




'''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()
SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	[
    ("""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"""),
]

SCREAMING_SNAKE_CASE						=	[
    """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 :       str			)       ->      str:



 """simple docstring"""




 lowercase					:   List[Any]				  =torch.load(UpperCamelCase__					,      map_location='''cpu'''			)
 return sd


def 							lowercase_				(    __A :       Union[str, Any]					,      __A :       str					,      __A :       Tuple=rename_keys_prefix			)       ->      Tuple:



 """simple docstring"""




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


@torch.no_grad()
def 							lowercase_				(    __A :       str					,      __A :       str			)       ->      List[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					:   Optional[int]				  ='''pretraining'''
  if "vcr" in checkpoint_path:
   lowercase					:   Union[str, Any]				  ={'''visual_embedding_dim''': 5_1_2}
  elif "vqa_advanced" in checkpoint_path:
   lowercase					:   Tuple				  ={'''visual_embedding_dim''': 2_0_4_8}
  elif "vqa" in checkpoint_path:
   lowercase					:   Tuple				  ={'''visual_embedding_dim''': 2_0_4_8}
  elif "nlvr" in checkpoint_path:
   lowercase					:   str				  ={'''visual_embedding_dim''': 1_0_2_4}
  else:
   raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'			)
 else:
  if "vcr" in checkpoint_path:
   lowercase					:   Any				  ={'''visual_embedding_dim''': 5_1_2}
   lowercase					:   List[str]				  ='''multichoice'''
  elif "vqa_advanced" in checkpoint_path:
   lowercase					:   Optional[int]				  ={'''visual_embedding_dim''': 2_0_4_8}
   lowercase					:   List[str]				  ='''vqa_advanced'''
  elif "vqa" in checkpoint_path:
   lowercase					:   List[Any]				  ={'''visual_embedding_dim''': 2_0_4_8, '''num_labels''': 3_1_2_9}
   lowercase					:   Any				  ='''vqa'''
  elif "nlvr" in checkpoint_path:
   lowercase					:   Optional[int]				  ={
       '''visual_embedding_dim''': 1_0_2_4,
       '''num_labels''': 2,
   }
   lowercase					:   List[Any]				  ='''nlvr'''

 lowercase					:   List[str]				  =VisualBertConfig(**UpperCamelCase__			)

 # Load State Dict
 lowercase					:   Any				  =load_state_dict(UpperCamelCase__			)

 lowercase					:   Union[str, Any]				  =get_new_dict(UpperCamelCase__					,      UpperCamelCase__			)

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	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.')
      SCREAMING_SNAKE_CASE						=	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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RemBertForCausalLM',
          'RemBertForMaskedLM',
          'RemBertForMultipleChoice',
          'RemBertForQuestionAnswering',
          'RemBertForSequenceClassification',
          'RemBertForTokenClassification',
          'RemBertLayer',
          'RemBertModel',
          'RemBertPreTrainedModel',
          'load_tf_weights_in_rembert',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRemBertForCausalLM',
          'TFRemBertForMaskedLM',
          'TFRemBertForMultipleChoice',
          'TFRemBertForQuestionAnswering',
          'TFRemBertForSequenceClassification',
          'TFRemBertForTokenClassification',
          'TFRemBertLayer',
          'TFRemBertModel',
          'TFRemBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

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



