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()
__SCREAMING_SNAKE_CASE						:   List[Any]         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   int         =			[
    ('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'),
]

__SCREAMING_SNAKE_CASE						:   Tuple         =			[
    '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						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   torch.load(a_ ,      map_location="cpu"						)
						return sd
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase=rename_keys_prefix						)	->	List[str]:

						'''simple docstring'''



						_snake_case						:					List[Any]    =   OrderedDict()
						_snake_case						:					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
												_snake_case						:					Optional[Any]    =   key
												for name_pair in rename_keys_prefix:
																		_snake_case						:					str    =   new_key.replace(name_pair[0] ,      name_pair[1]						)
												_snake_case						:					int    =   d[key]
												if key == "bert.cls.predictions.decoder.weight":
																		# Old bert code didn't have `decoder.bias`, but was added separately
																		_snake_case						:					str    =   new_d["cls.predictions.bias"]
						return new_d
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[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:
												_snake_case						:					Tuple    =   "pretraining"
												if "vcr" in checkpoint_path:
																		_snake_case						:					Dict    =   {"visual_embedding_dim": 512}
												elif "vqa_advanced" in checkpoint_path:
																		_snake_case						:					Dict    =   {"visual_embedding_dim": 2_048}
												elif "vqa" in checkpoint_path:
																		_snake_case						:					str    =   {"visual_embedding_dim": 2_048}
												elif "nlvr" in checkpoint_path:
																		_snake_case						:					Tuple    =   {"visual_embedding_dim": 1_024}
												else:
																		raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""						)
						else:
												if "vcr" in checkpoint_path:
																		_snake_case						:					Dict    =   {"visual_embedding_dim": 512}
																		_snake_case						:					Tuple    =   "multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		_snake_case						:					int    =   {"visual_embedding_dim": 2_048}
																		_snake_case						:					Optional[int]    =   "vqa_advanced"
												elif "vqa" in checkpoint_path:
																		_snake_case						:					List[Any]    =   {"visual_embedding_dim": 2_048, "num_labels": 3_129}
																		_snake_case						:					str    =   "vqa"
												elif "nlvr" in checkpoint_path:
																		_snake_case						:					Any    =   {
																		    "visual_embedding_dim": 1_024,
																		    "num_labels": 2,
																		}
																		_snake_case						:					List[str]    =   "nlvr"

						_snake_case						:					Any    =   VisualBertConfig(**a_						)

						# Load State Dict
						_snake_case						:					Optional[int]    =   load_state_dict(a_						)

						_snake_case						:					Tuple    =   get_new_dict(a_ ,      a_						)

						if model_type == "pretraining":
												_snake_case						:					Tuple    =   VisualBertForPreTraining(a_						)
						elif model_type == "vqa":
												_snake_case						:					str    =   VisualBertForQuestionAnswering(a_						)
						elif model_type == "nlvr":
												_snake_case						:					int    =   VisualBertForVisualReasoning(a_						)
						elif model_type == "multichoice":
												_snake_case						:					Tuple    =   VisualBertForMultipleChoice(a_						)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[str]         =			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.')
				__SCREAMING_SNAKE_CASE						:   str         =			parser.parse_args()
				convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
def  snake_case						(__lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Tuple    =   ""
						for word_or_phrase in separated:
												if not isinstance(__lowercase ,      __lowercase						):
																		raise Exception("join() accepts only strings to be joined"						)
												joined += word_or_phrase + separator
						return joined.strip(__lowercase						)


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

				testmod()