





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
    from PIL import Image

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'dandelin/vilt-b32-finetuned-vqa'
 __UpperCamelCase       =      (
     'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
     'image containing the information, as well as a `question` which should be the question in English. It '
     'returns a text that is the answer to the question.'
 )
 __UpperCamelCase       =      'image_qa'
 __UpperCamelCase       =      AutoProcessor
 __UpperCamelCase       =      AutoModelForVisualQuestionAnswering

 __UpperCamelCase       =      ['image', 'text']
 __UpperCamelCase       =      ['text']

 def __init__(  self,		*A_,		**A_		)	->       Optional[Any]:
    requires_backends(self,		["vision"]		)
    super().__init__(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Optional[int]:
    return self.pre_processor(A_,		A_,		return_tensors="pt"		)

 def  __UpperCAmelCase						(  self,		A_		)	->       int:
    with torch.no_grad():
       return self.model(**A_		).logits






 def  __UpperCAmelCase						(  self,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=outputs.argmax(-1		).item()
    return self.model.config.idalabel[idx]










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

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

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

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          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']))



