'''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					:		str							      =		logging.get_logger(__name__)

_UpperCamelCase					:		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'),
]

_UpperCamelCase					:		Tuple							      =		[
    '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							:  Any      ):
 __UpperCAmelCase								=       torch.load(lowerCAmelCase   ,	map_location='cpu'      )
 return sd



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



@torch.no_grad()
def     __snake_case			(							lowerCAmelCase							:  Any   ,	lowerCAmelCase							:  Dict      ):
 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								=       'pretraining'
  if "vcr" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 512}
  elif "vqa_advanced" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 2048}
  elif "vqa" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 2048}
  elif "nlvr" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 1024}
  else:
   raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""      )
 else:
  if "vcr" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 512}
   __UpperCAmelCase								=       'multichoice'
  elif "vqa_advanced" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 2048}
   __UpperCAmelCase								=       'vqa_advanced'
  elif "vqa" in checkpoint_path:
   __UpperCAmelCase								=       {'visual_embedding_dim': 2048, 'num_labels': 3129}
   __UpperCAmelCase								=       'vqa'
  elif "nlvr" in checkpoint_path:
   __UpperCAmelCase								=       {
       'visual_embedding_dim': 1024,
       'num_labels': 2,
   }
   __UpperCAmelCase								=       'nlvr'

 __UpperCAmelCase								=       VisualBertConfig(**lowerCAmelCase      )

 # Load State Dict
 __UpperCAmelCase								=       load_state_dict(lowerCAmelCase      )

 __UpperCAmelCase								=       get_new_dict(lowerCAmelCase   ,	lowerCAmelCase      )

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

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


if __name__ == "__main__":
     _UpperCamelCase					:		Union[str, 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.')
     _UpperCamelCase					:		Tuple							      =		parser.parse_args()
     convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




		lowercase__	     =						Node(1	)
		lowercase__	     =						Node(2	)
		lowercase__	     =						Node(3	)
		lowercase__	     =						Node(4	)
		lowercase__	     =						Node(5	)
		return tree






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




		return (max(height(root.left	)       ,       height(root.right	)	) + 1) if root else 0






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						process_queue.popleft()
				output.append(node.data	)

				if node.left:
						process_queue.append(node.left	)
				if node.right:
						process_queue.append(node.right	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.left       ,       level - 1	)
						populate_output(root.right       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.right       ,       level - 1	)
						populate_output(root.left       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

		for h in range(1       ,       height_tree + 1	):
				if not flag:
						output.append(get_nodes_from_left_to_right(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						1
				else:
						output.append(get_nodes_from_right_to_left(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						0

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




		lowercase__	     =						make_tree()
		print(f'''In-order Traversal: {inorder(__magic_name__	)}'''	)
		print(f'''Pre-order Traversal: {preorder(__magic_name__	)}'''	)
		print(f'''Post-order Traversal: {postorder(__magic_name__	)}'''       ,       """\n"""	)

		print(f'''Height of Tree: {height(__magic_name__	)}'''       ,       """\n"""	)

		print("""Complete Level Order Traversal: """	)
		print(level_order(__magic_name__	)       ,       """\n"""	)

		print("""Level-wise order Traversal: """	)

		for level in range(1       ,       height(__magic_name__	) + 1	):
				print(f'''Level {level}:'''       ,       get_nodes_from_left_to_right(__magic_name__       ,       level=__magic_name__	)	)

		print("""\nZigZag order Traversal: """	)
		print(zigzag(__magic_name__	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

