


"""simple docstring"""







from __future__ import annotations

import requests

a__    :							List[str]            =					set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_ = 1  ,						lowerCAmelCase_ = "new"  ,						lowerCAmelCase_ = None       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    wanted_data or []
					if invalid_search_terms := ", ".join(sorted(set(lowerCAmelCase_       ) - valid_terms       )       ):
										__SCREAMING_SNAKE_CASE						       =    f"""Invalid search term: {invalid_search_terms}"""
										raise ValueError(lowerCAmelCase_       )
					__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(lowerCAmelCase_       )}

					__SCREAMING_SNAKE_CASE						       =    {}
					for id_ in range(lowerCAmelCase_       ):
										__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''']))




"""simple docstring"""







import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


a__    :							Optional[Any]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  int    =      CLIPConfig

	snake_case__    :  str    =      ["CLIPEncoderLayer"]




	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    CLIPConfig       )	->     Dict:
						super().__init__(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    CLIPVisionModelWithProjection(config.vision_config       )

						__SCREAMING_SNAKE_CASE						       =    nn.Linear(config.vision_config.projection_dim      ,      1       )
						__SCREAMING_SNAKE_CASE						       =    nn.Linear(config.vision_config.projection_dim      ,      1       )




	@torch.no_grad()
	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int=0.5      ,      UpperCAmelCase__      :    Optional[int]=0.5       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.vision_model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    self.p_head(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected.flatten()
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected > p_threshold
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected.tolist()

						if any(UpperCAmelCase__       ):
											logger.warning(
											    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
											    " Try again with a different prompt and/or seed."       )

						for idx, nsfw_detected_ in enumerate(UpperCAmelCase__       ):
											if nsfw_detected_:
																__SCREAMING_SNAKE_CASE						       =    np.zeros(images[idx].shape       )

						__SCREAMING_SNAKE_CASE						       =    self.w_head(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    watermark_detected.flatten()
						__SCREAMING_SNAKE_CASE						       =    watermark_detected > w_threshold
						__SCREAMING_SNAKE_CASE						       =    watermark_detected.tolist()

						if any(UpperCAmelCase__       ):
											logger.warning(
											    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
											    " Try again with a different prompt and/or seed."       )

						for idx, watermark_detected_ in enumerate(UpperCAmelCase__       ):
											if watermark_detected_:
																__SCREAMING_SNAKE_CASE						       =    np.zeros(images[idx].shape       )

						return images, nsfw_detected, watermark_detected
