






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       a						:			int     	=					argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument(
           "--original_config_file",
           type=str,
           required=True,
           help="The YAML config file corresponding to the original architecture.",
       )
       parser.add_argument(
           "--num_in_channels",
           default=None,
           type=int,
           help="The number of input channels. If `None` number of input channels will be automatically inferred.",
       )
       parser.add_argument(
           "--image_size",
           default=512,
           type=int,
           help=(
               "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
               " Base. Use 768 for Stable Diffusion v2."
           ),
       )
       parser.add_argument(
           "--extract_ema",
           action="store_true",
           help=(
               "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
               " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
               " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
           ),
       )
       parser.add_argument(
           "--upcast_attention",
           action="store_true",
           help=(
               "Whether the attention computation should always be upcasted. This is necessary when running stable"
               " diffusion 2.1."
           ),
       )
       parser.add_argument(
           "--from_safetensors",
           action="store_true",
           help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
       )
       parser.add_argument(
           "--to_safetensors",
           action="store_true",
           help="Whether to store pipeline in safetensors format or not.",
       )
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")






       def 							lowerCamelCase__ (       __lowerCamelCase     :	Any						):
         if string == "True":
           return True
         elif string == "False":
           return False
         else:
           raise ValueError(f"""could not parse string as bool {string}"""						)

       parser.add_argument(
           "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
       )

       parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

       a						:			List[str]     	=					parser.parse_args()

       a						:			Optional[Any]     	=					download_controlnet_from_original_ckpt(
           checkpoint_path=args.checkpoint_path,
           original_config_file=args.original_config_file,
           image_size=args.image_size,
           extract_ema=args.extract_ema,
           num_in_channels=args.num_in_channels,
           upcast_attention=args.upcast_attention,
           from_safetensors=args.from_safetensors,
           device=args.device,
           use_linear_projection=args.use_linear_projection,
           cross_attention_dim=args.cross_attention_dim,
       )







       controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









from __future__ import annotations

import requests

UpperCamelCase_												=	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 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=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

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

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          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']))



