

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_(_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=rename_keys_prefix					)							->     List[Any]:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     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:
    _snake_case						=							'''pretraining'''
    if "vcr" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 512}
    elif "vqa_advanced" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048}
    elif "vqa" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048}
    elif "nlvr" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 1_024}
    else:
      raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""					)
  else:
    if "vcr" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 512}
      _snake_case						=							'''multichoice'''
    elif "vqa_advanced" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048}
      _snake_case						=							'''vqa_advanced'''
    elif "vqa" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048, '''num_labels''': 3_129}
      _snake_case						=							'''vqa'''
    elif "nlvr" in checkpoint_path:
      _snake_case						=							{
          '''visual_embedding_dim''': 1_024,
          '''num_labels''': 2,
      }
      _snake_case						=							'''nlvr'''

  _snake_case						=							VisualBertConfig(**_UpperCamelCase					)

  # Load State Dict
  _snake_case						=							load_state_dict(_UpperCamelCase					)

  _snake_case						=							get_new_dict(_UpperCamelCase			,	_UpperCamelCase					)

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

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


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)




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    int		)   ->					None:
        warnings.warn(
            '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DeformableDetrImageProcessor instead.'''      , A__      , )
        super().__init__(*A__      , **A__		)

