
"""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_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''




							def    A__						( self    ,				snake_case_=0				)       ->  Dict:
													__lowerCAmelCase     =  floats_tensor((1, 3, 128, 128)    ,				rng=random.Random(snake_case_				)				)
													__lowerCAmelCase     =  np.random.RandomState(snake_case_				)
													__lowerCAmelCase     =  {
													    """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    A__						( self				)       ->  str:
													__lowerCAmelCase     =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,				provider="""CPUExecutionProvider"""				)
													pipe.set_progress_bar_config(disable=snake_case_				)

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCAmelCase     =  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]				)
													assert np.abs(image_slice - expected_slice				).max() < 1e-1




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,				provider="""CPUExecutionProvider"""				)
													__lowerCAmelCase     =  PNDMScheduler.from_config(pipe.scheduler.config    ,				skip_prk_steps=snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCAmelCase     =  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]				)

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




							def    A__						( self				)       ->  List[Any]:
													__lowerCAmelCase     =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,				provider="""CPUExecutionProvider"""				)
													__lowerCAmelCase     =  LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
													pipe.set_progress_bar_config(disable=snake_case_				)

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

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCAmelCase     =  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]				)

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




							def    A__						( self				)       ->  List[Any]:
													__lowerCAmelCase     =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,				provider="""CPUExecutionProvider"""				)
													__lowerCAmelCase     =  EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
													pipe.set_progress_bar_config(disable=snake_case_				)

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCAmelCase     =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]				)

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




							def    A__						( self				)       ->  int:
													__lowerCAmelCase     =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,				provider="""CPUExecutionProvider"""				)
													__lowerCAmelCase     =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
													pipe.set_progress_bar_config(disable=snake_case_				)

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCAmelCase     =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]				)

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




							def    A__						( self				)       ->  int:
													__lowerCAmelCase     =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,				provider="""CPUExecutionProvider"""				)
													__lowerCAmelCase     =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
													pipe.set_progress_bar_config(disable=snake_case_				)

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCAmelCase     =  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]				)

													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    A__						( self				)       ->  str:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)




							@property
							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  ort.SessionOptions()
													__lowerCAmelCase     =  False
													return options




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

													__lowerCAmelCase     =  """A fantasy landscape, trending on artstation"""

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

													assert images.shape == (1, 512, 768, 3)
													__lowerCAmelCase     =  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]				)
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/img2img/sketch-mountains-input.jpg"""				)
													__lowerCAmelCase     =  init_image.resize((768, 512)				)
													__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=snake_case_    ,				safety_checker=snake_case_    ,				feature_extractor=snake_case_    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
													pipe.set_progress_bar_config(disable=snake_case_				)

													__lowerCAmelCase     =  """A fantasy landscape, trending on artstation"""

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

													assert images.shape == (1, 512, 768, 3)
													__lowerCAmelCase     =  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]				)
													# 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 argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE_					     =	[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]

def  lowercase     (_lowerCAmelCase						):
						# Test all the extensions added in the setup
						for file in FILES_TO_FIND:
												if not (transformers_path / file).exists():
																		return False
						return True


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			if args.check_lib:
						SCREAMING_SNAKE_CASE_					     =	importlib.import_module('''transformers''')
						SCREAMING_SNAKE_CASE_					     =	Path(transformers_module.__file__).parent
			else:
						SCREAMING_SNAKE_CASE_					     =	Path.cwd() / '''build/lib/transformers'''
			if not test_custom_files_are_present(transformers_path):
						raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
