

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 _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def __init__(      self				:			Optional[Any]   ,   a				:			Optional[int]   ,   a				:			Dict=7   ,   a				:			Union[str, Any]=3   ,   a				:			List[Any]=30   ,   a				:			int=400   ,   a				:			Tuple=True   ,   a				:			int=None   ,   a				:			Optional[int]=True   ,   a				:			int=[0.5, 0.5, 0.5]   ,   a				:			Any=[0.5, 0.5, 0.5]   ,   a				:			int=True   ,   a				:			str=1 / 255   ,   a				:			str=True   ,   )				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
				SCREAMING_SNAKE_CASE :   str					  =							parent
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							batch_size
				SCREAMING_SNAKE_CASE :   str					  =							num_channels
				SCREAMING_SNAKE_CASE :   List[Any]					  =							min_resolution
				SCREAMING_SNAKE_CASE :   Dict					  =							max_resolution
				SCREAMING_SNAKE_CASE :   List[Any]					  =							do_resize
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							size
				SCREAMING_SNAKE_CASE :   Tuple					  =							do_normalize
				SCREAMING_SNAKE_CASE :   int					  =							image_mean
				SCREAMING_SNAKE_CASE :   List[Any]					  =							image_std
				SCREAMING_SNAKE_CASE :   List[str]					  =							do_rescale
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							rescale_factor
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							do_pad
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""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 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Optional[int]   ,   a				:			Optional[Any]=False						)				->							Any:

				"""simple docstring"""

				if not batched:
					SCREAMING_SNAKE_CASE :   Tuple					  =							image_inputs[0]
					if isinstance(a   ,   Image.Image						):
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							image.size
					else:
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							image.shape[1], image.shape[2]
					if w < h:
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							int(self.size["shortest_edge"] * h / w						)
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.size["shortest_edge"]
					elif w > h:
						SCREAMING_SNAKE_CASE :   List[str]					  =							self.size["shortest_edge"]
						SCREAMING_SNAKE_CASE :   Dict					  =							int(self.size["shortest_edge"] * w / h						)
					else:
						SCREAMING_SNAKE_CASE :   Any					  =							self.size["shortest_edge"]
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.size["shortest_edge"]

				else:
					SCREAMING_SNAKE_CASE :   int					  =							[]
					for image in image_inputs:
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_expected_values([image]						)
						expected_values.append((expected_height, expected_width)						)
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							max(a   ,   key=lambda a						: item[0]						)[0]
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							max(a   ,   key=lambda a						: item[1]						)[1]

				return expected_height, expected_width






@require_torch
@require_vision
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =ConditionalDetrImageProcessor if is_vision_available() else None
			def 				__UpperCamelCase			(      self				:			Dict						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							ConditionalDetrImageProcessingTester(self						)
			@property
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Any:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							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   ,   "size"						)						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							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						)

				SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				pass
			def 				__UpperCamelCase			(      self				:			str						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				SCREAMING_SNAKE_CASE :   List[Any]					  =							prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=a						)
				for image in image_inputs:
					self.assertIsInstance(a   ,   Image.Image						)

				# Test not batched input
				SCREAMING_SNAKE_CASE :   Dict					  =							image_processing(image_inputs[0]   ,   return_tensors="pt"						).pixel_values

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							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
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.image_processor_tester.get_expected_values(a   ,   batched=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							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 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE :   Tuple					  =							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
				SCREAMING_SNAKE_CASE :   int					  =							image_processing(image_inputs[0]   ,   return_tensors="pt"						).pixel_values

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							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
				SCREAMING_SNAKE_CASE :   Any					  =							image_processing(a   ,   return_tensors="pt"						).pixel_values

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							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
				SCREAMING_SNAKE_CASE :   int					  =							image_processing(image_inputs[0]   ,   return_tensors="pt"						).pixel_values

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							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
				SCREAMING_SNAKE_CASE :   Tuple					  =							image_processing(a   ,   return_tensors="pt"						).pixel_values

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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 				__UpperCamelCase			(      self				:			List[str]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,   "r"						) as f:
					SCREAMING_SNAKE_CASE :   Tuple					  =							json.loads(f.read()						)

				SCREAMING_SNAKE_CASE :   int					  =							{"image_id": 3_9769, "annotations": target}

				# encode them
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_processing(images=a   ,   annotations=a   ,   return_tensors="pt"						)

				# verify pixel values
				SCREAMING_SNAKE_CASE :   Dict					  =							torch.Size([1, 3, 800, 1066]						)
				self.assertEqual(encoding["pixel_values"].shape   ,   a						)

				SCREAMING_SNAKE_CASE :   int					  =							torch.tensor([0.2796, 0.3138, 0.3481]						)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,   a   ,   atol=1e-4						)						)

				# verify area
				SCREAMING_SNAKE_CASE :   List[str]					  =							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"]   ,   a						)						)
				# verify boxes
				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.Size([6, 4]						)
				self.assertEqual(encoding["labels"][0]["boxes"].shape   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,   a   ,   atol=1e-3						)						)
				# verify image_id
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.tensor([3_9769]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,   a						)						)
				# verify is_crowd
				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.tensor([0, 0, 0, 0, 0, 0]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,   a						)						)
				# verify class_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.tensor([75, 75, 63, 65, 17, 17]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,   a						)						)
				# verify orig_size
				SCREAMING_SNAKE_CASE :   int					  =							torch.tensor([480, 640]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,   a						)						)
				# verify size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.tensor([800, 1066]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,   a						)						)






			@slow
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,   "r"						) as f:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							json.loads(f.read()						)

				SCREAMING_SNAKE_CASE :   str					  =							{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

				SCREAMING_SNAKE_CASE :   int					  =							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

				# encode them
				SCREAMING_SNAKE_CASE :   List[str]					  =							ConditionalDetrImageProcessor(format="coco_panoptic"						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image_processing(images=a   ,   annotations=a   ,   masks_path=a   ,   return_tensors="pt"						)

				# verify pixel values
				SCREAMING_SNAKE_CASE :   Any					  =							torch.Size([1, 3, 800, 1066]						)
				self.assertEqual(encoding["pixel_values"].shape   ,   a						)

				SCREAMING_SNAKE_CASE :   int					  =							torch.tensor([0.2796, 0.3138, 0.3481]						)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,   a   ,   atol=1e-4						)						)

				# verify area
				SCREAMING_SNAKE_CASE :   List[str]					  =							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"]   ,   a						)						)
				# verify boxes
				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.Size([6, 4]						)
				self.assertEqual(encoding["labels"][0]["boxes"].shape   ,   a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,   a   ,   atol=1e-3						)						)
				# verify image_id
				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.tensor([3_9769]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,   a						)						)
				# verify is_crowd
				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.tensor([0, 0, 0, 0, 0, 0]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,   a						)						)
				# verify class_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.tensor([17, 17, 63, 75, 75, 93]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,   a						)						)
				# verify masks
				SCREAMING_SNAKE_CASE :   Dict					  =							82_2873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,   a						)
				# verify orig_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([480, 640]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,   a						)						)
				# verify size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.tensor([800, 1066]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,   a						)						)


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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 _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							False
				return options
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   int					  =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   revision="onnx"   ,   safety_checker=a   ,   feature_extractor=a   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Tuple					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=10   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   subfolder="scheduler"   ,   revision="onnx"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   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						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Dict					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=20   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Any					  =							np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3