
'''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 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		pass







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







		"""simple docstring"""

		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







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



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						ort.SessionOptions()
				snake_case  :       Dict     			=						False
				return options



		def       lowerCAmelCase				(      self				:			Any      )						->			Tuple:






				"""simple docstring"""







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

				snake_case  :       Optional[Any]     			=						'''A red cat sitting on a park bench'''

				snake_case  :       Tuple     			=						np.random.RandomState(0      )
				snake_case  :       Tuple     			=						pipe(
				    prompt=UpperCamelCase__  ,	image=UpperCamelCase__  ,	mask_image=UpperCamelCase__  ,	guidance_scale=7.5  ,	num_inference_steps=10  ,	generator=UpperCamelCase__  ,	output_type='''np'''  ,	)
				snake_case  :       Any     			=						output.images
				snake_case  :       Optional[Any]     			=						images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				snake_case  :       Tuple     			=						np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]      )

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



		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Optional[int]:






				"""simple docstring"""







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

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

				snake_case  :       List[str]     			=						np.random.RandomState(0      )
				snake_case  :       int     			=						pipe(
				    prompt=UpperCamelCase__  ,	image=UpperCamelCase__  ,	mask_image=UpperCamelCase__  ,	guidance_scale=7.5  ,	num_inference_steps=20  ,	generator=UpperCamelCase__  ,	output_type='''np'''  ,	)
				snake_case  :       List[Any]     			=						output.images
				snake_case  :       str     			=						images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				snake_case  :       Any     			=						np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]      )

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




'''simple docstring'''
import os
from collections.abc import Iterator




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							Iterator[str]:


		'''simple docstring'''


		for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Optional[Any]     			=						[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(SCREAMING_SNAKE_CASE__			)[1] in (".py", ".ipynb"):
								yield os.path.join(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).lstrip('''./'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		return F'{i * "  "}*' if i else "\n##"




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       Union[str, Any]     			=						old_path.split(os.sep			)
		for i, new_part in enumerate(new_path.split(os.sep			)			):
				if (i + 1 > len(SCREAMING_SNAKE_CASE__			) or old_parts[i] != new_part) and new_part:
						print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} {new_part.replace("_" ,       " "			).title()}'			)
		return new_path




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							None:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						''''''
		for filepath in sorted(good_file_paths(SCREAMING_SNAKE_CASE__			)			):
				snake_case			,snake_case  :       Optional[Any]     			=						os.path.split(SCREAMING_SNAKE_CASE__			)
				if filepath != old_path:
						snake_case  :       Dict     			=						print_path(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						(filepath.count(os.sep			) + 1) if filepath else 0
				snake_case  :       int     			=						F'{filepath}/{filename}'.replace(''' ''' ,       '''%20'''			)
				snake_case  :       int     			=						os.path.splitext(filename.replace('''_''' ,       ''' '''			).title()			)[0]
				print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} [{filename}]({url})'			)


if __name__ == "__main__":
						print_directory_md(".")


