


'''simple docstring'''
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        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							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   UpperCamelCase						( self   )->      int:
            torch.manual_seed(0   )
            _A				=							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			,     )
            _A				=							DDIMScheduler(
                beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule='scaled_linear'			,     clip_sample=_UpperCamelCase			,     set_alpha_to_one=_UpperCamelCase			,     )
            torch.manual_seed(0   )
            _A				=							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   )
            _A				=							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=1000			,     )
            _A				=							CLIPTextModel(_UpperCamelCase   )
            _A				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _A				=							{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=0   )->      str:
            if str(_UpperCamelCase   ).startswith('mps'   ):
                 _A				=							torch.manual_seed(_UpperCamelCase   )
            else:
                 _A				=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
            _A				=							{
                'prompt': '.',
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 1.0,
                'sag_scale': 1.0,
                'output_type': 'numpy',
            }
            return inputs






       def   UpperCamelCase						( self   )->      List[str]:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3   )






@slow
@require_torch_gpu
class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      str:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      int:
            _A				=							StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sag_pipe.to(_UpperCamelCase   )
            sag_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

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

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]   )

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






       def   UpperCamelCase						( self   )->      Any:
            _A				=							StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sag_pipe.to(_UpperCamelCase   )
            sag_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

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

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]   )

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






       def   UpperCamelCase						( self   )->      Dict:
            _A				=							StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sag_pipe.to(_UpperCamelCase   )
            sag_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							'.'
            _A				=							torch.manual_seed(0   )
            _A				=							sag_pipe(
                [prompt]			,     width=768			,     height=512			,     generator=_UpperCamelCase			,     guidance_scale=7.5			,     sag_scale=1.0			,     num_inference_steps=20			,     output_type='np'			,     )

            _A				=							output.images

            assert image.shape == (1, 512, 768, 3)









'''simple docstring'''
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 DeformableDetrImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=True			,     _UpperCamelCase=1 / 255			,     _UpperCamelCase=True			,     )->      Tuple:
            # 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': 1333}
            _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   UpperCamelCase						( self   )->      Union[str, Any]:
            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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=False   )->      List[str]:
            if not batched:
                 _A				=							image_inputs[0]
                 if isinstance(_UpperCamelCase			,     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(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[0]   )[0]
                 _A				=							max(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[1]   )[1]

            return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=DeformableDetrImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							DeformableDetrImageProcessingTester(self   )






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






       def   UpperCamelCase						( 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			,     'do_rescale'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_pad'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'size'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class.from_dict(self.image_processor_dict   )
            self.assertEqual(image_processor.size			,     {'shortest_edge': 18, 'longest_edge': 1333}   )
            self.assertEqual(image_processor.do_pad			,     _UpperCamelCase   )

            _A				=							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   UpperCamelCase						( self   )->      Optional[Any]:
            pass






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # 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=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

            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(_UpperCamelCase			,     batched=_UpperCamelCase   )

            _A				=							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   UpperCamelCase						( self   )->      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=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

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

            # Test batched
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							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   UpperCamelCase						( self   )->      Dict:
            # 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=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

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

            # Test batched
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							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   UpperCamelCase						( self   )->      List[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_9769, 'annotations': target}

            # encode them
            _A				=							DeformableDetrImageProcessor()
            _A				=							image_processing(images=_UpperCamelCase			,     annotations=_UpperCamelCase			,     return_tensors='pt'   )

            # verify pixel values
            _A				=							torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape			,     _UpperCamelCase   )

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

            # verify area
            _A				=							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']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([75, 75, 63, 65, 17, 17]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )






       @slow
       def   UpperCamelCase						( self   )->      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_9769, 'segments_info': target}

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

            # encode them
            _A				=							DeformableDetrImageProcessor(format='coco_panoptic'   )
            _A				=							image_processing(images=_UpperCamelCase			,     annotations=_UpperCamelCase			,     masks_path=_UpperCamelCase			,     return_tensors='pt'   )

            # verify pixel values
            _A				=							torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape			,     _UpperCamelCase   )

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

            # verify area
            _A				=							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']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([17, 17, 63, 75, 75, 93]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify masks
            _A				=							82_2873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,     _UpperCamelCase   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )





