






"""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   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    int = 1      ,   __UpperCAmelCase      :    str = "new"      ,   __UpperCAmelCase      :    list | None = None  )    ->							dict:
						__magic_name__:	Optional[Any]							=       wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(__UpperCAmelCase  ) - valid_terms  )  ):
												__magic_name__:	Optional[int]							=       f'Invalid search term: {invalid_search_terms}'
												raise ValueError(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       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

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

						__magic_name__:	str							=       {}
						for id_ in range(__UpperCAmelCase  ):
												__magic_name__:	str							=       {
												    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"""





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"efficientnet"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int = 3     ,		__snake_case  :      int = 6_0_0     ,		__snake_case  :      float = 2.0     ,		__snake_case  :      float = 3.1     ,		__snake_case  :      int = 8     ,		__snake_case  :      List[int] = [3, 3, 5, 3, 5, 5, 3]     ,		__snake_case  :      List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,		__snake_case  :      List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,		__snake_case  :      List[int] = []     ,		__snake_case  :      List[int] = [1, 2, 2, 2, 1, 2, 1]     ,		__snake_case  :      List[int] = [1, 2, 2, 3, 3, 4, 1]     ,		__snake_case  :      List[int] = [1, 6, 6, 6, 6, 6, 6]     ,		__snake_case  :      float = 0.25     ,		__snake_case  :      str = "swish"     ,		__snake_case  :      int = 2_5_6_0     ,		__snake_case  :      str = "mean"     ,		__snake_case  :      float = 0.02     ,		__snake_case  :      float = 0.001     ,		__snake_case  :      float = 0.99     ,		__snake_case  :      float = 0.5     ,		__snake_case  :      float = 0.2     ,		**__snake_case  :      List[Any]     ,		)			->							List[Any]:
											super().__init__(**__snake_case					)

											__magic_name__:	str							=       num_channels
											__magic_name__:	List[str]							=       image_size
											__magic_name__:	List[str]							=       width_coefficient
											__magic_name__:	Optional[Any]							=       depth_coefficient
											__magic_name__:	Tuple							=       depth_divisor
											__magic_name__:	Dict							=       kernel_sizes
											__magic_name__:	int							=       in_channels
											__magic_name__:	str							=       out_channels
											__magic_name__:	Dict							=       depthwise_padding
											__magic_name__:	Union[str, Any]							=       strides
											__magic_name__:	Dict							=       num_block_repeats
											__magic_name__:	Tuple							=       expand_ratios
											__magic_name__:	List[str]							=       squeeze_expansion_ratio
											__magic_name__:	Any							=       hidden_act
											__magic_name__:	Tuple							=       hidden_dim
											__magic_name__:	int							=       pooling_type
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[str]							=       batch_norm_eps
											__magic_name__:	str							=       batch_norm_momentum
											__magic_name__:	List[str]							=       dropout_rate
											__magic_name__:	Dict							=       drop_connect_rate
											__magic_name__:	Optional[Any]							=       sum(__snake_case					) * 4



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				version.parse("1.11"			)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]					)






					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							float:
											return 1E-5



