





"""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     __A      (			a_						:Tuple)		->		Tuple:
				__a :						Tuple			       =  torch.load(a_   ,       map_location='''cpu''')
				return sd

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

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

				__a :						Any			       =  VisualBertConfig(**a_)

				# Load State Dict
				__a :						Union[str, Any]			       =  load_state_dict(a_)

				__a :						List[str]			       =  get_new_dict(a_   ,       a_)

				if model_type == "pretraining":
								__a :						Optional[Any]			       =  VisualBertForPreTraining(a_)
				elif model_type == "vqa":
								__a :						Union[str, Any]			       =  VisualBertForQuestionAnswering(a_)
				elif model_type == "nlvr":
								__a :						Any			       =  VisualBertForVisualReasoning(a_)
				elif model_type == "multichoice":
								__a :						str			       =  VisualBertForMultipleChoice(a_)

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


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     __A      (			a_						:int   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float(moles / volume) * nfactor)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (volume)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((pressure * volume) / (0.0_8_2_1 * moles)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()