

'''simple docstring'''



import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class       SCREAMING_SNAKE_CASE       (				pl.LightningModule       ):






      '''simple docstring'''






      def __init__(       self			:		Optional[Any]  ,       snake_case			:		Union[str, Any] ):



          """simple docstring"""





          super().__init__()
          _snake_case			:			int				       =		model
          _snake_case			:			List[str]				       =		2
          _snake_case			:			int				       =		nn.Linear(self.model.config.hidden_size  ,       self.num_labels )




      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          pass



def 	lowerCamelCase__							(   a__      ,   a__      ,   a__)		->     List[Any]:



    """simple docstring"""







    _snake_case			:			Optional[int]				       =		LongformerModel.from_pretrained(lowercase_)
    _snake_case			:			Optional[int]				       =		LightningModel(lowercase_)

    _snake_case			:			Any				       =		torch.load(lowercase_      ,   map_location=torch.device('cpu'))
    lightning_model.load_state_dict(ckpt['state_dict'])

    # init longformer question answering model
    _snake_case			:			List[str]				       =		LongformerForQuestionAnswering.from_pretrained(lowercase_)

    # transfer weights
    longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
    longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
    longformer_for_qa.eval()

    # save model
    longformer_for_qa.save_pretrained(lowercase_)

    print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""")


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_     = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--longformer_model",
       default=None,
       type=str,
       required=True,
       help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
   )
   parser.add_argument(
       "--longformer_question_answering_ckpt_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch Lightning Checkpoint.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE_     = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )









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"""]))


