





"""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()
__UpperCamelCase     :						Any						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[Any]						       =  [
    ('''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     :						List[str]						       =  [
    '''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			(_UpperCAmelCase					:    Union[str, Any]			):
 lowerCAmelCase    				= torch.load(_UpperCAmelCase  ,     map_location='cpu'			)
 return sd

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

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    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:
  lowerCAmelCase    				= 'pretraining'
  if "vcr" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 512}
  elif "vqa_advanced" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 2048}
  elif "vqa" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 2048}
  elif "nlvr" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 1024}
  else:
   raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'			)
 else:
  if "vcr" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 512}
   lowerCAmelCase    				= 'multichoice'
  elif "vqa_advanced" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 2048}
   lowerCAmelCase    				= 'vqa_advanced'
  elif "vqa" in checkpoint_path:
   lowerCAmelCase    				= {'visual_embedding_dim': 2048, 'num_labels': 3129}
   lowerCAmelCase    				= 'vqa'
  elif "nlvr" in checkpoint_path:
   lowerCAmelCase    				= {
       'visual_embedding_dim': 1024,
       'num_labels': 2,
   }
   lowerCAmelCase    				= 'nlvr'

 lowerCAmelCase    				= VisualBertConfig(**_UpperCAmelCase			)

 # Load State Dict
 lowerCAmelCase    				= load_state_dict(_UpperCAmelCase			)

 lowerCAmelCase    				= get_new_dict(_UpperCAmelCase  ,     _UpperCAmelCase			)

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

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


if __name__ == "__main__":
       __UpperCamelCase     :						Optional[Any]						       =  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     :						Optional[Any]						       =  parser.parse_args()
       convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)












__UpperCamelCase:	str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__UpperCamelCase:	Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

		for neighbour in graph[vert]:
				if not visited[neighbour]:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

		for neighbour in reversed_graph[vert]:
				if not visited[neighbour]:
						component += find_components(_lowercase   ,    _lowercase   ,    _lowercase			)

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

		for vert, neighbours in graph.items():
				for neighbour in neighbours:
						reversed_graph[neighbour].append(_lowercase			)

		lowercase__					:      Dict      =		[]
		for i, was_visited in enumerate(_lowercase			):
				if not was_visited:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

		for i in range(len(_lowercase			)			):
				lowercase__					:      List[Any]      =		order[len(_lowercase			) - i - 1]
				if not visited[vert]:
						lowercase__					:      Tuple      =		find_components(_lowercase   ,    _lowercase   ,    _lowercase			)
						components_list.append(_lowercase			)

		return components_list


