






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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def __init__(     self:      List[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      List[Any]=7       ,	UpperCamelCase:      Dict=3       ,	UpperCamelCase:      Union[str, Any]=30       ,	UpperCamelCase:      Optional[Any]=4_00       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      Tuple=True       ,	UpperCamelCase:      str=[0.5, 0.5, 0.5]       ,	UpperCamelCase:      List[Any]=[0.5, 0.5, 0.5]       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      Union[str, Any]=1 / 2_55       ,	UpperCamelCase:      List[str]=True       ,	)		->		Dict:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case__									=     size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     num_channels
											snake_case__									=     min_resolution
											snake_case__									=     max_resolution
											snake_case__									=     do_resize
											snake_case__									=     size
											snake_case__									=     do_normalize
											snake_case__									=     image_mean
											snake_case__									=     image_std
											snake_case__									=     do_rescale
											snake_case__									=     rescale_factor
											snake_case__									=     do_pad
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											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:      Tuple       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[int]=False						)		->		List[str]:
											if not batched:
															snake_case__									=     image_inputs[0]
															if isinstance(UpperCamelCase       ,	Image.Image						):
																			snake_case__									=     image.size
															else:
																			snake_case__									=     image.shape[1], image.shape[2]
															if w < h:
																			snake_case__									=     int(self.size['shortest_edge'] * h / w						)
																			snake_case__									=     self.size["shortest_edge"]
															elif w > h:
																			snake_case__									=     self.size["shortest_edge"]
																			snake_case__									=     int(self.size['shortest_edge'] * w / h						)
															else:
																			snake_case__									=     self.size["shortest_edge"]
																			snake_case__									=     self.size["shortest_edge"]

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

											return expected_height, expected_width


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	_UpperCamelCase    ,  unittest.TestCase	):
							_UpperCAmelCase     =       ConditionalDetrImageProcessor if is_vision_available() else None
							def lowerCAmelCase_ (     self:      str						)		->		str:
											snake_case__									=     ConditionalDetrImageProcessingTester(self						)
							@property
							def lowerCAmelCase_ (     self:      Tuple						)		->		Dict:
											return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Any:
											snake_case__									=     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 lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											snake_case__									=     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       ,	UpperCamelCase						)

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

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

											snake_case__									=     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
											snake_case__									=     self.image_processor_tester.get_expected_values(UpperCamelCase       ,	batched=UpperCamelCase						)

											snake_case__									=     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,
											        expected_height,
											        expected_width,
											    )       ,	)
							def lowerCAmelCase_ (     self:      Any						)		->		Union[str, Any]:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case__									=     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
											snake_case__									=     image_processing(image_inputs[0]       ,	return_tensors='pt'						).pixel_values

											snake_case__									=     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
											snake_case__									=     image_processing(UpperCamelCase       ,	return_tensors='pt'						).pixel_values

											snake_case__									=     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[str]						)		->		Optional[int]:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case__									=     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
											snake_case__									=     image_processing(image_inputs[0]       ,	return_tensors='pt'						).pixel_values

											snake_case__									=     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
											snake_case__									=     image_processing(UpperCamelCase       ,	return_tensors='pt'						).pixel_values

											snake_case__									=     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,
											    )       ,	)
							@slow
							def lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
											# prepare image and target
											snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,	'r'						) as f:
															snake_case__									=     json.loads(f.read()						)

											snake_case__									=     {"image_id": 3_97_69, "annotations": target}

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

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

											snake_case__									=     torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)

											# verify area
											snake_case__									=     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']       ,	UpperCamelCase						)						)
											# verify boxes
											snake_case__									=     torch.Size([6, 4]						)
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,	UpperCamelCase						)
											snake_case__									=     torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,	UpperCamelCase       ,	atol=1e-3						)						)
											# verify image_id
											snake_case__									=     torch.tensor([3_97_69]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,	UpperCamelCase						)						)
											# verify is_crowd
											snake_case__									=     torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,	UpperCamelCase						)						)
											# verify class_labels
											snake_case__									=     torch.tensor([75, 75, 63, 65, 17, 17]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,	UpperCamelCase						)						)
											# verify orig_size
											snake_case__									=     torch.tensor([4_80, 6_40]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,	UpperCamelCase						)						)
											# verify size
											snake_case__									=     torch.tensor([8_00, 10_66]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,	UpperCamelCase						)						)


							@slow
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[int]:
											# prepare image, target and masks_path
											snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,	'r'						) as f:
															snake_case__									=     json.loads(f.read()						)

											snake_case__									=     {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

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

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

											snake_case__									=     torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)

											# verify area
											snake_case__									=     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']       ,	UpperCamelCase						)						)
											# verify boxes
											snake_case__									=     torch.Size([6, 4]						)
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,	UpperCamelCase						)
											snake_case__									=     torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,	UpperCamelCase       ,	atol=1e-3						)						)
											# verify image_id
											snake_case__									=     torch.tensor([3_97_69]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,	UpperCamelCase						)						)
											# verify is_crowd
											snake_case__									=     torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,	UpperCamelCase						)						)
											# verify class_labels
											snake_case__									=     torch.tensor([17, 17, 63, 75, 75, 93]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,	UpperCamelCase						)						)
											# verify masks
											snake_case__									=     82_28_73
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,	UpperCamelCase						)
											# verify orig_size
											snake_case__									=     torch.tensor([4_80, 6_40]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,	UpperCamelCase						)						)
											# verify size
											snake_case__									=     torch.tensor([8_00, 10_66]						)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,	UpperCamelCase						)						)











import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionSAGPipeline
							_UpperCAmelCase     =       TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,	cross_attention_dim=32       ,	)
											snake_case__									=     DDIMScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	)
											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL(
											    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,	latent_channels=4       ,	)
											torch.manual_seed(0						)
											snake_case__									=     CLIPTextConfig(
											    bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=32       ,	intermediate_size=37       ,	layer_norm_eps=1e-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)
											snake_case__									=     CLIPTextModel(UpperCamelCase						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=0						)		->		Union[str, Any]:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': '.',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 1.0,
											    'sag_scale': 1.0,
											    'output_type': 'numpy',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	width=7_68       ,	height=5_12       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'       ,	)

											snake_case__									=     output.images

											assert image.shape == (1, 5_12, 7_68, 3)




