






'''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:      Optional[Any]  				=				logging.get_logger(__name__)

A:      List[str]  				=				[
    ("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 	_UpperCAmelCase    (  a		:	Dict							)			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   int        =			torch.load(a						,			map_location='cpu'							)
						return sd






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Tuple						,			a		:	Tuple=rename_keys_prefix							)			->			List[str]:





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






@torch.no_grad()
def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	Any							)			->			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_      :   Any        =			'pretraining'
												if "vcr" in checkpoint_path:
																		lowercase_      :   Dict        =			{'visual_embedding_dim': 5_1_2}
												elif "vqa_advanced" in checkpoint_path:
																		lowercase_      :   Tuple        =			{'visual_embedding_dim': 2_0_4_8}
												elif "vqa" in checkpoint_path:
																		lowercase_      :   List[Any]        =			{'visual_embedding_dim': 2_0_4_8}
												elif "nlvr" in checkpoint_path:
																		lowercase_      :   Optional[int]        =			{'visual_embedding_dim': 1_0_2_4}
												else:
																		raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."							)
						else:
												if "vcr" in checkpoint_path:
																		lowercase_      :   Tuple        =			{'visual_embedding_dim': 5_1_2}
																		lowercase_      :   List[Any]        =			'multichoice'
												elif "vqa_advanced" in checkpoint_path:
																		lowercase_      :   Tuple        =			{'visual_embedding_dim': 2_0_4_8}
																		lowercase_      :   str        =			'vqa_advanced'
												elif "vqa" in checkpoint_path:
																		lowercase_      :   List[str]        =			{'visual_embedding_dim': 2_0_4_8, 'num_labels': 3_1_2_9}
																		lowercase_      :   Optional[Any]        =			'vqa'
												elif "nlvr" in checkpoint_path:
																		lowercase_      :   int        =			{
																		    'visual_embedding_dim': 1_0_2_4,
																		    'num_labels': 2,
																		}
																		lowercase_      :   List[Any]        =			'nlvr'

						lowercase_      :   Any        =			VisualBertConfig(**a							)

						# Load State Dict
						lowercase_      :   Any        =			load_state_dict(a							)

						lowercase_      :   Optional[Any]        =			get_new_dict(a						,			a							)

						if model_type == "pretraining":
												lowercase_      :   Union[str, Any]        =			VisualBertForPreTraining(a							)
						elif model_type == "vqa":
												lowercase_      :   int        =			VisualBertForQuestionAnswering(a							)
						elif model_type == "nlvr":
												lowercase_      :   Union[str, Any]        =			VisualBertForVisualReasoning(a							)
						elif model_type == "multichoice":
												lowercase_      :   List[Any]        =			VisualBertForMultipleChoice(a							)

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


if __name__ == "__main__":
						A:      List[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:      Union[str, Any]  				=				parser.parse_args()
						convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						lowercase_      :   Dict        =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						lowercase_      :   Dict        =			''
						lowercase_      :   Any        =			''

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(a							) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						lowercase_  ,      lowercase_      :   Dict        =			0, 0

						# length[i] shows the length of palindromic substring with center i
						lowercase_      :   List[Any]        =			[1 for i in range(len(a							)							)]

						# for each character in new_string find corresponding palindromic string
						lowercase_      :   Dict        =			0
						for j in range(len(a							)							):
												lowercase_      :   Tuple        =			1 if j > r else min(length[l + r - j] // 2						,			r - j + 1							)
												while (
												    j - k >= 0
												    and j + k < len(a							)
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												lowercase_      :   int        =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		lowercase_      :   Tuple        =			j - k + 1  # noqa: E741
																		lowercase_      :   Tuple        =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		lowercase_      :   Tuple        =			length[j]
																		lowercase_      :   List[Any]        =			j

    # create that string
						lowercase_      :   str        =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
						import doctest

						doctest.testmod()
