
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_a      :			Any					=							logging.get_logger(__name__)

_a      :			Union[str, Any]					=							OrderedDict(
    [
        ("""align""", """EfficientNetImageProcessor"""),
        ("""beit""", """BeitImageProcessor"""),
        ("""bit""", """BitImageProcessor"""),
        ("""blip""", """BlipImageProcessor"""),
        ("""blip-2""", """BlipImageProcessor"""),
        ("""bridgetower""", """BridgeTowerImageProcessor"""),
        ("""chinese_clip""", """ChineseCLIPImageProcessor"""),
        ("""clip""", """CLIPImageProcessor"""),
        ("""clipseg""", """ViTImageProcessor"""),
        ("""conditional_detr""", """ConditionalDetrImageProcessor"""),
        ("""convnext""", """ConvNextImageProcessor"""),
        ("""convnextv2""", """ConvNextImageProcessor"""),
        ("""cvt""", """ConvNextImageProcessor"""),
        ("""data2vec-vision""", """BeitImageProcessor"""),
        ("""deformable_detr""", """DeformableDetrImageProcessor"""),
        ("""deit""", """DeiTImageProcessor"""),
        ("""deta""", """DetaImageProcessor"""),
        ("""detr""", """DetrImageProcessor"""),
        ("""dinat""", """ViTImageProcessor"""),
        ("""donut-swin""", """DonutImageProcessor"""),
        ("""dpt""", """DPTImageProcessor"""),
        ("""efficientformer""", """EfficientFormerImageProcessor"""),
        ("""efficientnet""", """EfficientNetImageProcessor"""),
        ("""flava""", """FlavaImageProcessor"""),
        ("""focalnet""", """BitImageProcessor"""),
        ("""git""", """CLIPImageProcessor"""),
        ("""glpn""", """GLPNImageProcessor"""),
        ("""groupvit""", """CLIPImageProcessor"""),
        ("""imagegpt""", """ImageGPTImageProcessor"""),
        ("""instructblip""", """BlipImageProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
        ("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
        ("""levit""", """LevitImageProcessor"""),
        ("""mask2former""", """Mask2FormerImageProcessor"""),
        ("""maskformer""", """MaskFormerImageProcessor"""),
        ("""mgp-str""", """ViTImageProcessor"""),
        ("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
        ("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevitv2""", """MobileViTImageProcessor"""),
        ("""nat""", """ViTImageProcessor"""),
        ("""oneformer""", """OneFormerImageProcessor"""),
        ("""owlvit""", """OwlViTImageProcessor"""),
        ("""perceiver""", """PerceiverImageProcessor"""),
        ("""pix2struct""", """Pix2StructImageProcessor"""),
        ("""poolformer""", """PoolFormerImageProcessor"""),
        ("""regnet""", """ConvNextImageProcessor"""),
        ("""resnet""", """ConvNextImageProcessor"""),
        ("""sam""", """SamImageProcessor"""),
        ("""segformer""", """SegformerImageProcessor"""),
        ("""swiftformer""", """ViTImageProcessor"""),
        ("""swin""", """ViTImageProcessor"""),
        ("""swin2sr""", """Swin2SRImageProcessor"""),
        ("""swinv2""", """ViTImageProcessor"""),
        ("""table-transformer""", """DetrImageProcessor"""),
        ("""timesformer""", """VideoMAEImageProcessor"""),
        ("""tvlt""", """TvltImageProcessor"""),
        ("""upernet""", """SegformerImageProcessor"""),
        ("""van""", """ConvNextImageProcessor"""),
        ("""videomae""", """VideoMAEImageProcessor"""),
        ("""vilt""", """ViltImageProcessor"""),
        ("""vit""", """ViTImageProcessor"""),
        ("""vit_hybrid""", """ViTHybridImageProcessor"""),
        ("""vit_mae""", """ViTImageProcessor"""),
        ("""vit_msn""", """ViTImageProcessor"""),
        ("""xclip""", """CLIPImageProcessor"""),
        ("""yolos""", """YolosImageProcessor"""),
    ]
)

_a      :			Optional[Any]					=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 				a__   (   a   :				str		):

  """simple docstring"""







  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _snake_case     :						Any						=   model_type_to_module_name(a		)

      _snake_case     :						Tuple						=   importlib.import_module(f'.{module_name}'       ,     "transformers.models"		)
      try:
        return getattr(a       ,     a		)
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(a       ,     "__name__"       ,     a		) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _snake_case     :						List[str]						=   importlib.import_module("transformers"		)
  if hasattr(a       ,     a		):
    return getattr(a       ,     a		)

  return None
def 				a__   (   a   :				Union[str, os.PathLike]       ,     a   :				Optional[Union[str, os.PathLike]] = None       ,     a   :				bool = False       ,     a   :				bool = False       ,     a   :				Optional[Dict[str, str]] = None       ,     a   :				Optional[Union[bool, str]] = None       ,     a   :				Optional[str] = None       ,     a   :				bool = False       ,     **a   :				Any       ,     ):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   get_file_from_repo(
      a       ,     a       ,     cache_dir=a       ,     force_download=a       ,     resume_download=a       ,     proxies=a       ,     use_auth_token=a       ,     revision=a       ,     local_files_only=a       ,     )
  if resolved_config_file is None:
    logger.info(
        "Could not locate the image processor configuration file, will try to use the model config instead."		)
    return {}

  with open(a       ,     encoding="utf-8"		) as reader:
    return json.load(a		)






class      _UpperCAmelCase				:
       def __init__(    self     ):
         raise EnvironmentError(
             "AutoImageProcessor is designed to be instantiated "
             "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."     )
       @classmethod
       @replace_list_option_in_docstrings(snake_case_     )
       def 					lowerCamelCase__	(    cls						,    snake_case_						,    **snake_case_     ):
         _snake_case     :						str						=   kwargs.pop("config"						,    snake_case_     )
         _snake_case     :						int						=   kwargs.pop("trust_remote_code"						,    snake_case_     )
         _snake_case     :						List[str]						=   True

         _snake_case     :						Union[str, Any]						=   ImageProcessingMixin.get_image_processor_dict(snake_case_						,    **snake_case_     )
         _snake_case     :						int						=   config_dict.get("image_processor_type"						,    snake_case_     )
         _snake_case     :						Optional[Any]						=   None
         if "AutoImageProcessor" in config_dict.get("auto_map"						,    {}     ):
           _snake_case     :						str						=   config_dict["""auto_map"""]["""AutoImageProcessor"""]

         # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
         # and if so, infer the image processor class from there.
         if image_processor_class is None and image_processor_auto_map is None:
           _snake_case     :						Union[str, Any]						=   config_dict.pop("feature_extractor_type"						,    snake_case_     )
           if feature_extractor_class is not None:
             logger.warning(
                 "Could not find image processor class in the image processor config or the model config. Loading"
                 " based on pattern matching with the model's feature extractor configuration."     )
             _snake_case     :						int						=   feature_extractor_class.replace("FeatureExtractor"						,    "ImageProcessor"     )
           if "AutoFeatureExtractor" in config_dict.get("auto_map"						,    {}     ):
             _snake_case     :						int						=   config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
             _snake_case     :						int						=   feature_extractor_auto_map.replace("FeatureExtractor"						,    "ImageProcessor"     )
             logger.warning(
                 "Could not find image processor auto map in the image processor config or the model config."
                 " Loading based on pattern matching with the model's feature extractor configuration."     )

        # If we don't find the image processor class in the image processor config, let's try the model config.
         if image_processor_class is None and image_processor_auto_map is None:
           if not isinstance(snake_case_						,    snake_case_     ):
             _snake_case     :						Any						=   AutoConfig.from_pretrained(snake_case_						,    **snake_case_     )
           # It could be in `config.image_processor_type``
           _snake_case     :						int						=   getattr(snake_case_						,    "image_processor_type"						,    snake_case_     )
           if hasattr(snake_case_						,    "auto_map"     ) and "AutoImageProcessor" in config.auto_map:
             _snake_case     :						Optional[Any]						=   config.auto_map["""AutoImageProcessor"""]

         if image_processor_class is not None:
           _snake_case     :						Optional[Any]						=   image_processor_class_from_name(snake_case_     )

         _snake_case     :						Optional[Any]						=   image_processor_auto_map is not None
         _snake_case     :						Dict						=   image_processor_class is not None or type(snake_case_     ) in IMAGE_PROCESSOR_MAPPING
         _snake_case     :						Dict						=   resolve_trust_remote_code(
             snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     )

         if has_remote_code and trust_remote_code:
           _snake_case     :						Union[str, Any]						=   get_class_from_dynamic_module(
               snake_case_						,    snake_case_						,    **snake_case_     )
           _snake_case     :						Optional[int]						=   kwargs.pop("code_revision"						,    snake_case_     )
           if os.path.isdir(snake_case_     ):
             image_processor_class.register_for_auto_class()
           return image_processor_class.from_dict(snake_case_						,    **snake_case_     )
         elif image_processor_class is not None:
           return image_processor_class.from_dict(snake_case_						,    **snake_case_     )
         # Last try: we use the IMAGE_PROCESSOR_MAPPING.
         elif type(snake_case_     ) in IMAGE_PROCESSOR_MAPPING:
           _snake_case     :						List[Any]						=   IMAGE_PROCESSOR_MAPPING[type(snake_case_     )]
           return image_processor_class.from_dict(snake_case_						,    **snake_case_     )

         raise ValueError(
             F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
             F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
             F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()     )}'     )


       @staticmethod
       def 					lowerCamelCase__	(    snake_case_						,    snake_case_     ):
         IMAGE_PROCESSOR_MAPPING.register(snake_case_						,    snake_case_     )



"""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"""]))

