


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

A     :   Tuple			    =			[
    ("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     :   Optional[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 _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )
			return sd


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=rename_keys_prefix      ):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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:
						__lowerCAmelCase				    =			"pretraining"
						if "vcr" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 512}
						elif "vqa_advanced" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048}
						elif "vqa" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048}
						elif "nlvr" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 1024}
						else:
									raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."      )
			else:
						if "vcr" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 512}
									__lowerCAmelCase				    =			"multichoice"
						elif "vqa_advanced" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048}
									__lowerCAmelCase				    =			"vqa_advanced"
						elif "vqa" in checkpoint_path:
									__lowerCAmelCase				    =			{"visual_embedding_dim": 2048, "num_labels": 3129}
									__lowerCAmelCase				    =			"vqa"
						elif "nlvr" in checkpoint_path:
									__lowerCAmelCase				    =			{
									    "visual_embedding_dim": 1024,
									    "num_labels": 2,
									}
									__lowerCAmelCase				    =			"nlvr"

			__lowerCAmelCase				    =			VisualBertConfig(**_UpperCamelCase      )

			# Load State Dict
			__lowerCAmelCase				    =			load_state_dict(_UpperCamelCase      )

			__lowerCAmelCase				    =			get_new_dict(_UpperCamelCase    , _UpperCamelCase      )

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

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


if __name__ == "__main__":
	A     :   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.")
	A     :   Optional[int]			    =			parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)










from queue import PriorityQueue
from typing import Any

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				PriorityQueue      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				float | int      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       np.inf

					queue_forward.put((0, source)     )
					queue_backward.put((0, destination)     )

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




