


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_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Optional[int]  					=       [
    ('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_      : Optional[int]  					=       [
    '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        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]				)	->		Tuple:

       """simple docstring"""

       a_					:		str							=  torch.load(__A  ,    map_location='cpu'				)
       return sd







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Union[str, Any]  ,    __A :			Dict=rename_keys_prefix				)	->		List[Any]:

       """simple docstring"""

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







@torch.no_grad()
def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			List[str]				)	->		int:

       """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_					:		Dict							=  'pretraining'
              if "vcr" in checkpoint_path:
                     a_					:		Any							=  {'visual_embedding_dim': 5_12}
              elif "vqa_advanced" in checkpoint_path:
                     a_					:		Optional[int]							=  {'visual_embedding_dim': 20_48}
              elif "vqa" in checkpoint_path:
                     a_					:		str							=  {'visual_embedding_dim': 20_48}
              elif "nlvr" in checkpoint_path:
                     a_					:		List[Any]							=  {'visual_embedding_dim': 10_24}
              else:
                     raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""				)
       else:
              if "vcr" in checkpoint_path:
                     a_					:		Dict							=  {'visual_embedding_dim': 5_12}
                     a_					:		Union[str, Any]							=  'multichoice'
              elif "vqa_advanced" in checkpoint_path:
                     a_					:		Union[str, Any]							=  {'visual_embedding_dim': 20_48}
                     a_					:		int							=  'vqa_advanced'
              elif "vqa" in checkpoint_path:
                     a_					:		int							=  {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
                     a_					:		Tuple							=  'vqa'
              elif "nlvr" in checkpoint_path:
                     a_					:		Dict							=  {
                         'visual_embedding_dim': 10_24,
                         'num_labels': 2,
                     }
                     a_					:		int							=  'nlvr'

       a_					:		List[str]							=  VisualBertConfig(**__A				)

       # Load State Dict
       a_					:		List[Any]							=  load_state_dict(__A				)

       a_					:		str							=  get_new_dict(__A  ,    __A				)

       if model_type == "pretraining":
              a_					:		int							=  VisualBertForPreTraining(__A				)
       elif model_type == "vqa":
              a_					:		str							=  VisualBertForQuestionAnswering(__A				)
       elif model_type == "nlvr":
              a_					:		Optional[Any]							=  VisualBertForVisualReasoning(__A				)
       elif model_type == "multichoice":
              a_					:		Tuple							=  VisualBertForMultipleChoice(__A				)

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


if __name__ == "__main__":
   UpperCAmelCase_      : List[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.')
   UpperCAmelCase_      : List[Any]  					=       parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)






from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


