageProcessor if is_vision_available() else None
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[Any]:
												a__    =  ConditionalDetrImageProcessingTester(self   )
					@property
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Any:
												return self.image_processor_tester.prepare_image_processor_dict()
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Optional[Any]:
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(__snake_case    ,'image_mean'   )   )
												self.assertTrue(hasattr(__snake_case    ,'image_std'   )   )
												self.assertTrue(hasattr(__snake_case    ,'do_normalize'   )   )
												self.assertTrue(hasattr(__snake_case    ,'do_resize'   )   )
												self.assertTrue(hasattr(__snake_case    ,'size'   )   )
					def 				lowerCamelCase__(   self     :int   )      ->					str:
												a__    =  self.image_processing_class.from_dict(self.image_processor_dict   )
												self.assertEqual(image_processor.size    ,{'shortest_edge': 18, 'longest_edge': 13_33}   )
												self.assertEqual(image_processor.do_pad    ,__snake_case   )

												a__    =  self.image_processing_class.from_dict(
												    self.image_processor_dict    ,size=42    ,max_size=84    ,pad_and_return_pixel_mask=__snake_case   )
												self.assertEqual(image_processor.size    ,{'shortest_edge': 42, 'longest_edge': 84}   )
												self.assertEqual(image_processor.do_pad    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Union[str, Any]:
												pass
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					List[str]:
												# Initialize image_processing
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												a__    =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=__snake_case   )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case    ,Image.Image   )

												# Test not batched input
												a__    =  image_processing(image_inputs[0]    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  self.image_processor_tester.get_expected_values(__snake_case   )

												self.assertEqual(
												    encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

												# Test batched
												a__   ,      a__    =  self.image_processor_tester.get_expected_values(__snake_case    ,batched=__snake_case   )

												a__    =  image_processing(__snake_case    ,return_tensors='pt'   ).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )    ,)
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[str]:
												# Initialize image_processing
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												a__    =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=__snake_case    ,numpify=__snake_case   )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case    ,np.ndarray   )

												# Test not batched input
												a__    =  image_processing(image_inputs[0]    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  self.image_processor_tester.get_expected_values(__snake_case   )

												self.assertEqual(
												    encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

												# Test batched
												a__    =  image_processing(__snake_case    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  self.image_processor_tester.get_expected_values(__snake_case    ,batched=__snake_case   )

												self.assertEqual(
												    encoded_images.shape    ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )    ,)
					def 				lowerCamelCase__(   self     :int   )      ->					Tuple:
												# Initialize image_processing
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												a__    =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=__snake_case    ,torchify=__snake_case   )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case    ,torch.Tensor   )

												# Test not batched input
												a__    =  image_processing(image_inputs[0]    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  self.image_processor_tester.get_expected_values(__snake_case   )

												self.assertEqual(
												    encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

												# Test batched
												a__    =  image_processing(__snake_case    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  self.image_processor_tester.get_expected_values(__snake_case    ,batched=__snake_case   )

												self.assertEqual(
												    encoded_images.shape    ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )    ,)
					@slow
					def 				lowerCamelCase__(   self     :Dict   )      ->					Optional[Any]:
												# prepare image and target
												a__    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
												with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,'r'   ) as f:
																			a__    =  json.loads(f.read()   )

												a__    =  {'image_id': 3_97_69, 'annotations': target}

												# encode them
												a__    =  ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'   )
												a__    =  image_processing(images=__snake_case    ,annotations=__snake_case    ,return_tensors='pt'   )

												# verify pixel values
												a__    =  torch.Size([1, 3, 8_00, 10_66]   )
												self.assertEqual(encoding['pixel_values'].shape    ,__snake_case   )

												a__    =  torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,__snake_case    ,atol=1E-4   )   )

												# verify area
												a__    =  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,__snake_case   )   )
												# verify boxes
												a__    =  torch.Size([6, 4]   )
												self.assertEqual(encoding['labels'][0]['boxes'].shape    ,__snake_case   )
												a__    =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,__snake_case    ,atol=1E-3   )   )
												# verify image_id
												a__    =  torch.tensor([3_97_69]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,__snake_case   )   )
												# verify is_crowd
												a__    =  torch.tensor([0, 0, 0, 0, 0, 0]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,__snake_case   )   )
												# verify class_labels
												a__    =  torch.tensor([75, 75, 63, 65, 17, 17]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,__snake_case   )   )
												# verify orig_size
												a__    =  torch.tensor([4_80, 6_40]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,__snake_case   )   )
												# verify size
												a__    =  torch.tensor([8_00, 10_66]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,__snake_case   )   )



					@slow
					def 				lowerCamelCase__(   self     :Tuple   )      ->					List[Any]:
												# prepare image, target and masks_path
												a__    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
												with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,'r'   ) as f:
																			a__    =  json.loads(f.read()   )

												a__    =  {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

												a__    =  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

												# encode them
												a__    =  ConditionalDetrImageProcessor(format='coco_panoptic'   )
												a__    =  image_processing(images=__snake_case    ,annotations=__snake_case    ,masks_path=__snake_case    ,return_tensors='pt'   )

												# verify pixel values
												a__    =  torch.Size([1, 3, 8_00, 10_66]   )
												self.assertEqual(encoding['pixel_values'].shape    ,__snake_case   )

												a__    =  torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,__snake_case    ,atol=1E-4   )   )

												# verify area
												a__    =  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,__snake_case   )   )
												# verify boxes
												a__    =  torch.Size([6, 4]   )
												self.assertEqual(encoding['labels'][0]['boxes'].shape    ,__snake_case   )
												a__    =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,__snake_case    ,atol=1E-3   )   )
												# verify image_id
												a__    =  torch.tensor([3_97_69]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,__snake_case   )   )
												# verify is_crowd
												a__    =  torch.tensor([0, 0, 0, 0, 0, 0]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,__snake_case   )   )
												# verify class_labels
												a__    =  torch.tensor([17, 17, 63, 75, 75, 93]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,__snake_case   )   )
												# verify masks
												a__    =  82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,__snake_case   )
												# verify orig_size
												a__    =  torch.tensor([4_80, 6_40]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,__snake_case   )   )
												# verify size
												a__    =  torch.tensor([8_00, 10_66]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,__snake_case   )   )








from manim import *







class  snake_case_     (lowerCamelCase_   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[int]:
												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   ):
																			a__    =  fill.copy().set_fill(__snake_case    ,opacity=0.8   )
																			target.move_to(__snake_case   )
																			model_arr.append(__snake_case   )

																			a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0.0   ).set_fill(__snake_case    ,opacity=0.8   )
																			cpu_target.move_to(cpu_left_col_base[i]   )
																			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, -1.25, 0]   )
												self.add(__snake_case    ,__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'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case   )   )

												a__    =  Square(0.3   )
												input.set_fill(__snake_case    ,opacity=1.0   )
												input.set_stroke(width=0.0   )
												input.next_to(model_base[0]    ,__snake_case    ,buff=0.5   )

												self.play(Write(__snake_case   )   )

												input.generate_target()
												input.target.next_to(model_arr[0]    ,direction=__snake_case    ,buff=0.02   )
												self.play(MoveToTarget(__snake_case   )   )

												self.play(FadeOut(__snake_case   )   )

												a__    =  Arrow(start=__snake_case    ,end=__snake_case    ,color=__snake_case    ,buff=0.5   )
												a.next_to(model_arr[0].get_left()    ,__snake_case    ,buff=0.2   )

												model_cpu_arr[0].generate_target()
												model_cpu_arr[0].target.move_to(gpu_rect[0]   )

												a__    =  MarkupText(
												    F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )   )

												a__    =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

												self.play(
												    Write(__snake_case   )    ,Circumscribe(model_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_cpu_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)
												self.play(MoveToTarget(model_cpu_arr[0]   )   )

												a__    =  a.copy()
												for i in range(6   ):
																			a_c.next_to(model_arr[i].get_right() + 0.02    ,__snake_case    ,buff=0.2   )

																			input.generate_target()
																			input.target.move_to(model_arr[i].get_right() + 0.02   )

																			a__    =  AnimationGroup(
																			    FadeOut(__snake_case    ,run_time=0.5   )    ,MoveToTarget(__snake_case    ,run_time=0.5   )    ,FadeIn(__snake_case    ,run_time=0.5   )    ,lag_ratio=0.2   )

																			self.play(__snake_case   )

																			model_cpu_arr[i].generate_target()
																			model_cpu_arr[i].target.move_to(cpu_left_col_base[i]   )

																			if i < 5:
																										model_cpu_arr[i + 1].generate_target()
																										model_cpu_arr[i + 1].target.move_to(gpu_rect[0]   )
																										if i >= 1:
																																	a__    =  0.7

																										self.play(
																										    Circumscribe(model_arr[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_arr[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,)
																										if i < 1:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]   )    ,MoveToTarget(model_cpu_arr[i + 1]   )    ,)
																										else:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]    ,run_time=0.7   )    ,MoveToTarget(model_cpu_arr[i + 1]    ,run_time=0.7   )    ,)
																			else:
																										model_cpu_arr[i].generate_target()
																										model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]   )
																										input.generate_target()
																										input.target.next_to(model_arr[-1].get_right()    ,RIGHT + 0.02    ,buff=0.2   )

																										self.play(
																										    Circumscribe(model_arr[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)

																										self.play(MoveToTarget(model_cpu_arr[i]   )   )

																			a__    =  a_c
																			a__    =  a_c.copy()

												input.generate_target()
												input.target.next_to(model_base[-1]    ,RIGHT + 0.02    ,buff=0.5   )
												self.play(
												    FadeOut(__snake_case   )    ,FadeOut(__snake_case    ,run_time=0.5   )    ,)

												a__    =  MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.'    ,font_size=24   )
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )    ,MoveToTarget(__snake_case   )   )

												self.wait()




