



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







				"""simple docstring"""



				__UpperCamelCase    :		Dict	=	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'




				def 					_snake_case  (self       ,			__lowercase=0		):
										__lowerCAmelCase							       =				floats_tensor((1, 3, 1_28, 1_28)       ,			rng=random.Random(__lowercase		)		)
										__lowerCAmelCase							       =				np.random.RandomState(__lowercase		)
										__lowerCAmelCase							       =				{
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''strength''': 0.7_5,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,			provider='''CPUExecutionProvider'''		)
										pipe.set_progress_bar_config(disable=__lowercase		)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase							       =				np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]		)
										assert np.abs(image_slice - expected_slice		).max() < 1e-1




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,			provider='''CPUExecutionProvider'''		)
										__lowerCAmelCase							       =				PNDMScheduler.from_config(pipe.scheduler.config       ,			skip_prk_steps=__lowercase		)
										pipe.set_progress_bar_config(disable=__lowercase		)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase							       =				np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,			provider='''CPUExecutionProvider'''		)
										__lowerCAmelCase							       =				LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
										pipe.set_progress_bar_config(disable=__lowercase		)

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

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase							       =				np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,			provider='''CPUExecutionProvider'''		)
										__lowerCAmelCase							       =				EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
										pipe.set_progress_bar_config(disable=__lowercase		)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase							       =				np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,			provider='''CPUExecutionProvider'''		)
										__lowerCAmelCase							       =				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
										pipe.set_progress_bar_config(disable=__lowercase		)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase							       =				np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,			provider='''CPUExecutionProvider'''		)
										__lowerCAmelCase							       =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
										pipe.set_progress_bar_config(disable=__lowercase		)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase							       =				np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]		)

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


@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'''
										    '''/img2img/sketch-mountains-input.jpg'''		)
										__lowerCAmelCase							       =				init_image.resize((7_68, 5_12)		)
										# using the PNDM scheduler by default
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    '''CompVis/stable-diffusion-v1-4'''       ,			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 fantasy landscape, trending on artstation'''

										__lowerCAmelCase							       =				np.random.RandomState(0		)
										__lowerCAmelCase							       =				pipe(
										    prompt=__lowercase       ,			image=__lowercase       ,			strength=0.7_5       ,			guidance_scale=7.5       ,			num_inference_steps=10       ,			generator=__lowercase       ,			output_type='''np'''       ,			)
										__lowerCAmelCase							       =				output.images
										__lowerCAmelCase							       =				images[0, 2_55:2_58, 3_83:3_86, -1]

										assert images.shape == (1, 5_12, 7_68, 3)
										__lowerCAmelCase							       =				np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]		)
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''		)
										__lowerCAmelCase							       =				init_image.resize((7_68, 5_12)		)
										__lowerCAmelCase							       =				LMSDiscreteScheduler.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''       ,			subfolder='''scheduler'''       ,			revision='''onnx'''		)
										__lowerCAmelCase							       =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''       ,			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 fantasy landscape, trending on artstation'''

										__lowerCAmelCase							       =				np.random.RandomState(0		)
										__lowerCAmelCase							       =				pipe(
										    prompt=__lowercase       ,			image=__lowercase       ,			strength=0.7_5       ,			guidance_scale=7.5       ,			num_inference_steps=20       ,			generator=__lowercase       ,			output_type='''np'''       ,			)
										__lowerCAmelCase							       =				output.images
										__lowerCAmelCase							       =				images[0, 2_55:2_58, 3_83:3_86, -1]

										assert images.shape == (1, 5_12, 7_68, 3)
										__lowerCAmelCase							       =				np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]		)
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





'''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)

