






"""simple docstring"""







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a			:    Optional[Any]				 =		logging.getLogger(__name__)

if __name__ == "__main__":
			a			:    int				 =		argparse.ArgumentParser(
			    description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
			)
			parser.add_argument(
			    """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
			)
			parser.add_argument(
			    """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
			)
			parser.add_argument("""--vocab_size""", default=30522, type=int)
			a			:    Any				 =		parser.parse_args()

			logger.info(F"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						a			:    Tuple				 =		pickle.load(fp)

			logger.info("""Counting occurrences for MLM.""")
			a			:    int				 =		Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			a			:    Union[str, Any]				 =		[0] * args.vocab_size
			for k, v in counter.items():
						a			:    Tuple				 =		v

			logger.info(F"""Dump to {args.token_counts_dump}""")
			with open(args.token_counts_dump, """wb""") as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)














"""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)






