"""simple docstring"""
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   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				# FIXME: add fast tests
				pass





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



				"""simple docstring"""

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			ort.SessionOptions()
										snake_case								=			False
										return options

				def 		snake_case (				self    ):






										"""simple docstring"""

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

										snake_case								=			'A red cat sitting on a park bench'

										snake_case								=			np.random.RandomState(0    )
										snake_case								=			pipe(
										    prompt=lowerCAmelCase		,    image=lowerCAmelCase		,    mask_image=lowerCAmelCase		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=lowerCAmelCase		,    output_type='np'		,    )
										snake_case								=			output.images
										snake_case								=			images[0, 2_55:2_58, 2_55:2_58, -1]

										assert images.shape == (1, 5_12, 5_12, 3)
										snake_case								=			np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]    )

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/in_paint/overture-creations-5sI6fQgYIuo.png'    )
										snake_case								=			load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'    )
										snake_case								=			LMSDiscreteScheduler.from_pretrained(
										    'runwayml/stable-diffusion-inpainting'		,    subfolder='scheduler'		,    revision='onnx'    )
										snake_case								=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
										    'runwayml/stable-diffusion-inpainting'		,    revision='onnx'		,    scheduler=lowerCAmelCase		,    safety_checker=lowerCAmelCase		,    feature_extractor=lowerCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
										pipe.set_progress_bar_config(disable=lowerCAmelCase    )

										snake_case								=			'A red cat sitting on a park bench'

										snake_case								=			np.random.RandomState(0    )
										snake_case								=			pipe(
										    prompt=lowerCAmelCase		,    image=lowerCAmelCase		,    mask_image=lowerCAmelCase		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=lowerCAmelCase		,    output_type='np'		,    )
										snake_case								=			output.images
										snake_case								=			images[0, 2_55:2_58, 2_55:2_58, -1]

										assert images.shape == (1, 5_12, 5_12, 3)
										snake_case								=			np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]    )

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



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
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 DonutImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}





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



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_resize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=42    )
										self.assertEqual(image_processor.size		,    {'height': 42, 'width': 42}    )

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										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=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    Image.Image    )

										# Test not batched input
										snake_case								=			image_processing(image_inputs[0]		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										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=lowerCAmelCase		,    numpify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    np.ndarray    )

										# Test not batched input
										snake_case								=			image_processing(image_inputs[0]		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										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=lowerCAmelCase		,    torchify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    torch.Tensor    )

										# Test not batched input
										snake_case								=			image_processing(image_inputs[0]		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )


