





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 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=3_0       ,  __lowerCAmelCase=4_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=[0.5, 0.5, 0.5]       ,  __lowerCAmelCase=[0.5, 0.5, 0.5]       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=1 / 2_5_5       ,  __lowerCAmelCase=True       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		min_resolution
				lowerCamelCase__								=		max_resolution
				lowerCamelCase__								=		do_resize
				lowerCamelCase__								=		size
				lowerCamelCase__								=		do_normalize
				lowerCamelCase__								=		image_mean
				lowerCamelCase__								=		image_std
				lowerCamelCase__								=		do_rescale
				lowerCamelCase__								=		rescale_factor
				lowerCamelCase__								=		do_pad





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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       ,  __lowerCAmelCase       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







				if not batched:
					lowerCamelCase__								=		image_inputs[0]
					if isinstance(__lowerCAmelCase       ,  Image.Image							):
						lowerCamelCase__				,	lowerCamelCase__								=		image.size
					else:
						lowerCamelCase__				,	lowerCamelCase__								=		image.shape[1], image.shape[2]
					if w < h:
						lowerCamelCase__								=		int(self.size['''shortest_edge'''] * h / w							)
						lowerCamelCase__								=		self.size['''shortest_edge''']
					elif w > h:
						lowerCamelCase__								=		self.size['''shortest_edge''']
						lowerCamelCase__								=		int(self.size['''shortest_edge'''] * w / h							)
					else:
						lowerCamelCase__								=		self.size['''shortest_edge''']
						lowerCamelCase__								=		self.size['''shortest_edge''']

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

				return expected_height, expected_width






@require_torch
@require_vision
class 					__A    (    lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						ConditionalDetrImageProcessor if is_vision_available() else None





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ConditionalDetrImageProcessingTester(self							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__lowerCAmelCase       ,  '''image_mean'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase       ,  '''image_std'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase       ,  '''do_normalize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase       ,  '''do_resize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase       ,  '''size'''							)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size       ,  {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}							)
				self.assertEqual(image_processor.do_pad       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		self.image_processing_class.from_dict(
				    self.image_processor_dict       ,  size=4_2       ,  max_size=8_4       ,  pad_and_return_pixel_mask=__lowerCAmelCase							)
				self.assertEqual(image_processor.size       ,  {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}							)
				self.assertEqual(image_processor.do_pad       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random PIL images
				lowerCamelCase__								=		prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase       ,  Image.Image							)

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

				lowerCamelCase__				,	lowerCamelCase__								=		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)

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

				# Test batched
				lowerCamelCase__				,	lowerCamelCase__								=		self.image_processor_tester.get_expected_values(__lowerCAmelCase       ,  batched=__lowerCAmelCase							)

				lowerCamelCase__								=		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,
				        expected_height,
				        expected_width,
				    )       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random numpy tensors
				lowerCamelCase__								=		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
				lowerCamelCase__								=		image_processing(image_inputs[0]       ,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__				,	lowerCamelCase__								=		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)

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

				# Test batched
				lowerCamelCase__								=		image_processing(__lowerCAmelCase       ,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__				,	lowerCamelCase__								=		self.image_processor_tester.get_expected_values(__lowerCAmelCase       ,  batched=__lowerCAmelCase							)

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				lowerCamelCase__								=		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
				lowerCamelCase__								=		image_processing(image_inputs[0]       ,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__				,	lowerCamelCase__								=		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)

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

				# Test batched
				lowerCamelCase__								=		image_processing(__lowerCAmelCase       ,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__				,	lowerCamelCase__								=		self.image_processor_tester.get_expected_values(__lowerCAmelCase       ,  batched=__lowerCAmelCase							)

				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							):



				'''simple docstring'''







				lowerCamelCase__								=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,  '''r'''							) as f:
					lowerCamelCase__								=		json.loads(f.read()							)

				lowerCamelCase__								=		{'''image_id''': 3_9_7_6_9, '''annotations''': target}

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

				# verify pixel values
				lowerCamelCase__								=		torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
				self.assertEqual(encoding['''pixel_values'''].shape       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		torch.tensor([0.2796, 0.3138, 0.3481]							)
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,  __lowerCAmelCase       ,  atol=1E-4							)							)

				# verify area
				lowerCamelCase__								=		torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,  __lowerCAmelCase							)							)
				# verify boxes
				lowerCamelCase__								=		torch.Size([6, 4]							)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,  __lowerCAmelCase       ,  atol=1E-3							)							)
				# verify image_id
				lowerCamelCase__								=		torch.tensor([3_9_7_6_9]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,  __lowerCAmelCase							)							)
				# verify is_crowd
				lowerCamelCase__								=		torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,  __lowerCAmelCase							)							)
				# verify class_labels
				lowerCamelCase__								=		torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,  __lowerCAmelCase							)							)
				# verify orig_size
				lowerCamelCase__								=		torch.tensor([4_8_0, 6_4_0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,  __lowerCAmelCase							)							)
				# verify size
				lowerCamelCase__								=		torch.tensor([8_0_0, 1_0_6_6]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,  __lowerCAmelCase							)							)





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,  '''r'''							) as f:
					lowerCamelCase__								=		json.loads(f.read()							)

				lowerCamelCase__								=		{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

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

				# verify pixel values
				lowerCamelCase__								=		torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
				self.assertEqual(encoding['''pixel_values'''].shape       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		torch.tensor([0.2796, 0.3138, 0.3481]							)
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,  __lowerCAmelCase       ,  atol=1E-4							)							)

				# verify area
				lowerCamelCase__								=		torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,  __lowerCAmelCase							)							)
				# verify boxes
				lowerCamelCase__								=		torch.Size([6, 4]							)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,  __lowerCAmelCase       ,  atol=1E-3							)							)
				# verify image_id
				lowerCamelCase__								=		torch.tensor([3_9_7_6_9]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,  __lowerCAmelCase							)							)
				# verify is_crowd
				lowerCamelCase__								=		torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,  __lowerCAmelCase							)							)
				# verify class_labels
				lowerCamelCase__								=		torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,  __lowerCAmelCase							)							)
				# verify masks
				lowerCamelCase__								=		8_2_2_8_7_3
				self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,  __lowerCAmelCase							)
				# verify orig_size
				lowerCamelCase__								=		torch.tensor([4_8_0, 6_4_0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,  __lowerCAmelCase							)							)
				# verify size
				lowerCamelCase__								=		torch.tensor([8_0_0, 1_0_6_6]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,  __lowerCAmelCase							)							)










import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(UnCLIPScheduler,)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
					for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

						self.check_over_forward(time_step=__lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''fixed_small_log'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000E-10							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.054_9625							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.999_4987							)							) < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''learned_range'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		0.5

				assert scheduler._get_variance(1       ,  predicted_variance=__lowerCAmelCase							) - -10.171_2790 < 1E-5
				assert scheduler._get_variance(4_8_7       ,  predicted_variance=__lowerCAmelCase							) - -5.799_8052 < 1E-5
				assert scheduler._get_variance(9_9_9       ,  predicted_variance=__lowerCAmelCase							) - -0.001_0011 < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 252.268_2495							) < 1E-2
				assert abs(result_mean.item() - 0.328_4743							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(2_5							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					if i + 1 == timesteps.shape[0]:
						lowerCamelCase__								=		None
					else:
						lowerCamelCase__								=		timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(
					    __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 258.204_4983							) < 1E-2
				assert abs(result_mean.item() - 0.336_2038							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass



