
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402






class      _UpperCAmelCase				(					unittest.TestCase):
       def 					lowerCamelCase__	(    self     ):
         _snake_case     :						Optional[int]						=   0
       def 					lowerCamelCase__	(    self     ):
         _snake_case     :						str						=   AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"     )
         self.assertIsInstance(snake_case_						,    snake_case_     )
       def 					lowerCamelCase__	(    self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
           _snake_case     :						Dict						=   Path(snake_case_     ) / "preprocessor_config.json"
           _snake_case     :						List[str]						=   Path(snake_case_     ) / "config.json"
           json.dump(
               {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}						,    open(snake_case_						,    "w"     )						,    )
           json.dump({"model_type": "clip"}						,    open(snake_case_						,    "w"     )     )

           _snake_case     :						Optional[Any]						=   AutoImageProcessor.from_pretrained(snake_case_     )
           self.assertIsInstance(snake_case_						,    snake_case_     )
       def 					lowerCamelCase__	(    self     ):
         # Ensure we can load the image processor from the feature extractor config
         with tempfile.TemporaryDirectory() as tmpdirname:
           _snake_case     :						str						=   Path(snake_case_     ) / "preprocessor_config.json"
           _snake_case     :						Dict						=   Path(snake_case_     ) / "config.json"
           json.dump(
               {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}						,    open(snake_case_						,    "w"     )						,    )
           json.dump({"model_type": "clip"}						,    open(snake_case_						,    "w"     )     )

           _snake_case     :						Dict						=   AutoImageProcessor.from_pretrained(snake_case_     )
           self.assertIsInstance(snake_case_						,    snake_case_     )
       def 					lowerCamelCase__	(    self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
           _snake_case     :						Tuple						=   CLIPConfig()

           # Create a dummy config file with image_proceesor_type
           _snake_case     :						Any						=   Path(snake_case_     ) / "preprocessor_config.json"
           _snake_case     :						Dict						=   Path(snake_case_     ) / "config.json"
           json.dump(
               {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}						,    open(snake_case_						,    "w"     )						,    )
           json.dump({"model_type": "clip"}						,    open(snake_case_						,    "w"     )     )

           # remove image_processor_type to make sure config.json alone is enough to load image processor locally
           _snake_case     :						int						=   AutoImageProcessor.from_pretrained(snake_case_     ).to_dict()

           config_dict.pop("image_processor_type"     )
           _snake_case     :						Dict						=   CLIPImageProcessor(**snake_case_     )

           # save in new folder
           model_config.save_pretrained(snake_case_     )
           config.save_pretrained(snake_case_     )

           _snake_case     :						List[Any]						=   AutoImageProcessor.from_pretrained(snake_case_     )

           # make sure private variable is not incorrectly saved
           _snake_case     :						Union[str, Any]						=   json.loads(config.to_json_string()     )
           self.assertTrue("_processor_class" not in dict_as_saved     )

         self.assertIsInstance(snake_case_						,    snake_case_     )
       def 					lowerCamelCase__	(    self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
           _snake_case     :						Tuple						=   Path(snake_case_     ) / "preprocessor_config.json"
           json.dump(
               {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}						,    open(snake_case_						,    "w"     )						,    )

           _snake_case     :						Optional[Any]						=   AutoImageProcessor.from_pretrained(snake_case_     )
           self.assertIsInstance(snake_case_						,    snake_case_     )
       def 					lowerCamelCase__	(    self     ):
         with self.assertRaisesRegex(
             snake_case_						,    "clip-base is not a local folder and is not a valid model identifier"     ):
           _snake_case     :						Optional[Any]						=   AutoImageProcessor.from_pretrained("clip-base"     )
       def 					lowerCamelCase__	(    self     ):
         with self.assertRaisesRegex(
             snake_case_						,    r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"     ):
           _snake_case     :						List[str]						=   AutoImageProcessor.from_pretrained(snake_case_						,    revision="aaaaaa"     )
       def 					lowerCamelCase__	(    self     ):
         with self.assertRaisesRegex(
             snake_case_						,    "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."						,    ):
           _snake_case     :						str						=   AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"     )
       def 					lowerCamelCase__	(    self     ):
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(snake_case_     ):
           _snake_case     :						str						=   AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"     )
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(snake_case_     ):
           _snake_case     :						Union[str, Any]						=   AutoImageProcessor.from_pretrained(
               "hf-internal-testing/test_dynamic_image_processor"						,    trust_remote_code=snake_case_     )

         _snake_case     :						Dict						=   AutoImageProcessor.from_pretrained(
             "hf-internal-testing/test_dynamic_image_processor"						,    trust_remote_code=snake_case_     )
         self.assertEqual(image_processor.__class__.__name__						,    "NewImageProcessor"     )

         # Test image processor can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(snake_case_     )
           _snake_case     :						Optional[Any]						=   AutoImageProcessor.from_pretrained(snake_case_						,    trust_remote_code=snake_case_     )
         self.assertEqual(reloaded_image_processor.__class__.__name__						,    "NewImageProcessor"     )
       def 					lowerCamelCase__	(    self     ):
         try:
           AutoConfig.register("custom"						,    snake_case_     )
           AutoImageProcessor.register(snake_case_						,    snake_case_     )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(snake_case_     ):
             AutoImageProcessor.register(snake_case_						,    snake_case_     )

           with tempfile.TemporaryDirectory() as tmpdirname:
             _snake_case     :						Optional[Any]						=   Path(snake_case_     ) / "preprocessor_config.json"
             _snake_case     :						Dict						=   Path(snake_case_     ) / "config.json"
             json.dump(
                 {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}						,    open(snake_case_						,    "w"     )						,    )
             json.dump({"model_type": "clip"}						,    open(snake_case_						,    "w"     )     )

             _snake_case     :						Tuple						=   CustomImageProcessor.from_pretrained(snake_case_     )

           # Now that the config is registered, it can be used as any other config with the auto-API
           with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(snake_case_     )
             _snake_case     :						Optional[Any]						=   AutoImageProcessor.from_pretrained(snake_case_     )
             self.assertIsInstance(snake_case_						,    snake_case_     )

         finally:
           if "custom" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
             del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


       def 					lowerCamelCase__	(    self     ):






         class      _UpperCAmelCase				(					_snake_case):
                __lowercase							: str  =					True

         try:
           AutoConfig.register("custom"						,    snake_case_     )
           AutoImageProcessor.register(snake_case_						,    snake_case_     )
           # If remote code is not set, the default is to use local
           _snake_case     :						Union[str, Any]						=   AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"     )
           self.assertEqual(image_processor.__class__.__name__						,    "NewImageProcessor"     )
           self.assertTrue(image_processor.is_local     )

           # If remote code is disabled, we load the local one.
           _snake_case     :						Union[str, Any]						=   AutoImageProcessor.from_pretrained(
               "hf-internal-testing/test_dynamic_image_processor"						,    trust_remote_code=snake_case_     )
           self.assertEqual(image_processor.__class__.__name__						,    "NewImageProcessor"     )
           self.assertTrue(image_processor.is_local     )

           # If remote is enabled, we load from the Hub
           _snake_case     :						Dict						=   AutoImageProcessor.from_pretrained(
               "hf-internal-testing/test_dynamic_image_processor"						,    trust_remote_code=snake_case_     )
           self.assertEqual(image_processor.__class__.__name__						,    "NewImageProcessor"     )
           self.assertTrue(not hasattr(snake_case_						,    "is_local"     )     )

         finally:
           if "custom" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
             del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



"""simple docstring"""
from __future__ import annotations

import requests

_a      :			List[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 				a__   (   a   :				str       ,     a   :				int = 1       ,     a   :				str = "new"       ,     a   :				list | None = None		):

  """simple docstring"""







  _snake_case     :						Any						=   wanted_data or []
  if invalid_search_terms := ", ".join(sorted(set(a		) - valid_terms		)		):
    _snake_case     :						Optional[int]						=   f'Invalid search term: {invalid_search_terms}'
    raise ValueError(a		)
  _snake_case     :						int						=   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

  _snake_case     :						Optional[Any]						=   response.json()
  if not wanted_data:
    return {id_: data["data"]["children"][id_] for id_ in range(a		)}

  _snake_case     :						Tuple						=   {}
  for id_ in range(a		):
    _snake_case     :						List[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"""]))

