


"""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()
lowercase__  :     Optional[int]    					=    logging.get_logger(__name__)

lowercase__  :     Optional[int]    					=    [
    ('''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'''),
]

lowercase__  :     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 _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location="cpu"   )
			return sd


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=rename_keys_prefix   ):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''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:
						__lowerCAmelCase       					=    'pretraining'
						if "vcr" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 512}
						elif "vqa_advanced" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 2048}
						elif "vqa" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 2048}
						elif "nlvr" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 1024}
						else:
									raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."   )
			else:
						if "vcr" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 512}
									__lowerCAmelCase       					=    'multichoice'
						elif "vqa_advanced" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 2048}
									__lowerCAmelCase       					=    'vqa_advanced'
						elif "vqa" in checkpoint_path:
									__lowerCAmelCase       					=    {'visual_embedding_dim': 2048, 'num_labels': 3129}
									__lowerCAmelCase       					=    'vqa'
						elif "nlvr" in checkpoint_path:
									__lowerCAmelCase       					=    {
									    'visual_embedding_dim': 1024,
									    'num_labels': 2,
									}
									__lowerCAmelCase       					=    'nlvr'

			__lowerCAmelCase       					=    VisualBertConfig(**_UpperCamelCase   )

			# Load State Dict
			__lowerCAmelCase       					=    load_state_dict(_UpperCamelCase   )

			__lowerCAmelCase       					=    get_new_dict(_UpperCamelCase					,				_UpperCamelCase   )

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

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


if __name__ == "__main__":
	lowercase__  :     str    					=    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.")
	lowercase__  :     Dict    					=    parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""


import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

A  :     Union[str, Any]    					=    {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}


def _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    min(_UpperCamelCase					,				50   )  # Prevent abuse!
			__lowerCAmelCase       					=    {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

			__lowerCAmelCase       					=    requests.get("https://www.google.com/search"					,				params=_UpperCamelCase					,				headers=_UpperCamelCase   )
			__lowerCAmelCase       					=    BeautifulSoup(html.text					,				"html.parser"   )
			__lowerCAmelCase       					=    "".join(
			    re.findall(R"AF_initDataCallback\(([^<]+)\);"					,				str(soup.select("script"   )   )   )   )

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

			__lowerCAmelCase       					=    re.findall(
			    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","					,				_UpperCamelCase					,				)
			if not matched_google_image_data:
						return 0

			__lowerCAmelCase       					=    re.sub(
			    R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"					,				""					,				str(_UpperCamelCase   )					,				)

			__lowerCAmelCase       					=    re.findall(
			    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"					,				_UpperCamelCase					,				)
			for index, fixed_full_res_image in enumerate(_UpperCamelCase   ):
						if index >= max_images:
									return index
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    urllib.request.build_opener()
						__lowerCAmelCase       					=    [
						    (
						        "User-Agent",
						        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
						        " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
						    )
						]
						urllib.request.install_opener(_UpperCamelCase   )
						__lowerCAmelCase       					=    f"query_{query.replace(' '					,				'_'   )}"
						if not os.path.exists(_UpperCamelCase   ):
									os.makedirs(_UpperCamelCase   )
						urllib.request.urlretrieve(  # noqa: S310
						    _UpperCamelCase					,				f"{path_name}/original_size_img_{index}.jpg"   )
			return index


if __name__ == "__main__":
	try:
		A  :     Any    					=    download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print("Please provide a search term.")
		raise




