


"""simple docstring"""






import json
import sys



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				with open(lowerCamelCase      ,    encoding='utf-8' ) as f:
								UpperCAmelCase__    =							json.load(lowerCamelCase )

				UpperCAmelCase__    =							['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

				for benchmark_name in sorted(lowerCamelCase ):
								UpperCAmelCase__    =							results[benchmark_name]

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

								UpperCAmelCase__    =							'| metric |'
								UpperCAmelCase__    =							'|--------|'
								UpperCAmelCase__    =							'| new / old (diff) |'
								for metric_name in sorted(lowerCamelCase ):
												UpperCAmelCase__    =							benchmark_res[metric_name]
												UpperCAmelCase__    =							metric_vals['new']
												UpperCAmelCase__    =							metric_vals.get('old'      ,    lowerCamelCase )
												UpperCAmelCase__    =							metric_vals.get('diff'      ,    lowerCamelCase )

												UpperCAmelCase__    =							f''' {new_val:f}''' if isinstance(lowerCamelCase      ,    (int, float) ) else 'None'

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

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

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

				output_md.append('</details>' )

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


if __name__ == "__main__":
						lowerCAmelCase__						:						Optional[int]		  =			sys.argv[1]
						lowerCAmelCase__						:						Dict		  =			sys.argv[2]

						format_json_to_md(input_json_file, output_md_file)




"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCAmelCase__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@slow
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe(
									    image=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2



