

'''simple docstring'''


import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Tuple       	=					nn.functional.normalize(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					nn.functional.normalize(UpperCamelCase  )
	return torch.mm(UpperCamelCase   ,normalized_text_embeds.t()  )







class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   CLIPConfig

							_lowerCamelCase		:      Union[str, Any]=   ["CLIPEncoderLayer"]


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__(_snake_case)

								UpperCAmelCase_		:      Optional[int]       	=					CLIPVisionModel(config.vision_config)
								UpperCAmelCase_		:      Tuple       	=					nn.Linear(config.vision_config.hidden_size    ,			config.projection_dim    ,			bias=_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					nn.Parameter(torch.ones(17    ,			config.projection_dim)    ,			requires_grad=_snake_case)
								UpperCAmelCase_		:      Tuple       	=					nn.Parameter(torch.ones(3    ,			config.projection_dim)    ,			requires_grad=_snake_case)

								UpperCAmelCase_		:      Tuple       	=					nn.Parameter(torch.ones(17)    ,			requires_grad=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					nn.Parameter(torch.ones(3)    ,			requires_grad=_snake_case)


							@torch.no_grad()
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				str:
								UpperCAmelCase_		:      str       	=					self.vision_model(_snake_case)[1]  # pooled_output
								UpperCAmelCase_		:      Union[str, Any]       	=					self.visual_projection(_snake_case)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								UpperCAmelCase_		:      Tuple       	=					cosine_distance(_snake_case    ,			self.special_care_embeds).cpu().float().numpy()
								UpperCAmelCase_		:      Optional[int]       	=					cosine_distance(_snake_case    ,			self.concept_embeds).cpu().float().numpy()

								UpperCAmelCase_		:      str       	=					[]
								UpperCAmelCase_		:      Union[str, Any]       	=					image_embeds.shape[0]
								for i in range(_snake_case):
									UpperCAmelCase_		:      Optional[int]       	=					{'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}

									# increase this value to create a stronger `nfsw` filter
									# at the cost of increasing the possibility of filtering benign images
									UpperCAmelCase_		:      str       	=					0.0

									for concept_idx in range(len(special_cos_dist[0])):
										UpperCAmelCase_		:      Tuple       	=					special_cos_dist[i][concept_idx]
										UpperCAmelCase_		:      Tuple       	=					self.special_care_embeds_weights[concept_idx].item()
										UpperCAmelCase_		:      Optional[Any]       	=					round(concept_cos - concept_threshold + adjustment    ,			3)
										if result_img["special_scores"][concept_idx] > 0:
											result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]})
											UpperCAmelCase_		:      Tuple       	=					0.01

									for concept_idx in range(len(cos_dist[0])):
										UpperCAmelCase_		:      Optional[Any]       	=					cos_dist[i][concept_idx]
										UpperCAmelCase_		:      List[str]       	=					self.concept_embeds_weights[concept_idx].item()
										UpperCAmelCase_		:      Union[str, Any]       	=					round(concept_cos - concept_threshold + adjustment    ,			3)
										if result_img["concept_scores"][concept_idx] > 0:
											result_img["bad_concepts"].append(_snake_case)

									result.append(_snake_case)

								UpperCAmelCase_		:      int       	=					[len(res['bad_concepts']) > 0 for res in result]

								return images, has_nsfw_concepts





							@torch.no_grad()
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					self.vision_model(_snake_case)[1]  # pooled_output
								UpperCAmelCase_		:      Optional[Any]       	=					self.visual_projection(_snake_case)

								UpperCAmelCase_		:      Any       	=					cosine_distance(_snake_case    ,			self.special_care_embeds)
								UpperCAmelCase_		:      List[Any]       	=					cosine_distance(_snake_case    ,			self.concept_embeds)

								# increase this value to create a stronger `nsfw` filter
								# at the cost of increasing the possibility of filtering benign images
								UpperCAmelCase_		:      Optional[Any]       	=					0.0

								UpperCAmelCase_		:      Dict       	=					special_cos_dist - self.special_care_embeds_weights + adjustment
								# special_scores = special_scores.round(decimals=3)
								UpperCAmelCase_		:      Union[str, Any]       	=					torch.any(special_scores > 0    ,			dim=1)
								UpperCAmelCase_		:      Any       	=					special_care * 0.01
								UpperCAmelCase_		:      str       	=					special_adjustment.unsqueeze(1).expand(-1    ,			cos_dist.shape[1])

								UpperCAmelCase_		:      Any       	=					(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								UpperCAmelCase_		:      Optional[int]       	=					torch.any(concept_scores > 0    ,			dim=1)

								return images, has_nsfw_concepts









'''simple docstring'''


from __future__ import annotations

import requests

lowerCAmelCase__										= 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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1   ,UpperCamelCase = "new"   ,UpperCamelCase = None  )    ->    dict:
	UpperCAmelCase_		:      Dict       	=					wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase  ) - valid_terms  )  ):
		UpperCAmelCase_		:      str       	=					f"""Invalid search term: {invalid_search_terms}"""
		raise ValueError(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					requests.get(
	    f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""   ,headers={'User-agent': 'A random string'}   ,)
	if response.status_code == 4_2_9:
		raise requests.HTTPError

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

	UpperCAmelCase_		:      List[str]       	=					{}
	for id_ in range(UpperCamelCase  ):
		UpperCAmelCase_		:      Tuple       	=					{
		    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"]))






