

'''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			:				Dict 			=					logging.get_logger(__name__)

lowerCamelCase			:				Union[str, 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			:				List[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 	_lowerCAmelCase (						_UpperCamelCase						:					Any    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =torch.load(_UpperCamelCase							,			map_location='cpu'    )
     return sd


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Any							,			_UpperCamelCase						:					List[str]=rename_keys_prefix    )				->      Optional[int]:
     """simple docstring"""




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


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

     _SCREAMING_SNAKE_CASE							   =VisualBertConfig(**_UpperCamelCase    )

     # Load State Dict
     _SCREAMING_SNAKE_CASE							   =load_state_dict(_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =get_new_dict(_UpperCamelCase							,			_UpperCamelCase    )

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

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


if __name__ == "__main__":
     lowerCamelCase			:				str 			=					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			:				int 			=					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,
)


lowerCamelCase			:				Tuple 			=					{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
