



'''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       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				pass


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







				"""simple docstring"""



				@property
				def 					_snake_case  (self		):
										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)




				@property
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				ort.SessionOptions()
										__lowerCAmelCase							       =				False
										return options




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

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

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

										assert images.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =				np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]		)

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




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

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

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

										assert images.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =				np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]		)

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





'''simple docstring'''

import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_UpperCAmelCase				: Tuple							    =				"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
	if pkg in deps:
		if pkg == "tokenizers":
			# must be loaded here, or else tqdm check may fail
			from .utils import is_tokenizers_available

			if not is_tokenizers_available():
				continue  # not required, check version only if installed

		require_version_core(deps[pkg])
	else:
		raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=None):
						require_version(deps[pkg],				lowerCamelCase)

