


"""simple docstring"""


A     :   List[Any]			    =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			set()
			# keep track of all the paths to be checked
			__lowerCAmelCase				    =			[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__lowerCAmelCase				    =			queue.pop(0      )
						# get the last node from the path
						__lowerCAmelCase				    =			path[-1]
						if node not in explored:
									__lowerCAmelCase				    =			graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowerCAmelCase				    =			list(_UpperCamelCase      )
												new_path.append(_UpperCamelCase      )
												queue.append(_UpperCamelCase      )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCamelCase      )

    # in case there's no path between the 2 nodes
			return []


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowerCAmelCase				    =			[start]
			__lowerCAmelCase				    =			set(_UpperCamelCase      )
			# Keep tab on distances from `start` node.
			__lowerCAmelCase				    =			{start: 0, target: -1}
			while queue:
						__lowerCAmelCase				    =			queue.pop(0      )
						if node == target:
									__lowerCAmelCase				    =			(
									    dist[node] if dist[target] == -1 else min(dist[target]    , dist[node]      )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCamelCase      )
												queue.append(_UpperCamelCase      )
												__lowerCAmelCase				    =			dist[node] + 1
			return dist[target]


if __name__ == "__main__":
	print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
	print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4








"""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)




