





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 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     [
    ("""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 				=     [
    """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		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				int			      =	torch.load(_lowerCAmelCase  ,map_location="""cpu"""     )
   return sd



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=rename_keys_prefix     ):
   '''simple docstring'''


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



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

   A_     :				List[Any]			      =	VisualBertConfig(**_lowerCAmelCase     )

   # Load State Dict
   A_     :				Optional[Any]			      =	load_state_dict(_lowerCAmelCase     )

   A_     :				str			      =	get_new_dict(_lowerCAmelCase  ,_lowerCAmelCase     )

   if model_type == "pretraining":
      A_     :				Union[str, Any]			      =	VisualBertForPreTraining(_lowerCAmelCase     )
   elif model_type == "vqa":
      A_     :				Any			      =	VisualBertForQuestionAnswering(_lowerCAmelCase     )
   elif model_type == "nlvr":
      A_     :				Any			      =	VisualBertForVisualReasoning(_lowerCAmelCase     )
   elif model_type == "multichoice":
      A_     :				Optional[Any]			      =	VisualBertForMultipleChoice(_lowerCAmelCase     )

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


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













from ....configuration_utils import PretrainedConfig
from ....utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

# TODO: upload to AWS
_lowerCAmelCase 				=     {
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''retribert'''
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=8      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=True      ,       a__=128      ,       a__=0      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       **a__       )

         A_     :				Union[str, Any]			      =	vocab_size
         A_     :				Optional[Any]			      =	hidden_size
         A_     :				Optional[Any]			      =	num_hidden_layers
         A_     :				List[str]			      =	num_attention_heads
         A_     :				Optional[int]			      =	hidden_act
         A_     :				Any			      =	intermediate_size
         A_     :				Tuple			      =	hidden_dropout_prob
         A_     :				Optional[Any]			      =	attention_probs_dropout_prob
         A_     :				Dict			      =	max_position_embeddings
         A_     :				Dict			      =	type_vocab_size
         A_     :				List[str]			      =	initializer_range
         A_     :				Optional[int]			      =	layer_norm_eps
         A_     :				Optional[Any]			      =	share_encoders
         A_     :				Dict			      =	projection_dim






