





from __future__ import annotations

import requests

_snake_case  :    Dict						    =  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_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			int = 1,	lowerCAmelCase_      :			str = "new",	lowerCAmelCase_      :			list | None = None	):
   __lowerCAmelCase								=	wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(lowerCAmelCase_	) - valid_terms	)	):
      __lowerCAmelCase								=	F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(lowerCAmelCase_	)
   __lowerCAmelCase								=	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

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

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








from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    List[Any]						    =  {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """beit"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple=8_1_9_2					,  lowerCAmelCase_ :		Optional[int]=7_6_8					,  lowerCAmelCase_ :		int=1_2					,  lowerCAmelCase_ :		Optional[int]=1_2					,  lowerCAmelCase_ :		Any=3_0_7_2					,  lowerCAmelCase_ :		Optional[int]="gelu"					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.02					,  lowerCAmelCase_ :		int=1e-12					,  lowerCAmelCase_ :		int=2_2_4					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		int=3					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		Union[str, Any]=0.1					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=[3, 5, 7, 1_1]					,  lowerCAmelCase_ :		Optional[Any]=[1, 2, 3, 6]					,  lowerCAmelCase_ :		Tuple=True					,  lowerCAmelCase_ :		Dict=0.4					,  lowerCAmelCase_ :		Tuple=2_5_6					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Optional[int]=2_5_5					,  **lowerCAmelCase_ :		Any					,  )							->						Dict:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	layer_norm_eps

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	use_mask_token
     __lowerCAmelCase								=	use_absolute_position_embeddings
     __lowerCAmelCase								=	use_relative_position_bias
     __lowerCAmelCase								=	use_shared_relative_position_bias
     __lowerCAmelCase								=	layer_scale_init_value
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase								=	use_auxiliary_head
     __lowerCAmelCase								=	auxiliary_loss_weight
     __lowerCAmelCase								=	auxiliary_channels
     __lowerCAmelCase								=	auxiliary_num_convs
     __lowerCAmelCase								=	auxiliary_concat_input
     __lowerCAmelCase								=	semantic_loss_ignore_index





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = version.parse("""1.11"""    )






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






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						float:
     return 1e-4

