




"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


a							      =     logging.get_logger(__name__)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Optional[int]       ,				*_UpperCAmelCase			:			int       ,				**_UpperCAmelCase			:			Optional[Any]						):
       warnings.warn(
           'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use FlavaImageProcessor instead.'       ,				_UpperCAmelCase       ,				)
       super().__init__(*_UpperCAmelCase       ,				**_UpperCAmelCase						)







"""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()
a							      =     logging.get_logger(__name__)

a							      =     [
    ('''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'''),
]

a							      =     [
    '''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 		_snake_case	(   _snake_case				:   Optional[Any]				)       ->   str:






      '''simple docstring'''


      _A     =  torch.load(_snake_case							,    map_location='cpu'				)
      return sd






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   str							,    _snake_case				:   Tuple=rename_keys_prefix				)       ->   List[str]:






      '''simple docstring'''


      _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 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   Dict				)       ->   Dict:






      '''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     =  'pretraining'
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "nlvr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 10_24}
            else:
                  raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''				)
      else:
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
                  _A     =  'multichoice'
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
                  _A     =  'vqa_advanced'
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
                  _A     =  'vqa'
            elif "nlvr" in checkpoint_path:
                  _A     =  {
                      'visual_embedding_dim': 10_24,
                      'num_labels': 2,
                  }
                  _A     =  'nlvr'

      _A     =  VisualBertConfig(**_snake_case				)

      # Load State Dict
      _A     =  load_state_dict(_snake_case				)

      _A     =  get_new_dict(_snake_case							,    _snake_case				)

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

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


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

