






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()
UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						[
    ("""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"""),
]

UpperCAmelCase__							  =						[
    """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       ( a					:Union[str, Any]					)				->     Optional[Any]:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 return sd
def 		_a       ( a					:Optional[int]      ,				a					:str      ,				a					:Optional[int]=rename_keys_prefix					)				->     Union[str, Any]:
 a						    = OrderedDict()
 a						    = 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						    = key
  for name_pair in rename_keys_prefix:
   a						    = new_key.replace(name_pair[0]      ,				name_pair[1]					)
  a						    = d[key]
  if key == "bert.cls.predictions.decoder.weight":
   # Old bert code didn't have `decoder.bias`, but was added separately
   a						    = new_d["cls.predictions.bias"]
 return new_d
@torch.no_grad()
def 		_a       ( a					:List[Any]      ,				a					:Optional[Any]					)				->     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:
  a						    = "pretraining"
  if "vcr" in checkpoint_path:
   a						    = {"visual_embedding_dim": 512}
  elif "vqa_advanced" in checkpoint_path:
   a						    = {"visual_embedding_dim": 2_048}
  elif "vqa" in checkpoint_path:
   a						    = {"visual_embedding_dim": 2_048}
  elif "nlvr" in checkpoint_path:
   a						    = {"visual_embedding_dim": 1_024}
  else:
   raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""					)
 else:
  if "vcr" in checkpoint_path:
   a						    = {"visual_embedding_dim": 512}
   a						    = "multichoice"
  elif "vqa_advanced" in checkpoint_path:
   a						    = {"visual_embedding_dim": 2_048}
   a						    = "vqa_advanced"
  elif "vqa" in checkpoint_path:
   a						    = {"visual_embedding_dim": 2_048, "num_labels": 3_129}
   a						    = "vqa"
  elif "nlvr" in checkpoint_path:
   a						    = {
       "visual_embedding_dim": 1_024,
       "num_labels": 2,
   }
   a						    = "nlvr"

 a						    = VisualBertConfig(**a					)

 # Load State Dict
 a						    = load_state_dict(a					)

 a						    = get_new_dict(a      ,				a					)

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

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


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










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


