




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()
_lowerCamelCase	:		Optional[int]							       =   logging.get_logger(__name__)

_lowerCamelCase	:		List[str]							       =   [
    ("""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"""),
]

_lowerCamelCase	:		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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       A__       =    torch.load(lowercase_ ,				map_location='''cpu'''						)
       return sd







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=rename_keys_prefix						)   ->       Optional[int]:



       """simple docstring"""

       A__       =    OrderedDict()
       A__       =    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
              A__       =    key
              for name_pair in rename_keys_prefix:
                     A__       =    new_key.replace(name_pair[0] ,				name_pair[1]						)
              A__       =    d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     A__       =    new_d['''cls.predictions.bias''']
       return new_d







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       List[str]:



       """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:
              A__       =    '''pretraining'''
              if "vcr" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 512}
              elif "vqa_advanced" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 2_048}
              elif "vqa" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 2_048}
              elif "nlvr" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 1_024}
              else:
                     raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""						)
       else:
              if "vcr" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 512}
                     A__       =    '''multichoice'''
              elif "vqa_advanced" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 2_048}
                     A__       =    '''vqa_advanced'''
              elif "vqa" in checkpoint_path:
                     A__       =    {'''visual_embedding_dim''': 2_048, '''num_labels''': 3_129}
                     A__       =    '''vqa'''
              elif "nlvr" in checkpoint_path:
                     A__       =    {
                         '''visual_embedding_dim''': 1_024,
                         '''num_labels''': 2,
                     }
                     A__       =    '''nlvr'''

       A__       =    VisualBertConfig(**lowercase_						)

       # Load State Dict
       A__       =    load_state_dict(lowercase_						)

       A__       =    get_new_dict(lowercase_ ,				lowercase_						)

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

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


if __name__ == "__main__":
  _lowerCamelCase	:		Tuple							       =   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.""")
  _lowerCamelCase	:		str							       =   parser.parse_args()
  convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       tuple[float, list[float]]:



       """simple docstring"""

       A__       =    list(range(len(lowercase_						)						)						)
       A__       =    [v / w for v, w in zip(lowercase_ ,				lowercase_						)]
       index.sort(key=lambda lowercase_						: ratio[i] ,				reverse=lowercase_						)

       A__       =    0
       A__       =    [0] * len(lowercase_						)
       for i in index:
              if weight[i] <= capacity:
                     A__       =    1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     A__       =    capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()


