






"""simple docstring"""







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



						return {key.lstrip("-"						): value for key, value in zip(unknown_args[::2] ,		unknown_args[1::2]						)}





def 						lowercase__()    ->List[str]:




						"""simple docstring"""



						lowercase__    :				Any=	ArgumentParser(
						    "HuggingFace Datasets CLI tool" ,		usage="datasets-cli <command> [<args>]" ,		allow_abbrev=A						)
						lowercase__    :				Tuple=	parser.add_subparsers(help="datasets-cli command helpers"						)
						set_verbosity_info()

						# Register commands
						ConvertCommand.register_subcommand(A						)
						EnvironmentCommand.register_subcommand(A						)
						TestCommand.register_subcommand(A						)
						RunBeamCommand.register_subcommand(A						)
						DummyDataCommand.register_subcommand(A						)

						# Parse args
						lowercase__,     lowercase__    :				int=	parser.parse_known_args()
						if not hasattr(A ,		"func"						):
												parser.print_help()
												exit(1						)
						lowercase__    :				int=	parse_unknown_args(A						)

						# Run
						lowercase__    :				List[Any]=	args.func(A ,		**A						)
						service.run()


if __name__ == "__main__":
			main()














"""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)






