


import argparse
import json

from tqdm import tqdm
def 				UpperCAmelCase_					(							)     ->			int:







 """simple docstring"""


 _lowercase							      =argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
     '''--src_path'''     ,						type=__snake_case     ,						default='''biencoder-nq-dev.json'''     ,						help='''Path to raw DPR training data'''     ,						)
 parser.add_argument(
     '''--evaluation_set'''     ,						type=__snake_case     ,						help='''where to store parsed evaluation_set file'''     ,						)
 parser.add_argument(
     '''--gold_data_path'''     ,						type=__snake_case     ,						help='''where to store parsed gold_data_path file'''     ,						)
 _lowercase							      =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:
  _lowercase							      =json.load(__snake_case						)
  for dpr_record in tqdm(__snake_case						):
   _lowercase							      =dpr_record['''question''']
   _lowercase							      =[context['''title'''] for context in dpr_record['''positive_ctxs''']]
   eval_file.write(question + '''\n'''						)
   gold_file.write('''\t'''.join(__snake_case						) + '''\n'''						)


if __name__ == "__main__":
  main()









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	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  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