"""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()
_UpperCamelCase   :						int      =    logging.get_logger(__name__)

_UpperCamelCase   :						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"),
]

_UpperCamelCase   :						Dict      =    [
    "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  : Optional[Any]     ):





       '''simple docstring'''

       lowercase__			:		Dict       =	torch.load(_lowerCAmelCase   , map_location='cpu'     )
       return sd





def 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : str=rename_keys_prefix     ):





       '''simple docstring'''

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





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

       lowercase__			:		Optional[Any]       =	VisualBertConfig(**_lowerCAmelCase     )

       # Load State Dict
       lowercase__			:		List[Any]       =	load_state_dict(_lowerCAmelCase     )

       lowercase__			:		str       =	get_new_dict(_lowerCAmelCase   , _lowerCAmelCase     )

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

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


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







'''simple docstring'''







class 			_a  :
       def __init__(	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		{}  # Mapping from char to TrieNode
           UpperCAmelCase			       =		False
       def     A      (	self						:	int   ,				lowercase						:	list[str]				):



           '''simple docstring'''

           for word in words:
               self.insert(lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self
           for char in word:
               if char not in curr.nodes:
                   UpperCAmelCase			       =		TrieNode()
               UpperCAmelCase			       =		curr.nodes[char]
           UpperCAmelCase			       =		True
       def     A      (	self						:	Optional[int]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self
           for char in word:
               if char not in curr.nodes:
                   return False
               UpperCAmelCase			       =		curr.nodes[char]
           return curr.is_leaf







       def     A      (	self						:	str   ,				lowercase						:	str				):



           '''simple docstring'''

           def _delete(lowercase						:	TrieNode   ,				lowercase						:	str   ,				lowercase						:	int				) -> bool:
               if index == len(lowercase				):
                   # If word does not exist
                   if not curr.is_leaf:
                       return False
                   UpperCAmelCase			       =		False
                   return len(curr.nodes				) == 0
               UpperCAmelCase			       =		word[index]
               UpperCAmelCase			       =		curr.nodes.get(lowercase				)
               # If char not in current trie node
               if not char_node:
                   return False
               # Flag to check if node can be deleted
               UpperCAmelCase			       =		_delete(lowercase   ,				lowercase   ,				index + 1				)
               if delete_curr:
                   del curr.nodes[char]
                   return len(curr.nodes				) == 0
               return delete_curr

           _delete(self   ,				lowercase   ,				0				)






def 						snake_case_							(_a	:							TrieNode    ,   _a	:							str     ):
    if node.is_leaf:
        print(_a    ,   end=''' '''     )

    for key, value in node.nodes.items():
        print_words(_a    ,   word + key     )





def 						snake_case_							():
    UpperCAmelCase			       =		'''banana bananas bandana band apple all beast'''.split()
    UpperCAmelCase			       =		TrieNode()
    root.insert_many(_a     )
    # print_words(root, "")
    assert all(root.find(_a     ) for word in words     )
    assert root.find('''banana'''     )
    assert not root.find('''bandanas'''     )
    assert not root.find('''apps'''     )
    assert root.find('''apple'''     )
    assert root.find('''all'''     )
    root.delete('''all'''     )
    assert not root.find('''all'''     )
    root.delete('''banana'''     )
    assert not root.find('''banana'''     )
    assert root.find('''bananas'''     )
    return True





def 						snake_case_							(_a	:							str    ,   _a	:							bool     ):
    print(str(_a     )    ,   '''works!''' if passes else '''doesn\'t work :('''     )





def 						snake_case_							():
    assert test_trie()





def 						snake_case_							():
    print_results('''Testing trie functionality'''    ,   test_trie()     )


if __name__ == "__main__":
   main()




