

'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_     = {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_     = [
       """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """GPTBigCodeForSequenceClassification""",
       """GPTBigCodeForTokenClassification""",
       """GPTBigCodeForCausalLM""",
       """GPTBigCodeModel""",
       """GPTBigCodePreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_bigcode import (
          GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTBigCodeForCausalLM,
          GPTBigCodeForSequenceClassification,
          GPTBigCodeForTokenClassification,
          GPTBigCodeModel,
          GPTBigCodePreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









from __future__ import annotations

import requests

_lowerCamelCase      :						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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = 1					,							lowercase_ = "new"					,							lowercase_ = None		)  ->			dict:



       """simple docstring"""

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

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

       A__ 					=       {}
       for id_ in range(lowercase_		):
              A__ 					=       {
                  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"""]))


