





'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






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







 def 						__a   (				self			:   Optional[Any]		):




    '''simple docstring'''
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







 def 						__a   (				self			:   List[Any]		):




    '''simple docstring'''
    a__     = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"		)
    a__     = sd_pipe.to(lowerCamelCase		)
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

    sd_pipe.set_scheduler("sample_euler"		)

    a__     = "A painting of a squirrel eating a burger"
    a__     = torch.manual_seed(0		)
    a__     = sd_pipe([prompt] , generator=lowerCamelCase , guidance_scale=9.0 , num_inference_steps=2_0 , output_type="np"		)

    a__     = output.images

    a__     = image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    a__     = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]		)

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







 def 						__a   (				self			:   List[str]		):




    '''simple docstring'''
    a__     = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"		)
    a__     = sd_pipe.to(lowerCamelCase		)
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

    sd_pipe.set_scheduler("sample_euler"		)

    a__     = "A painting of a squirrel eating a burger"
    a__     = torch.manual_seed(0		)
    a__     = sd_pipe([prompt] , generator=lowerCamelCase , guidance_scale=9.0 , num_inference_steps=2_0 , output_type="np"		)

    a__     = output.images

    a__     = image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    a__     = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]		)

    assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-1







 def 						__a   (				self			:   Tuple		):




    '''simple docstring'''
    a__     = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"		)
    a__     = sd_pipe.to(lowerCamelCase		)
    sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

    sd_pipe.set_scheduler("sample_dpmpp_2m"		)

    a__     = "A painting of a squirrel eating a burger"
    a__     = torch.manual_seed(0		)
    a__     = sd_pipe(
        [prompt] , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=1_5 , output_type="np" , use_karras_sigmas=lowerCamelCase , )

    a__     = output.images

    a__     = image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    a__     = np.array(
        [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]		)

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










'''simple docstring'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"					,  type=__lowerCamelCase					,  default="biencoder-nq-dev.json"					,  help="Path to raw DPR training data"					,  )
			parser.add_argument(
			    "--evaluation_set"					,  type=__lowerCamelCase					,  help="where to store parsed evaluation_set file"					,  )
			parser.add_argument(
			    "--gold_data_path"					,  type=__lowerCamelCase					,  help="where to store parsed gold_data_path file"					,  )
			a__     = parser.parse_args()

			with open(args.src_path					,  "r"     ) as src_file, open(args.evaluation_set					,  "w"     ) as eval_file, open(
			    args.gold_data_path					,  "w"     ) as gold_file:
						a__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"     )
									gold_file.write("\t".join(__lowerCamelCase     ) + "\n"     )


if __name__ == "__main__":
		main()



