




"""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  UpperCAmelCase__	(snake_case__							: Any   ):
		"""simple docstring"""

		_snake_case							:		Tuple								=  torch.load(lowerCAmelCase__		,       map_location="""cpu"""   )
		return sd


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: int		,       snake_case__							: Tuple=rename_keys_prefix   ):
		"""simple docstring"""

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


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

		_snake_case							:		str								=  VisualBertConfig(**lowerCAmelCase__   )

		# Load State Dict
		_snake_case							:		Tuple								=  load_state_dict(lowerCAmelCase__   )

		_snake_case							:		Dict								=  get_new_dict(lowerCAmelCase__		,       lowerCAmelCase__   )

		if model_type == "pretraining":
				_snake_case							:		str								=  VisualBertForPreTraining(lowerCAmelCase__   )
		elif model_type == "vqa":
				_snake_case							:		Union[str, Any]								=  VisualBertForQuestionAnswering(lowerCAmelCase__   )
		elif model_type == "nlvr":
				_snake_case							:		str								=  VisualBertForVisualReasoning(lowerCAmelCase__   )
		elif model_type == "multichoice":
				_snake_case							:		Tuple								=  VisualBertForMultipleChoice(lowerCAmelCase__   )

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


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)








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


if __name__ == "__main__":
     print(solution())


