

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a    :Optional[Any]      =			{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Tuple      =			['XLNetTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Optional[int]      =			['XLNetTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Optional[Any]      =			[
          'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'XLNetForMultipleChoice',
          'XLNetForQuestionAnswering',
          'XLNetForQuestionAnsweringSimple',
          'XLNetForSequenceClassification',
          'XLNetForTokenClassification',
          'XLNetLMHeadModel',
          'XLNetModel',
          'XLNetPreTrainedModel',
          'load_tf_weights_in_xlnet',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :int      =			[
          'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFXLNetForMultipleChoice',
          'TFXLNetForQuestionAnsweringSimple',
          'TFXLNetForSequenceClassification',
          'TFXLNetForTokenClassification',
          'TFXLNetLMHeadModel',
          'TFXLNetMainLayer',
          'TFXLNetModel',
          'TFXLNetPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlnet import XLNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlnet_fast import XLNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlnet import (
                XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLNetForMultipleChoice,
                XLNetForQuestionAnswering,
                XLNetForQuestionAnsweringSimple,
                XLNetForSequenceClassification,
                XLNetForTokenClassification,
                XLNetLMHeadModel,
                XLNetModel,
                XLNetPreTrainedModel,
                load_tf_weights_in_xlnet,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlnet import (
                TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLNetForMultipleChoice,
                TFXLNetForQuestionAnsweringSimple,
                TFXLNetForSequenceClassification,
                TFXLNetForTokenClassification,
                TFXLNetLMHeadModel,
                TFXLNetMainLayer,
                TFXLNetModel,
                TFXLNetPreTrainedModel,
            )

else:
      import sys

      __a    :int      =			_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"""]))


