






'''simple docstring'''
class    __SCREAMING_SNAKE_CASE					:




 def __init__(					self		:							Optional[int]    ,					__lowercase		:							Tuple					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__   : List[str]													=val
       SCREAMING_SNAKE_CASE__   : int													=None
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=None




 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							str					)		->  str:
       if self.val:
             if val < self.val:
                   if self.left is None:
                         SCREAMING_SNAKE_CASE__   : Tuple													=Node(a_					)
                   else:
                         self.left.insert(a_					)
             elif val > self.val:
                   if self.right is None:
                         SCREAMING_SNAKE_CASE__   : Union[str, Any]													=Node(a_					)
                   else:
                         self.right.insert(a_					)
       else:
             SCREAMING_SNAKE_CASE__   : Optional[int]													=val





def        _a(     UpperCamelCase__ :  Union[str, Any],		UpperCamelCase__ :  List[Any]							):




      '''simple docstring'''






      if root:
            inorder(root.left,		lowerCAmelCase_							)
            res.append(root.val							)
            inorder(root.right,		lowerCAmelCase_							)





def        _a(     UpperCamelCase__ :  List[str]							):




      '''simple docstring'''






      if len(lowerCAmelCase_							) == 0:
            return arr
      SCREAMING_SNAKE_CASE__   : Optional[int]													=Node(arr[0]							)
      for i in range(1,		len(lowerCAmelCase_							)							):
            root.insert(arr[i]							)
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE__   : Any													=[]
      inorder(lowerCAmelCase_,		lowerCAmelCase_							)
      return res


if __name__ == "__main__":
  print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))




'''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_							: Union[str, Any]						 =	logging.get_logger(__name__)

A_							: Union[str, Any]						 =	[
    ("""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_							: 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     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)
       return sd




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=rename_keys_prefix		)->		Optional[Any]:





       '''simple docstring'''


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




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''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  :						Optional[int]			     = """pretraining"""
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						Optional[int]			     = {"""visual_embedding_dim""": 512}
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						List[Any]			     = {"""visual_embedding_dim""": 2048}
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 2048}
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 1024}
              else:
                     raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''		)
       else:
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 512}
                     _UpperCAmelCase  :						int			     = """multichoice"""
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 2048}
                     _UpperCAmelCase  :						int			     = """vqa_advanced"""
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						List[str]			     = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
                     _UpperCAmelCase  :						int			     = """vqa"""
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						int			     = {
                         """visual_embedding_dim""": 1024,
                         """num_labels""": 2,
                     }
                     _UpperCAmelCase  :						Optional[Any]			     = """nlvr"""

       _UpperCAmelCase  :						int			     = VisualBertConfig(**lowerCAmelCase_		)

       # Load State Dict
       _UpperCAmelCase  :						Any			     = load_state_dict(lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[int]			     = get_new_dict(lowerCAmelCase_       ,   lowerCAmelCase_		)

       if model_type == "pretraining":
              _UpperCAmelCase  :						List[str]			     = VisualBertForPreTraining(lowerCAmelCase_		)
       elif model_type == "vqa":
              _UpperCAmelCase  :						Optional[int]			     = VisualBertForQuestionAnswering(lowerCAmelCase_		)
       elif model_type == "nlvr":
              _UpperCAmelCase  :						str			     = VisualBertForVisualReasoning(lowerCAmelCase_		)
       elif model_type == "multichoice":
              _UpperCAmelCase  :						Dict			     = VisualBertForMultipleChoice(lowerCAmelCase_		)

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


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_							: int						 =	parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



