





import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

UpperCamelCase_												=	[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=torch.load(A				,							map_location="cpu"     )
   return sd






def 				_UpperCAmelCase			(    A				,							A				,							A=rename_keys_prefix     ):




   '''simple docstring'''

   UpperCAmelCase__						=OrderedDict()
   UpperCAmelCase__						=torch.arange(config.max_position_embeddings     ).expand((1, -1)     )
   # detector_d = OrderedDict()
   for key in d:
      if "detector" in key:
         # detector_d[key.replace('detector.','')] = d[key]
         continue
      UpperCAmelCase__						=key
      for name_pair in rename_keys_prefix:
         UpperCAmelCase__						=new_key.replace(name_pair[0]				,							name_pair[1]     )
      UpperCAmelCase__						=d[key]
      if key == "bert.cls.predictions.decoder.weight":
         # Old bert code didn't have `decoder.bias`, but was added separately
         UpperCAmelCase__						=new_d["cls.predictions.bias"]
   return new_d






@torch.no_grad()
def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   assert (
       checkpoint_path.split("/"     )[-1] in ACCEPTABLE_CHECKPOINTS
   ), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

   # Get Config
   if "pre" in checkpoint_path:
      UpperCAmelCase__						="pretraining"
      if "vcr" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 512}
      elif "vqa_advanced" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 2048}
      elif "vqa" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 2048}
      elif "nlvr" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 1024}
      else:
         raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""     )
   else:
      if "vcr" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 512}
         UpperCAmelCase__						="multichoice"
      elif "vqa_advanced" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 2048}
         UpperCAmelCase__						="vqa_advanced"
      elif "vqa" in checkpoint_path:
         UpperCAmelCase__						={"visual_embedding_dim": 2048, "num_labels": 3129}
         UpperCAmelCase__						="vqa"
      elif "nlvr" in checkpoint_path:
         UpperCAmelCase__						={
             "visual_embedding_dim": 1024,
             "num_labels": 2,
         }
         UpperCAmelCase__						="nlvr"

   UpperCAmelCase__						=VisualBertConfig(**A     )

   # Load State Dict
   UpperCAmelCase__						=load_state_dict(A     )

   UpperCAmelCase__						=get_new_dict(A				,							A     )

   if model_type == "pretraining":
      UpperCAmelCase__						=VisualBertForPreTraining(A     )
   elif model_type == "vqa":
      UpperCAmelCase__						=VisualBertForQuestionAnswering(A     )
   elif model_type == "nlvr":
      UpperCAmelCase__						=VisualBertForVisualReasoning(A     )
   elif model_type == "multichoice":
      UpperCAmelCase__						=VisualBertForMultipleChoice(A     )

   model.load_state_dict(A     )
   # Save Checkpoints
   Path(A     ).mkdir(exist_ok=A     )
   model.save_pretrained(A     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
    UpperCamelCase_												=	parser.parse_args()
    convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)










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']))



