



from math import factorial



class 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          UpperCamelCase       :    Tuple		      =      real
          if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    int		      =      [1] * rank
          else:
             UpperCamelCase       :    List[Any]		      =      rank
       def __repr__(     self			)	->						Any:
          return (
              F"""{self.real}+"""
              F"""{"+".join(str(SCREAMING_SNAKE_CASE_			)+"E"+str(n+1			)for n,dual in enumerate(self.duals			)			)}"""
          )
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Optional[Any]		      =      self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1			)
          return Dual(self.real,     SCREAMING_SNAKE_CASE_			)
       def __add__(     self,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             return Dual(self.real + other,     self.duals			)
          UpperCamelCase       :    List[str]		      =      self.duals.copy()
          UpperCamelCase       :    Optional[int]		      =      other.duals.copy()
          if len(SCREAMING_SNAKE_CASE_			) > len(SCREAMING_SNAKE_CASE_			):
             o_dual.extend([1] * (len(SCREAMING_SNAKE_CASE_			) - len(SCREAMING_SNAKE_CASE_			))			)
          elif len(SCREAMING_SNAKE_CASE_			) < len(SCREAMING_SNAKE_CASE_			):
             s_dual.extend([1] * (len(SCREAMING_SNAKE_CASE_			) - len(SCREAMING_SNAKE_CASE_			))			)
          UpperCamelCase       :    Optional[Any]		      =      []
          for i in range(len(SCREAMING_SNAKE_CASE_			)			):
             new_duals.append(s_dual[i] + o_dual[i]			)
          return Dual(self.real + other.real,     SCREAMING_SNAKE_CASE_			)

       UpperCAmelCase__							:   List[Any]  	=    __add__
       def __sub__(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self + other * -1
       def __mul__(     self,     SCREAMING_SNAKE_CASE_			)	->						List[Any]:
          if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    List[str]		      =      []
             for i in self.duals:
                new_duals.append(i * other			)
             return Dual(self.real * other,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      [0] * (len(self.duals			) + len(other.duals			) + 1)
          for i, item in enumerate(self.duals			):
             for j, jtem in enumerate(other.duals			):
                new_duals[i + j + 1] += item * jtem
          for k in range(len(self.duals			)			):
             new_duals[k] += self.duals[k] * other.real
          for index in range(len(other.duals			)			):
             new_duals[index] += other.duals[index] * self.real
          return Dual(self.real * other.real,     SCREAMING_SNAKE_CASE_			)

       UpperCAmelCase__							:   int  	=    __mul__
       def __truediv__(     self,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Dict		      =      []
             for i in self.duals:
                new_duals.append(i / other			)
             return Dual(self.real / other,     SCREAMING_SNAKE_CASE_			)
          raise ValueError
       def __floordiv__(     self,     SCREAMING_SNAKE_CASE_			)	->						int:
          if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Any		      =      []
             for i in self.duals:
                new_duals.append(i // other			)
             return Dual(self.real // other,     SCREAMING_SNAKE_CASE_			)
          raise ValueError






       def __pow__(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          if n < 0 or isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             raise ValueError('power must be a positive integer'			)
          if n == 0:
             return 1
          if n == 1:
             return self
          UpperCamelCase       :    Union[str, Any]		      =      self
          for _ in range(n - 1			):
             x *= self
          return x






def 						UpperCamelCase      (  snake_case__			:       Optional[Any]			,	snake_case__			:       Optional[int]			,	snake_case__			:       Dict      )      ->	Optional[int]:
   if not callable(snake_case__      ):
      raise ValueError('differentiate() requires a function as input for func'      )
   if not isinstance(snake_case__			,	(float, int)      ):
      raise ValueError('differentiate() requires a float as input for position'      )
   if not isinstance(snake_case__			,	snake_case__      ):
      raise ValueError('differentiate() requires an int as input for order'      )
   UpperCamelCase       :    Optional[int]		      =      Dual(snake_case__			,	1      )
   UpperCamelCase       :    Dict		      =      func(snake_case__      )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(snake_case__      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    def 						UpperCamelCase      (  snake_case__			:       str      )      ->	Optional[Any]:
       return y**2 * y**4


    print(differentiate(f, 9, 2))





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      UpperCamelCase   (       __lowerCamelCase	:					int       ):
       snake_case			:  List[Any]     							= torch.load(__lowerCamelCase							,   map_location="cpu"       )
       return sd


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


@torch.no_grad()
def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					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:
              snake_case			:  Optional[Any]     							= "pretraining"
              if "vcr" in checkpoint_path:
                     snake_case			:  Optional[int]     							= {"visual_embedding_dim": 512}
              elif "vqa_advanced" in checkpoint_path:
                     snake_case			:  int     							= {"visual_embedding_dim": 2048}
              elif "vqa" in checkpoint_path:
                     snake_case			:  Dict     							= {"visual_embedding_dim": 2048}
              elif "nlvr" in checkpoint_path:
                     snake_case			:  Tuple     							= {"visual_embedding_dim": 1024}
              else:
                     raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""       )
       else:
              if "vcr" in checkpoint_path:
                     snake_case			:  Optional[Any]     							= {"visual_embedding_dim": 512}
                     snake_case			:  List[str]     							= "multichoice"
              elif "vqa_advanced" in checkpoint_path:
                     snake_case			:  Dict     							= {"visual_embedding_dim": 2048}
                     snake_case			:  Tuple     							= "vqa_advanced"
              elif "vqa" in checkpoint_path:
                     snake_case			:  Optional[Any]     							= {"visual_embedding_dim": 2048, "num_labels": 3129}
                     snake_case			:  Tuple     							= "vqa"
              elif "nlvr" in checkpoint_path:
                     snake_case			:  str     							= {
                         "visual_embedding_dim": 1024,
                         "num_labels": 2,
                     }
                     snake_case			:  str     							= "nlvr"

       snake_case			:  int     							= VisualBertConfig(**__lowerCamelCase       )

       # Load State Dict
       snake_case			:  Any     							= load_state_dict(__lowerCamelCase       )

       snake_case			:  List[Any]     							= get_new_dict(__lowerCamelCase							,   __lowerCamelCase       )

       if model_type == "pretraining":
              snake_case			:  str     							= VisualBertForPreTraining(__lowerCamelCase       )
       elif model_type == "vqa":
              snake_case			:  Any     							= VisualBertForQuestionAnswering(__lowerCamelCase       )
       elif model_type == "nlvr":
              snake_case			:  Optional[Any]     							= VisualBertForVisualReasoning(__lowerCamelCase       )
       elif model_type == "multichoice":
              snake_case			:  Optional[int]     							= 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)





