


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()
lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							[
    ('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'),
]

lowerCAmelCase	=							[
    '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			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				torch.load(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''					)
		return sd
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=rename_keys_prefix					):


		"""simple docstring"""




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


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

		lowercase__						=				VisualBertConfig(**SCREAMING_SNAKE_CASE					)

		# Load State Dict
		lowercase__						=				load_state_dict(SCREAMING_SNAKE_CASE					)

		lowercase__						=				get_new_dict(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

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

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


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






import logging

from transformers import PretrainedConfig


lowerCAmelCase	=							logging.getLogger(__name__)


lowerCAmelCase	=							{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''bertabs'''

							def __init__(							self:     List[str]   ,						UpperCamelCase_:     Dict=30_522   ,						UpperCamelCase_:     Union[str, Any]=512   ,						UpperCamelCase_:     Optional[int]=6   ,						UpperCamelCase_:     int=512   ,						UpperCamelCase_:     Optional[int]=8   ,						UpperCamelCase_:     List[Any]=512   ,						UpperCamelCase_:     Tuple=0.2   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Tuple=768   ,						UpperCamelCase_:     List[Any]=8   ,						UpperCamelCase_:     Union[str, Any]=2_048   ,						UpperCamelCase_:     str=0.2   ,						**UpperCamelCase_:     Any   ,						)      ->       List[str]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				vocab_size
									lowercase__						=				max_pos

									lowercase__						=				enc_layers
									lowercase__						=				enc_hidden_size
									lowercase__						=				enc_heads
									lowercase__						=				enc_ff_size
									lowercase__						=				enc_dropout

									lowercase__						=				dec_layers
									lowercase__						=				dec_hidden_size
									lowercase__						=				dec_heads
									lowercase__						=				dec_ff_size
									lowercase__						=				dec_dropout


