




"""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()
lowerCAmelCase							:    Optional[Any]  							=							logging.get_logger(__name__)

lowerCAmelCase							:    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"),
]

lowerCAmelCase							:    Union[str, Any]  							=							[
    "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__				(       snake_case__       )		->      Union[str, Any]:
			lowerCamelCase       =      torch.load(UpperCAmelCase__ ,				map_location="""cpu"""       )
			return sd



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



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[Any]:
			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       =      """pretraining"""
						if "vcr" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 5_12}
						elif "vqa_advanced" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 20_48}
						elif "vqa" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 20_48}
						elif "nlvr" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 10_24}
						else:
									raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'       )
			else:
						if "vcr" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 5_12}
									lowerCamelCase       =      """multichoice"""
						elif "vqa_advanced" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 20_48}
									lowerCamelCase       =      """vqa_advanced"""
						elif "vqa" in checkpoint_path:
									lowerCamelCase       =      {"""visual_embedding_dim""": 20_48, """num_labels""": 31_29}
									lowerCamelCase       =      """vqa"""
						elif "nlvr" in checkpoint_path:
									lowerCamelCase       =      {
									    """visual_embedding_dim""": 10_24,
									    """num_labels""": 2,
									}
									lowerCamelCase       =      """nlvr"""

			lowerCamelCase       =      VisualBertConfig(**UpperCAmelCase__       )

			# Load State Dict
			lowerCamelCase       =      load_state_dict(UpperCAmelCase__       )

			lowerCamelCase       =      get_new_dict(UpperCAmelCase__ ,				UpperCAmelCase__       )

			if model_type == "pretraining":
						lowerCamelCase       =      VisualBertForPreTraining(UpperCAmelCase__       )
			elif model_type == "vqa":
						lowerCamelCase       =      VisualBertForQuestionAnswering(UpperCAmelCase__       )
			elif model_type == "nlvr":
						lowerCamelCase       =      VisualBertForVisualReasoning(UpperCAmelCase__       )
			elif model_type == "multichoice":
						lowerCamelCase       =      VisualBertForMultipleChoice(UpperCAmelCase__       )

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


if __name__ == "__main__":
					lowerCAmelCase							:    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.""")
					lowerCAmelCase							:    str  							=							parser.parse_args()
					convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:    List[str]  							=							{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Tuple  							=							[
					    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AutoformerForPrediction""",
					    """AutoformerModel""",
					    """AutoformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_autoformer import (
					    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    AutoformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_autoformer import (
										    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AutoformerForPrediction,
										    AutoformerModel,
										    AutoformerPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:    str  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



