




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

__A   :  List[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   :  Any					=      [
    '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"""


		__lowercase				:		Optional[int]												=				torch.load(_lowerCamelCase,  map_location="cpu"				)
		return sd

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=rename_keys_prefix				)			->Tuple:






		"""simple docstring"""


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

@torch.no_grad()
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Union[str, 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:
				__lowercase				:		int												=				"pretraining"
				if "vcr" in checkpoint_path:
						__lowercase				:		int												=				{"visual_embedding_dim": 5_12}
				elif "vqa_advanced" in checkpoint_path:
						__lowercase				:		List[Any]												=				{"visual_embedding_dim": 20_48}
				elif "vqa" in checkpoint_path:
						__lowercase				:		List[Any]												=				{"visual_embedding_dim": 20_48}
				elif "nlvr" in checkpoint_path:
						__lowercase				:		int												=				{"visual_embedding_dim": 10_24}
				else:
						raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'				)
		else:
				if "vcr" in checkpoint_path:
						__lowercase				:		Union[str, Any]												=				{"visual_embedding_dim": 5_12}
						__lowercase				:		Any												=				"multichoice"
				elif "vqa_advanced" in checkpoint_path:
						__lowercase				:		List[Any]												=				{"visual_embedding_dim": 20_48}
						__lowercase				:		int												=				"vqa_advanced"
				elif "vqa" in checkpoint_path:
						__lowercase				:		List[Any]												=				{"visual_embedding_dim": 20_48, "num_labels": 31_29}
						__lowercase				:		Any												=				"vqa"
				elif "nlvr" in checkpoint_path:
						__lowercase				:		Optional[Any]												=				{
						    "visual_embedding_dim": 10_24,
						    "num_labels": 2,
						}
						__lowercase				:		Tuple												=				"nlvr"

		__lowercase				:		Union[str, Any]												=				VisualBertConfig(**_lowerCamelCase				)

		# Load State Dict
		__lowercase				:		Any												=				load_state_dict(_lowerCamelCase				)

		__lowercase				:		Any												=				get_new_dict(_lowerCamelCase,  _lowerCamelCase				)

		if model_type == "pretraining":
				__lowercase				:		Optional[int]												=				VisualBertForPreTraining(_lowerCamelCase				)
		elif model_type == "vqa":
				__lowercase				:		List[Any]												=				VisualBertForQuestionAnswering(_lowerCamelCase				)
		elif model_type == "nlvr":
				__lowercase				:		str												=				VisualBertForVisualReasoning(_lowerCamelCase				)
		elif model_type == "multichoice":
				__lowercase				:		Tuple												=				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   :  Dict					=      parser.parse_args()
						convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	List[Any]						):
						__lowercase				:		Tuple												=				val
						__lowercase				:		Optional[Any]												=				None
						__lowercase				:		Tuple												=				None



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	List[str]						):
						if self.val:
								if val < self.val:
										if self.left is None:
												__lowercase				:		Tuple												=				Node(lowercase__						)
										else:
												self.left.insert(lowercase__						)
								elif val > self.val:
										if self.right is None:
												__lowercase				:		Any												=				Node(lowercase__						)
										else:
												self.right.insert(lowercase__						)
						else:
								__lowercase				:		Optional[Any]												=				val





def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Any:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


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


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