
"""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    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Dict  ,A_     :      Union[str, Any]=7  ,A_     :      List[Any]=3  ,A_     :      Any=30  ,A_     :      Tuple=400  ,A_     :      int=True  ,A_     :      str=None  ,A_     :      Any=True  ,A_     :      int=[0.5, 0.5, 0.5]  ,A_     :      Any=[0.5, 0.5, 0.5]  ,A_     :      int=True  ,A_     :      Any=1 / 255  ,A_     :      List[str]=True  ,)     ->   int:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   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 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Tuple  ,A_     :      Tuple=False      )     ->   str:
							if not batched:
								A						   =   image_inputs[0]
								if isinstance(A_  ,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(A_  ,key=lambda A_      : item[0]      )[0]
								A						   =   max(A_  ,key=lambda A_      : item[1]      )[1]

							return expected_height, expected_width







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



						'''simple docstring'''


						_lowerCamelCase:    Union[str, Any]      =    DeformableDetrImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[Any]:
							A						   =   DeformableDetrImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Any:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'image_mean'      )      )
							self.assertTrue(hasattr(A_  ,'image_std'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'do_rescale'      )      )
							self.assertTrue(hasattr(A_  ,'do_pad'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[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  ,A_      )

							A						   =   self.image_processing_class.from_dict(
							    self.image_processor_dict  ,size=42  ,max_size=84  ,pad_and_return_pixel_mask=A_      )
							self.assertEqual(image_processor.size  ,{'shortest_edge': 42, 'longest_edge': 84}      )
							self.assertEqual(image_processor.do_pad  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   int:
							pass


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							# create random PIL images
							A						   =   prepare_image_inputs(self.image_processor_tester  ,equal_resolution=A_      )
							for image in image_inputs:
								self.assertIsInstance(A_  ,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(A_      )

							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(A_  ,batched=A_      )

							A						   =   image_processing(A_  ,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 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Any:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							# create random numpy tensors
							A						   =   prepare_image_inputs(self.image_processor_tester  ,equal_resolution=A_  ,numpify=A_      )
							for image in image_inputs:
								self.assertIsInstance(A_  ,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(A_      )

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

							# Test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      ).pixel_values

							A   ,      A						   =   self.image_processor_tester.get_expected_values(A_  ,batched=A_      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							# create random PyTorch tensors
							A						   =   prepare_image_inputs(self.image_processor_tester  ,equal_resolution=A_  ,torchify=A_      )
							for image in image_inputs:
								self.assertIsInstance(A_  ,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(A_      )

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

							# Test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      ).pixel_values

							A   ,      A						   =   self.image_processor_tester.get_expected_values(A_  ,batched=A_      )

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


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							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=A_  ,annotations=A_  ,return_tensors='pt'      )

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

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

							# verify area
							A						   =   torch.tensor([58_87.96_00, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']  ,A_      )      )
							# verify boxes
							A						   =   torch.Size([6, 4]      )
							self.assertEqual(encoding['labels'][0]['boxes'].shape  ,A_      )
							A						   =   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,A_  ,atol=1e-3      )      )
							# verify image_id
							A						   =   torch.tensor([3_9769]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,A_      )      )
							# verify is_crowd
							A						   =   torch.tensor([0, 0, 0, 0, 0, 0]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,A_      )      )
							# verify class_labels
							A						   =   torch.tensor([75, 75, 63, 65, 17, 17]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,A_      )      )
							# verify orig_size
							A						   =   torch.tensor([480, 640]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,A_      )      )
							# verify size
							A						   =   torch.tensor([800, 1066]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,A_      )      )




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Tuple:
							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=A_  ,annotations=A_  ,masks_path=A_  ,return_tensors='pt'      )

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

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

							# verify area
							A						   =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 58_79.65_62, 76_34.11_47]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']  ,A_      )      )
							# verify boxes
							A						   =   torch.Size([6, 4]      )
							self.assertEqual(encoding['labels'][0]['boxes'].shape  ,A_      )
							A						   =   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,A_  ,atol=1e-3      )      )
							# verify image_id
							A						   =   torch.tensor([3_9769]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,A_      )      )
							# verify is_crowd
							A						   =   torch.tensor([0, 0, 0, 0, 0, 0]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,A_      )      )
							# verify class_labels
							A						   =   torch.tensor([17, 17, 63, 75, 75, 93]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,A_      )      )
							# verify masks
							A						   =   82_2873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,A_      )
							# verify orig_size
							A						   =   torch.tensor([480, 640]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,A_      )      )
							# verify size
							A						   =   torch.tensor([800, 1066]      )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,A_      )      )






"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort







class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]=0      )     ->   Dict:
							A						   =   floats_tensor((1, 3, 128, 128)  ,rng=random.Random(A_      )      )
							A						   =   np.random.RandomState(A_      )
							A						   =   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'strength': 0.75,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]      )
							assert np.abs(image_slice - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   PNDMScheduler.from_config(pipe.scheduler.config  ,skip_prk_steps=A_      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							# warmup pass to apply optimizations
							A						   =   pipe(**self.get_dummy_inputs()      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							A						   =   ort.SessionOptions()
							A						   =   False
							return options


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							# using the PNDM scheduler by default
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'  ,revision='onnx'  ,safety_checker=A_  ,feature_extractor=A_  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=10  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Any:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							A						   =   LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,subfolder='scheduler'  ,revision='onnx'      )
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,revision='onnx'  ,scheduler=A_  ,safety_checker=A_  ,feature_extractor=A_  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=20  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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