





'''simple docstring'''

from __future__ import annotations

import requests

a   	=    set(
    "approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports".split()
)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase = 1				,						__UpperCAmelCase = "new"				,						__UpperCAmelCase = None   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(__UpperCAmelCase   ) - valid_terms   )   ):
      __SCREAMING_SNAKE_CASE   				=							f"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							requests.get(
       f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,						headers={"""User-agent""": """A random string"""}				,						)
   if response.status_code == 429:
      raise requests.HTTPError

   __SCREAMING_SNAKE_CASE   				=							response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(__UpperCAmelCase   )}

   __SCREAMING_SNAKE_CASE   				=							{}
   for id_ in range(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							{
          item: data["""data"""]["""children"""][id_]["""data"""][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))












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


a__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

       A_          =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       A_          =       torch.manual_seed(0 )
       A_          =       pipe(
           image=__snake_case ,generator=__snake_case ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

       A_          =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       A_          =       np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

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



