'''simple docstring'''

from __future__ import annotations

import requests

__a:			Optional[Any]	       =       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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase = 1		,					UpperCAmelCase = "new"		,					UpperCAmelCase = None					):
 lowercase__    :   List[Any]						= wanted_data or []
 if invalid_search_terms := ", ".join(sorted(set(UpperCAmelCase					) - valid_terms					)					):
  lowercase__    :   Union[str, Any]						= F"""Invalid search term: {invalid_search_terms}"""
  raise ValueError(UpperCAmelCase					)
 lowercase__    :   Any						= 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

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

 lowercase__    :   Any						= {}
 for id_ in range(UpperCAmelCase					):
  lowercase__    :   Tuple						= {
      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"""]))


'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

__a:			Optional[Any]	       =       logging.get_logger(__name__)



@add_end_docstrings(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       **__lowerCAmelCase			)  ->  int:
        super().__init__(**__lowerCAmelCase			)

        requires_backends(self     ,       '''vision'''			)
        self.check_model_type(
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == '''tf'''
            else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING			)

       def __call__(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[str]:
        return super().__call__(__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   str						= {}
        if "candidate_labels" in kwargs:
         lowercase__    :   str						= kwargs['''candidate_labels''']
        if "hypothesis_template" in kwargs:
         lowercase__    :   str						= kwargs['''hypothesis_template''']

        return preprocess_params, {}, {}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="This is a photo of {}."			)  ->  Any:
        lowercase__    :   Union[str, Any]						= load_image(__lowerCAmelCase			)
        lowercase__    :   List[str]						= self.image_processor(images=[image]     ,       return_tensors=self.framework			)
        lowercase__    :   Union[str, Any]						= candidate_labels
        lowercase__    :   int						= [hypothesis_template.format(__lowerCAmelCase			) for x in candidate_labels]
        lowercase__    :   Any						= self.tokenizer(__lowerCAmelCase     ,       return_tensors=self.framework     ,       padding=__lowerCAmelCase			)
        lowercase__    :   Any						= [text_inputs]
        return inputs

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Any						= model_inputs.pop('''candidate_labels'''			)
        lowercase__    :   int						= model_inputs.pop('''text_inputs'''			)
        if isinstance(text_inputs[0]     ,       __lowerCAmelCase			):
         lowercase__    :   Union[str, Any]						= text_inputs[0]
        else:
         # Batching case.
         lowercase__    :   Optional[Any]						= text_inputs[0][0]

        lowercase__    :   Any						= self.model(**__lowerCAmelCase     ,       **__lowerCAmelCase			)

        lowercase__    :   Any						= {
            '''candidate_labels''': candidate_labels,
            '''logits''': outputs.logits_per_image,
        }
        return model_outputs


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   Union[str, Any]						= model_outputs.pop('''candidate_labels'''			)
        lowercase__    :   Optional[int]						= model_outputs['''logits'''][0]
        if self.framework == "pt":
         lowercase__    :   Optional[int]						= logits.softmax(dim=-1			).squeeze(-1			)
         lowercase__    :   Any						= probs.tolist()
         if not isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Dict						= [scores]
        elif self.framework == "tf":
         lowercase__    :   List[Any]						= stable_softmax(__lowerCAmelCase     ,       axis=-1			)
         lowercase__    :   str						= probs.numpy().tolist()
        else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""			)

        lowercase__    :   Optional[int]						= [
            {'''score''': score, '''label''': candidate_label}
            for score, candidate_label in sorted(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       key=lambda __lowerCAmelCase			: -x[0]			)
        ]
        return result

