
"""simple docstring"""
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()
_a      :			List[str]					=							logging.get_logger(__name__)

_a      :			Tuple					=							[
    ("""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"""),
]

_a      :			Optional[int]					=							[
    """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 				a__   (   a   :				Optional[int]		):

  """simple docstring"""







  _snake_case     :						Any						=   torch.load(UpperCamelCase__       ,     map_location="cpu"		)
  return sd
def 				a__   (   a   :				Optional[int]       ,     a   :				Union[str, Any]       ,     a   :				Dict=rename_keys_prefix		):

  """simple docstring"""







  _snake_case     :						Tuple						=   OrderedDict()
  _snake_case     :						List[Any]						=   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
    _snake_case     :						Union[str, Any]						=   key
    for name_pair in rename_keys_prefix:
      _snake_case     :						Optional[int]						=   new_key.replace(name_pair[0]       ,     name_pair[1]		)
    _snake_case     :						List[str]						=   d[key]
    if key == "bert.cls.predictions.decoder.weight":
      # Old bert code didn't have `decoder.bias`, but was added separately
      _snake_case     :						Tuple						=   new_d["cls.predictions.bias"]
  return new_d
@torch.no_grad()
def 				a__   (   a   :				Any       ,     a   :				Dict		):

  """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:
    _snake_case     :						Any						=   "pretraining"
    if "vcr" in checkpoint_path:
      _snake_case     :						List[Any]						=   {"visual_embedding_dim": 512}
    elif "vqa_advanced" in checkpoint_path:
      _snake_case     :						Any						=   {"visual_embedding_dim": 2_048}
    elif "vqa" in checkpoint_path:
      _snake_case     :						Any						=   {"visual_embedding_dim": 2_048}
    elif "nlvr" in checkpoint_path:
      _snake_case     :						Optional[Any]						=   {"visual_embedding_dim": 1_024}
    else:
      raise NotImplementedError(f'No implementation found for `{checkpoint_path}`.'		)
  else:
    if "vcr" in checkpoint_path:
      _snake_case     :						Union[str, Any]						=   {"visual_embedding_dim": 512}
      _snake_case     :						Tuple						=   "multichoice"
    elif "vqa_advanced" in checkpoint_path:
      _snake_case     :						List[str]						=   {"visual_embedding_dim": 2_048}
      _snake_case     :						List[str]						=   "vqa_advanced"
    elif "vqa" in checkpoint_path:
      _snake_case     :						List[Any]						=   {"visual_embedding_dim": 2_048, "num_labels": 3_129}
      _snake_case     :						List[str]						=   "vqa"
    elif "nlvr" in checkpoint_path:
      _snake_case     :						List[Any]						=   {
          "visual_embedding_dim": 1_024,
          "num_labels": 2,
      }
      _snake_case     :						Any						=   "nlvr"

  _snake_case     :						Tuple						=   VisualBertConfig(**UpperCamelCase__		)

  # Load State Dict
  _snake_case     :						Dict						=   load_state_dict(UpperCamelCase__		)

  _snake_case     :						Any						=   get_new_dict(UpperCamelCase__       ,     UpperCamelCase__		)

  if model_type == "pretraining":
    _snake_case     :						List[Any]						=   VisualBertForPreTraining(UpperCamelCase__		)
  elif model_type == "vqa":
    _snake_case     :						Optional[Any]						=   VisualBertForQuestionAnswering(UpperCamelCase__		)
  elif model_type == "nlvr":
    _snake_case     :						Dict						=   VisualBertForVisualReasoning(UpperCamelCase__		)
  elif model_type == "multichoice":
    _snake_case     :						Tuple						=   VisualBertForMultipleChoice(UpperCamelCase__		)

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


if __name__ == "__main__":
    _a      :			Optional[Any]					=							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.""")
    _a      :			List[Any]					=							parser.parse_args()
    convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
    _a      :			Dict					=							input("""Enter image url: """).strip()
    print(f'Downloading image from {url} ...')
    _a      :			str					=							BeautifulSoup(requests.get(url).content, """html.parser""")
    # The image URL is in the content field of the first meta tag with property og:image
    _a      :			str					=							soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
    _a      :			Dict					=							requests.get(image_url).content
    _a      :			str					=							f'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
    with open(file_name, """wb""") as fp:
        fp.write(image_data)
    print(f'Done. Image saved to disk as {file_name}.')

