


"""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()
snake_case__		:     Union[str, Any]    				=				logging.get_logger(__name__)

snake_case__		:     Tuple    				=				[
    ('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'),
]

snake_case__		:     Any    				=				[
    '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				:			Union[str, Any]			)     ->   Optional[Any]:
      lowerCAmelCase					:	int       =   torch.load(__UpperCamelCase ,   map_location='''cpu'''			)
      return sd


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


@torch.no_grad()
def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			List[str]			)     ->   str:
      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					:	str       =   '''pretraining'''
            if "vcr" in checkpoint_path:
                  lowerCAmelCase					:	int       =   {'''visual_embedding_dim''': 512}
            elif "vqa_advanced" in checkpoint_path:
                  lowerCAmelCase					:	List[str]       =   {'''visual_embedding_dim''': 2048}
            elif "vqa" in checkpoint_path:
                  lowerCAmelCase					:	Optional[Any]       =   {'''visual_embedding_dim''': 2048}
            elif "nlvr" in checkpoint_path:
                  lowerCAmelCase					:	Any       =   {'''visual_embedding_dim''': 1024}
            else:
                  raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''			)
      else:
            if "vcr" in checkpoint_path:
                  lowerCAmelCase					:	int       =   {'''visual_embedding_dim''': 512}
                  lowerCAmelCase					:	str       =   '''multichoice'''
            elif "vqa_advanced" in checkpoint_path:
                  lowerCAmelCase					:	Union[str, Any]       =   {'''visual_embedding_dim''': 2048}
                  lowerCAmelCase					:	List[Any]       =   '''vqa_advanced'''
            elif "vqa" in checkpoint_path:
                  lowerCAmelCase					:	Tuple       =   {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
                  lowerCAmelCase					:	Optional[int]       =   '''vqa'''
            elif "nlvr" in checkpoint_path:
                  lowerCAmelCase					:	Dict       =   {
                      '''visual_embedding_dim''': 1024,
                      '''num_labels''': 2,
                  }
                  lowerCAmelCase					:	Optional[int]       =   '''nlvr'''

      lowerCAmelCase					:	str       =   VisualBertConfig(**__UpperCamelCase			)

      # Load State Dict
      lowerCAmelCase					:	int       =   load_state_dict(__UpperCamelCase			)

      lowerCAmelCase					:	Optional[Any]       =   get_new_dict(__UpperCamelCase ,   __UpperCamelCase			)

      if model_type == "pretraining":
            lowerCAmelCase					:	Optional[int]       =   VisualBertForPreTraining(__UpperCamelCase			)
      elif model_type == "vqa":
            lowerCAmelCase					:	Union[str, Any]       =   VisualBertForQuestionAnswering(__UpperCamelCase			)
      elif model_type == "nlvr":
            lowerCAmelCase					:	Tuple       =   VisualBertForVisualReasoning(__UpperCamelCase			)
      elif model_type == "multichoice":
            lowerCAmelCase					:	List[str]       =   VisualBertForMultipleChoice(__UpperCamelCase			)

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


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






"""simple docstring"""





snake_case__		:     List[Any]    				=				'''Tobias Carryer'''

from time import time



class 		snake_case_:






     def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Dict=int(time()				)				):  # noqa: B008
           lowerCAmelCase					:	str       =   multiplier
           lowerCAmelCase					:	Optional[int]       =   increment
           lowerCAmelCase					:	Optional[Any]       =   modulo
           lowerCAmelCase					:	Optional[Any]       =   seed






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Optional[int]       =   (self.multiplier * self.seed + self.increment) % self.modulo
           return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__		:     int    				=				LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
 while True:
  print(lcg.next_number())


