






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        __lowerCamelCase			(		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  torch.load(snake_case__       ,map_location="""cpu"""		)
						return sd

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__=rename_keys_prefix		)							->  Tuple:






						"""simple docstring"""





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

@torch.no_grad()
def        __lowerCamelCase			(		snake_case__       ,snake_case__		)							->  Optional[Any]:






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

						_SCREAMING_SNAKE_CASE	      =  VisualBertConfig(**snake_case__		)

						# Load State Dict
						_SCREAMING_SNAKE_CASE	      =  load_state_dict(snake_case__		)

						_SCREAMING_SNAKE_CASE	      =  get_new_dict(snake_case__       ,snake_case__		)

						if model_type == "pretraining":
												_SCREAMING_SNAKE_CASE	      =  VisualBertForPreTraining(snake_case__		)
						elif model_type == "vqa":
												_SCREAMING_SNAKE_CASE	      =  VisualBertForQuestionAnswering(snake_case__		)
						elif model_type == "nlvr":
												_SCREAMING_SNAKE_CASE	      =  VisualBertForVisualReasoning(snake_case__		)
						elif model_type == "multichoice":
												_SCREAMING_SNAKE_CASE	      =  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__":
		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)


def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):

 def get_matched_characters(lowerCamelCase					:     str  ,  lowerCamelCase					:     str	) -> str:
  UpperCamelCase_					:						Tuple =					[]
  UpperCamelCase_					:						List[Any] =					min(len(_stra	)  ,  len(_stra	)	) // 2
  for i, l in enumerate(_stra	):
   UpperCamelCase_					:						int =					int(max(0  ,  i - limit	)	)
   UpperCamelCase_					:						Dict =					int(min(i + limit + 1  ,  len(_stra	)	)	)
   if l in _stra[left:right]:
    matched.append(lowerCamelCase	)
    UpperCamelCase_					:						Dict =					F"{_stra[0:_stra.index(lowerCamelCase	)]} {_stra[_stra.index(lowerCamelCase	) + 1:]}"

  return "".join(lowerCamelCase	)

 # matching characters
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					len(lowerCamelCase	)

 # transposition
 UpperCamelCase_					:						int =					(
     len([(ca, ca) for ca, ca in zip(lowerCamelCase  ,  lowerCamelCase	) if ca != ca]	) // 2
 )

 if not match_count:
  UpperCamelCase_					:						Union[str, Any] =					0.0
 else:
  UpperCamelCase_					:						str =					(
      1
      / 3
      * (
          match_count / len(lowerCamelCase	)
          + match_count / len(lowerCamelCase	)
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 UpperCamelCase_					:						Dict =					0
 for ca, ca in zip(stra[:4]  ,  stra[:4]	):
  if ca == ca:
   prefix_len += 1
  else:
   break

 return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(jaro_winkler('hello', 'world'))



