


'''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	:	Optional[Any]       =     logging.get_logger(__name__)

__A	:	List[str]       =     [
    ("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	:	Dict       =     [
    "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      (		lowerCamelCase_  :Dict ):







		'''simple docstring'''
		snake_case_		:							int			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )
		return sd





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Tuple=rename_keys_prefix ):







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





@torch.no_grad()
def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :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_		:							Tuple			       =						{'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_		:							Tuple			       =						{'visual_embedding_dim': 5_12}
						snake_case_		:							List[Any]			       =						'multichoice'
				elif "vqa_advanced" in checkpoint_path:
						snake_case_		:							Tuple			       =						{'visual_embedding_dim': 20_48}
						snake_case_		:							str			       =						'vqa_advanced'
				elif "vqa" in checkpoint_path:
						snake_case_		:							List[str]			       =						{'visual_embedding_dim': 20_48, 'num_labels': 31_29}
						snake_case_		:							Optional[Any]			       =						'vqa'
				elif "nlvr" in checkpoint_path:
						snake_case_		:							int			       =						{
						    'visual_embedding_dim': 10_24,
						    'num_labels': 2,
						}
						snake_case_		:							List[Any]			       =						'nlvr'

		snake_case_		:							Any			       =						VisualBertConfig(**lowerCamelCase_ )

		# Load State Dict
		snake_case_		:							Any			       =						load_state_dict(lowerCamelCase_ )

		snake_case_		:							Optional[Any]			       =						get_new_dict(lowerCamelCase_				,      lowerCamelCase_ )

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

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


if __name__ == "__main__":
							__A	:	List[Any]       =     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	:	Union[str, Any]       =     parser.parse_args()
							convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))