

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

import requests
from bsa import BeautifulSoup

UpperCamelCase__		 =     {
    '''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__      ( __A    :str = "dhaka"	,__A    :int = 5		):


       """simple docstring"""




       __snake_case       	=							min(__A	,5_0		)  # Prevent abuse!
       __snake_case       	=							{
           """q""": query,
           """tbm""": """isch""",
           """hl""": """en""",
           """ijn""": """0""",
       }

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

       __snake_case       	=							json.dumps(__A		)
       __snake_case       	=							json.loads(__A		)

       __snake_case       	=							re.findall(
           r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""	,__A	,)
       if not matched_google_image_data:
              return 0

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

       __snake_case       	=							re.findall(
           r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""	,__A	,)
       for index, fixed_full_res_image in enumerate(__A		):
              if index >= max_images:
                     return index
              __snake_case       	=							bytes(__A	,"""ascii"""		).decode(
                  """unicode-escape"""		)
              __snake_case       	=							bytes(__A	,"""ascii"""		).decode(
                  """unicode-escape"""		)
              __snake_case       	=							urllib.request.build_opener()
              __snake_case       	=							[
                  (
                      """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(__A		)
              __snake_case       	=							F'query_{query.replace(" "	,"_"		)}'
              if not os.path.exists(__A		):
                     os.makedirs(__A		)
              urllib.request.urlretrieve(  # noqa: S310
                  __A	,F'{path_name}/original_size_img_{index}.jpg'		)
       return index


if __name__ == "__main__":
    try:
        UpperCamelCase__		 =     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




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=sys.maxsize   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							"""bilinear"""
              __snake_case       	=							max_size
              __snake_case       	=							short_edge_length






       def __call__(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							[]
              for img in imgs:
                     __snake_case	,    __snake_case       	=							img.shape[:2]
                     # later: provide list and randomly choose index for resize
                     __snake_case       	=							np.random.randint(self.short_edge_length[0]      , self.short_edge_length[1] + 1   )
                     if size == 0:
                            return img
                     __snake_case       	=							size * 1.0 / min(_UpperCamelCase      , _UpperCamelCase   )
                     if h < w:
                            __snake_case	,    __snake_case       	=							size, scale * w
                     else:
                            __snake_case	,    __snake_case       	=							scale * h, size
                     if max(_UpperCamelCase      , _UpperCamelCase   ) > self.max_size:
                            __snake_case       	=							self.max_size * 1.0 / max(_UpperCamelCase      , _UpperCamelCase   )
                            __snake_case       	=							newh * scale
                            __snake_case       	=							neww * scale
                     __snake_case       	=							int(neww + 0.5   )
                     __snake_case       	=							int(newh + 0.5   )

                     if img.dtype == np.uinta:
                            __snake_case       	=							Image.fromarray(_UpperCamelCase   )
                            __snake_case       	=							pil_image.resize((neww, newh)      , PILImageResampling.BILINEAR   )
                            __snake_case       	=							np.asarray(_UpperCamelCase   )
                     else:
                            __snake_case       	=							img.permute(2      , 0      , 1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
                            __snake_case       	=							nn.functional.interpolate(
                                _UpperCamelCase      , (newh, neww)      , mode=self.interp_method      , align_corners=_UpperCamelCase   ).squeeze(0   )
                     img_augs.append(_UpperCamelCase   )

              return img_augs






class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      , cfg.INPUT.MAX_SIZE_TEST   )
              __snake_case       	=							cfg.INPUT.FORMAT
              __snake_case       	=							cfg.SIZE_DIVISIBILITY
              __snake_case       	=							cfg.PAD_VALUE
              __snake_case       	=							cfg.INPUT.MAX_SIZE_TEST
              __snake_case       	=							cfg.MODEL.DEVICE
              __snake_case       	=							torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )      , 1      , 1   )
              __snake_case       	=							torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )      , 1      , 1   )
              __snake_case       	=							lambda _UpperCamelCase   : (x - self.pixel_mean) / self.pixel_std


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              __snake_case       	=							tuple(max(_UpperCamelCase   ) for s in zip(*[img.shape for img in images]   )   )
              __snake_case       	=							[im.shape[-2:] for im in images]
              __snake_case       	=							[
                  nn.functional.pad(
                      _UpperCamelCase      , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      , value=self.pad_value      , )
                  for size, im in zip(_UpperCamelCase      , _UpperCamelCase   )
              ]

              return torch.stack(_UpperCamelCase   ), torch.tensor(_UpperCamelCase   )






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase=False   )      ->							Union[str, Any]:






              """simple docstring"""


              with torch.no_grad():
                     if not isinstance(_UpperCamelCase      , _UpperCamelCase   ):
                            __snake_case       	=							[images]
                     if single_image:
                            assert len(_UpperCamelCase   ) == 1
                     for i in range(len(_UpperCamelCase   )   ):
                            if isinstance(images[i]      , torch.Tensor   ):
                                   images.insert(_UpperCamelCase      , images.pop(_UpperCamelCase   ).to(self.device   ).float()   )
                            elif not isinstance(images[i]      , torch.Tensor   ):
                                   images.insert(
                                       _UpperCamelCase      , torch.as_tensor(img_tensorize(images.pop(_UpperCamelCase   )      , input_format=self.input_format   )   )
                                       .to(self.device   )
                                       .float()      , )
            # resize smallest edge
                     __snake_case       	=							torch.tensor([im.shape[:2] for im in images]   )
                     __snake_case       	=							self.aug(_UpperCamelCase   )
                     # transpose images and convert to torch tensors
                     # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                     # now normalize before pad to avoid useless arithmetic
                     __snake_case       	=							[self.normalizer(_UpperCamelCase   ) for x in images]
                     # now pad them to do the following operations
                     __snake_case	,    __snake_case       	=							self.pad(_UpperCamelCase   )
                     # Normalize

                     if self.size_divisibility > 0:
                            raise NotImplementedError()
                     # pad
                     __snake_case       	=							torch.true_divide(_UpperCamelCase      , _UpperCamelCase   )
                     if single_image:
                            return images[0], sizes[0], scales_yx[0]
                     else:
                            return images, sizes, scales_yx







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :List[Any]		):


       """simple docstring"""




       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :Tuple[int, int]		):


       """simple docstring"""




       assert torch.isfinite(__A		).all(), "Box tensor contains infinite or NaN!"
       __snake_case	,    __snake_case       	=							box_size
       tensor[:, 0].clamp_(min=0	,max=__A		)
       tensor[:, 1].clamp_(min=0	,max=__A		)
       tensor[:, 2].clamp_(min=0	,max=__A		)
       tensor[:, 3].clamp_(min=0	,max=__A		)

