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

UpperCAmelCase_							: Dict =							[
    ('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_							: 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 		A_							(						_lowerCAmelCase :     Optional[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = torch.load(_lowerCAmelCase					,						map_location="cpu"				)
      return sd







def 		A_							(						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Tuple=rename_keys_prefix				):



      """simple docstring"""




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







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Dict				):



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

      _lowerCamelCase					: str		    = VisualBertConfig(**_lowerCAmelCase				)

      # Load State Dict
      _lowerCamelCase					: str		    = load_state_dict(_lowerCAmelCase				)

      _lowerCamelCase					: List[str]		    = get_new_dict(_lowerCAmelCase					,						_lowerCAmelCase				)

      if model_type == "pretraining":
            _lowerCamelCase					: List[Any]		    = VisualBertForPreTraining(_lowerCAmelCase				)
      elif model_type == "vqa":
            _lowerCamelCase					: Dict		    = VisualBertForQuestionAnswering(_lowerCAmelCase				)
      elif model_type == "nlvr":
            _lowerCamelCase					: Tuple		    = VisualBertForVisualReasoning(_lowerCAmelCase				)
      elif model_type == "multichoice":
            _lowerCamelCase					: str		    = VisualBertForMultipleChoice(_lowerCAmelCase				)

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


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




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a							   =				{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				['OwlViTFeatureExtractor']
	a							   =				['OwlViTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				[
	    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OwlViTModel',
	    'OwlViTPreTrainedModel',
	    'OwlViTTextModel',
	    'OwlViTVisionModel',
	    'OwlViTForObjectDetection',
	]

if TYPE_CHECKING:
	from .configuration_owlvit import (
	    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    OwlViTConfig,
	    OwlViTOnnxConfig,
	    OwlViTTextConfig,
	    OwlViTVisionConfig,
	)
	from .processing_owlvit import OwlViTProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_owlvit import OwlViTFeatureExtractor
		from .image_processing_owlvit import OwlViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_owlvit import (
		    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OwlViTForObjectDetection,
		    OwlViTModel,
		    OwlViTPreTrainedModel,
		    OwlViTTextModel,
		    OwlViTVisionModel,
		)

else:
	import sys

	a							   =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




