


'''simple docstring'''
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				ort.SessionOptions()
							UpperCamelCase__     :Union[str, Any]							      =				False
							return options




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''							)
							UpperCamelCase__     :str							      =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''							)
							UpperCamelCase__     :List[str]							      =				load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''							)

							# using the PNDM scheduler by default
							UpperCamelCase__     :Tuple							      =				OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4'''					,     revision='''onnx'''					,     safety_checker=UpperCamelCase_					,     feature_extractor=UpperCamelCase_					,     provider=self.gpu_provider					,     sess_options=self.gpu_options					,     )
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :List[Any]							      =				'''A red cat sitting on a park bench'''

							UpperCamelCase__     :Dict							      =				np.random.RandomState(0							)
							UpperCamelCase__     :Optional[Any]							      =				pipe(
							    prompt=UpperCamelCase_					,     image=UpperCamelCase_					,     mask_image=UpperCamelCase_					,     strength=0.75					,     guidance_scale=7.5					,     num_inference_steps=15					,     generator=UpperCamelCase_					,     output_type='''np'''					,     )

							UpperCamelCase__     :Union[str, Any]							      =				output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image							).max() < 1e-2



'''simple docstring'''
import json
import sys


def     a   (    __a				,      __a     )						->			str:



					'''simple docstring'''






					with open(__a				,      encoding='''utf-8'''     ) as f:
										UpperCamelCase__     :List[str]							      =				json.load(__a     )

					UpperCamelCase__     :int							      =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

					for benchmark_name in sorted(__a     ):
										UpperCamelCase__     :Optional[Any]							      =				results[benchmark_name]

										UpperCamelCase__     :int							      =				benchmark_name.split('''/'''     )[-1]
										output_md.append(f'''### Benchmark: {benchmark_file_name}'''     )

										UpperCamelCase__     :List[str]							      =				'''| metric |'''
										UpperCamelCase__     :str							      =				'''|--------|'''
										UpperCamelCase__     :Union[str, Any]							      =				'''| new / old (diff) |'''
										for metric_name in sorted(__a     ):
															UpperCamelCase__     :List[Any]							      =				benchmark_res[metric_name]
															UpperCamelCase__     :Optional[int]							      =				metric_vals['''new''']
															UpperCamelCase__     :Any							      =				metric_vals.get('''old'''				,      __a     )
															UpperCamelCase__     :Optional[int]							      =				metric_vals.get('''diff'''				,      __a     )

															UpperCamelCase__     :List[str]							      =				f''' {new_val:f}''' if isinstance(__a				,      (int, float)     ) else '''None'''

															if old_val is not None:
																				val_str += f''' / {old_val:f}''' if isinstance(__a				,      (int, float)     ) else "None"
															if dif_val is not None:
																				val_str += f''' ({dif_val:f})''' if isinstance(__a				,      (int, float)     ) else "None"

															title += " " + metric_name + " |"
															lines += "---|"
															value += val_str + " |"

										output_md += [title, lines, value, " "]

					output_md.append('''</details>'''     )

					with open(__a				,      '''w'''				,      encoding='''utf-8'''     ) as f:
										f.writelines('''\n'''.join(__a     )     )


if __name__ == "__main__":
		__snake_case							   =  sys.argv[1]
		__snake_case							   =  sys.argv[2]

		format_json_to_md(input_json_file, output_md_file)