






"""simple docstring"""







from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt





def 						lowercase__(A ,		A ,		A						)    ->tuple[int | None, int | None, float]:




						"""simple docstring"""



						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						lowercase__    :				int=	(low + high) // 2
						lowercase__,     lowercase__,     lowercase__    :				Dict=	max_subarray(__snake_case ,		__snake_case ,		__snake_case						)
						lowercase__,     lowercase__,     lowercase__    :				Union[str, Any]=	max_subarray(__snake_case ,		mid + 1 ,		__snake_case						)
						lowercase__,     lowercase__,     lowercase__    :				List[str]=	max_cross_sum(__snake_case ,		__snake_case ,		__snake_case ,		__snake_case						)
						if left_sum >= right_sum and left_sum >= cross_sum:
												return left_low, left_high, left_sum
						elif right_sum >= left_sum and right_sum >= cross_sum:
												return right_low, right_high, right_sum
						return cross_left, cross_right, cross_sum





def 						lowercase__(A ,		A ,		A ,		A						)    ->tuple[int, int, float]:




						"""simple docstring"""



						lowercase__,     lowercase__    :				str=	float("-inf"						), -1
						lowercase__,     lowercase__    :				Optional[Any]=	float("-inf"						), -1

						lowercase__    :				Any=	0
						for i in range(__snake_case ,		low - 1 ,		-1						):
												summ += arr[i]
												if summ > left_sum:
																		lowercase__    :				List[Any]=	summ
																		lowercase__    :				Any=	i

						lowercase__    :				List[str]=	0
						for i in range(mid + 1 ,		high + 1						):
												summ += arr[i]
												if summ > right_sum:
																		lowercase__    :				Any=	summ
																		lowercase__    :				Optional[int]=	i

						return max_left, max_right, (left_sum + right_sum)





def 						lowercase__(A						)    ->float:




						"""simple docstring"""



						lowercase__    :				str=	[randint(1 ,		__snake_case						) for _ in range(__snake_case						)]
						lowercase__    :				List[str]=	time.time()
						max_subarray(__snake_case ,		0 ,		input_size - 1						)
						lowercase__    :				Tuple=	time.time()
						return end - start





def 						lowercase__()    ->None:




						"""simple docstring"""



						lowercase__    :				Optional[Any]=	[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
						lowercase__    :				int=	[time_max_subarray(__snake_case						) for input_size in input_sizes]
						print("No of Inputs\t\tTime Taken"						)
						for input_size, runtime in zip(__snake_case ,		__snake_case						):
												print(__snake_case ,		"\t\t" ,		__snake_case						)
						plt.plot(__snake_case ,		__snake_case						)
						plt.xlabel("Number of Inputs"						)
						plt.ylabel("Time taken in seconds"						)
						plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()














"""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			:    List[str]				 =		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			:    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 						lowercase__(A						)    ->Optional[int]:




						"""simple docstring"""



						lowercase__    :				Any=	torch.load(A ,		map_location="cpu"						)
						return sd





def 						lowercase__(A ,		A ,		A=rename_keys_prefix						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				int=	OrderedDict()
						lowercase__    :				Optional[Any]=	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__    :				Union[str, Any]=	key
												for name_pair in rename_keys_prefix:
																		lowercase__    :				str=	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__    :				Optional[int]=	new_d["cls.predictions.bias"]
						return new_d





@torch.no_grad()
def 						lowercase__(A ,		A						)    ->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__    :				Union[str, Any]=	"pretraining"
												if "vcr" in checkpoint_path:
																		lowercase__    :				str=	{"visual_embedding_dim": 512}
												elif "vqa_advanced" in checkpoint_path:
																		lowercase__    :				Optional[Any]=	{"visual_embedding_dim": 2_048}
												elif "vqa" in checkpoint_path:
																		lowercase__    :				int=	{"visual_embedding_dim": 2_048}
												elif "nlvr" in checkpoint_path:
																		lowercase__    :				Tuple=	{"visual_embedding_dim": 1_024}
												else:
																		raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''						)
						else:
												if "vcr" in checkpoint_path:
																		lowercase__    :				int=	{"visual_embedding_dim": 512}
																		lowercase__    :				int=	"multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		lowercase__    :				Dict=	{"visual_embedding_dim": 2_048}
																		lowercase__    :				Optional[Any]=	"vqa_advanced"
												elif "vqa" in checkpoint_path:
																		lowercase__    :				Optional[int]=	{"visual_embedding_dim": 2_048, "num_labels": 3_129}
																		lowercase__    :				List[str]=	"vqa"
												elif "nlvr" in checkpoint_path:
																		lowercase__    :				Dict=	{
																		    "visual_embedding_dim": 1_024,
																		    "num_labels": 2,
																		}
																		lowercase__    :				Any=	"nlvr"

						lowercase__    :				List[Any]=	VisualBertConfig(**A						)

						# Load State Dict
						lowercase__    :				Union[str, Any]=	load_state_dict(A						)

						lowercase__    :				List[str]=	get_new_dict(A ,		A						)

						if model_type == "pretraining":
												lowercase__    :				Optional[Any]=	VisualBertForPreTraining(A						)
						elif model_type == "vqa":
												lowercase__    :				Any=	VisualBertForQuestionAnswering(A						)
						elif model_type == "nlvr":
												lowercase__    :				Union[str, Any]=	VisualBertForVisualReasoning(A						)
						elif model_type == "multichoice":
												lowercase__    :				str=	VisualBertForMultipleChoice(A						)

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


if __name__ == "__main__":
			a			:    int				 =		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)






