




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()
_UpperCAmelCase	:						Union[str, Any]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						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"),
]

_UpperCAmelCase	:						Optional[int]			=     [
    "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__  (     lowerCamelCase   ):
		lowercase    :Dict      	=						torch.load(snake_case__,			map_location="cpu"   )
		return sd



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



@torch.no_grad()
def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
		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    :int      	=						"pretraining"
				if "vcr" in checkpoint_path:
						lowercase    :int      	=						{"visual_embedding_dim": 512}
				elif "vqa_advanced" in checkpoint_path:
						lowercase    :Any      	=						{"visual_embedding_dim": 2048}
				elif "vqa" in checkpoint_path:
						lowercase    :str      	=						{"visual_embedding_dim": 2048}
				elif "nlvr" in checkpoint_path:
						lowercase    :List[Any]      	=						{"visual_embedding_dim": 1024}
				else:
						raise NotImplementedError(F"No implementation found for `{checkpoint_path}`."   )
		else:
				if "vcr" in checkpoint_path:
						lowercase    :str      	=						{"visual_embedding_dim": 512}
						lowercase    :Tuple      	=						"multichoice"
				elif "vqa_advanced" in checkpoint_path:
						lowercase    :Dict      	=						{"visual_embedding_dim": 2048}
						lowercase    :Optional[Any]      	=						"vqa_advanced"
				elif "vqa" in checkpoint_path:
						lowercase    :Tuple      	=						{"visual_embedding_dim": 2048, "num_labels": 3129}
						lowercase    :Union[str, Any]      	=						"vqa"
				elif "nlvr" in checkpoint_path:
						lowercase    :Tuple      	=						{
						    "visual_embedding_dim": 1024,
						    "num_labels": 2,
						}
						lowercase    :Optional[Any]      	=						"nlvr"

		lowercase    :Dict      	=						VisualBertConfig(**snake_case__   )

		# Load State Dict
		lowercase    :Tuple      	=						load_state_dict(snake_case__   )

		lowercase    :int      	=						get_new_dict(snake_case__,			snake_case__   )

		if model_type == "pretraining":
				lowercase    :Optional[int]      	=						VisualBertForPreTraining(snake_case__   )
		elif model_type == "vqa":
				lowercase    :Tuple      	=						VisualBertForQuestionAnswering(snake_case__   )
		elif model_type == "nlvr":
				lowercase    :Optional[int]      	=						VisualBertForVisualReasoning(snake_case__   )
		elif model_type == "multichoice":
				lowercase    :int      	=						VisualBertForMultipleChoice(snake_case__   )

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


if __name__ == "__main__":
					_UpperCAmelCase	:						Tuple			=     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.")
					_UpperCAmelCase	:						int			=     parser.parse_args()
					convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



