
"""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		       =							logging.get_logger(__name__)

_a		       =							[
    ('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		       =							[
    '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     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    torch.load(__lowerCamelCase,		map_location="cpu"  )
   return sd







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







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   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_					:					List[str]			     =    "pretraining"
      if "vcr" in checkpoint_path:
         UpperCAmelCase_					:					Optional[int]			     =    {"visual_embedding_dim": 512}
      elif "vqa_advanced" in checkpoint_path:
         UpperCAmelCase_					:					Optional[Any]			     =    {"visual_embedding_dim": 2048}
      elif "vqa" in checkpoint_path:
         UpperCAmelCase_					:					Tuple			     =    {"visual_embedding_dim": 2048}
      elif "nlvr" in checkpoint_path:
         UpperCAmelCase_					:					List[str]			     =    {"visual_embedding_dim": 1024}
      else:
         raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""  )
   else:
      if "vcr" in checkpoint_path:
         UpperCAmelCase_					:					int			     =    {"visual_embedding_dim": 512}
         UpperCAmelCase_					:					List[Any]			     =    "multichoice"
      elif "vqa_advanced" in checkpoint_path:
         UpperCAmelCase_					:					Dict			     =    {"visual_embedding_dim": 2048}
         UpperCAmelCase_					:					List[str]			     =    "vqa_advanced"
      elif "vqa" in checkpoint_path:
         UpperCAmelCase_					:					List[Any]			     =    {"visual_embedding_dim": 2048, "num_labels": 3129}
         UpperCAmelCase_					:					Dict			     =    "vqa"
      elif "nlvr" in checkpoint_path:
         UpperCAmelCase_					:					Any			     =    {
             "visual_embedding_dim": 1024,
             "num_labels": 2,
         }
         UpperCAmelCase_					:					List[Any]			     =    "nlvr"

   UpperCAmelCase_					:					int			     =    VisualBertConfig(**__lowerCamelCase  )

   # Load State Dict
   UpperCAmelCase_					:					List[Any]			     =    load_state_dict(__lowerCamelCase  )

   UpperCAmelCase_					:					Any			     =    get_new_dict(__lowerCamelCase,		__lowerCamelCase  )

   if model_type == "pretraining":
      UpperCAmelCase_					:					Optional[int]			     =    VisualBertForPreTraining(__lowerCamelCase  )
   elif model_type == "vqa":
      UpperCAmelCase_					:					List[Any]			     =    VisualBertForQuestionAnswering(__lowerCamelCase  )
   elif model_type == "nlvr":
      UpperCAmelCase_					:					Optional[int]			     =    VisualBertForVisualReasoning(__lowerCamelCase  )
   elif model_type == "multichoice":
      UpperCAmelCase_					:					Union[str, Any]			     =    VisualBertForMultipleChoice(__lowerCamelCase  )

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


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




"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    set(__lowerCamelCase  ), [start]

   while stack:
      UpperCAmelCase_					:					Any			     =    stack.pop()
      explored.add(__lowerCamelCase  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(__lowerCamelCase  )
   return explored


_a		       =							{
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, 'A'))


