
"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def      lowerCAmelCase__			(		__magic_name__      )						->Optional[int]:
			def wrapper(*__magic_name__			,   **__magic_name__      ):
						__lowercase       =       timeit.default_timer()
						__lowercase       =       func(*__magic_name__			,   **__magic_name__      )
						__lowercase       =       timeit.default_timer() - starttime
						return delta

			__lowercase       =       func.__name__

			return wrapper



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__=1_0_0			,   __magic_name__=None      )						->Union[str, Any]:
			__lowercase       =       []
			__lowercase       =       seq_shapes or {}
			for i in range(__magic_name__      ):
						__lowercase       =       {}
						for col_id, (k, v) in enumerate(features.items()      ):
									if isinstance(__magic_name__			,   _ArrayXD      ):
												__lowercase       =       np.random.rand(*v.shape      ).astype(v.dtype      )
									elif isinstance(__magic_name__			,   datasets.Value      ):
												if v.dtype == "string":
															__lowercase       =       "The small grey turtle was surprisingly fast when challenged."
												else:
															__lowercase       =       np.random.randint(1_0			,   size=1      ).astype(v.dtype      ).item()
									elif isinstance(__magic_name__			,   datasets.Sequence      ):
												while isinstance(__magic_name__			,   datasets.Sequence      ):
															__lowercase       =       v.feature
												__lowercase       =       seq_shapes[k]
												__lowercase       =       np.random.rand(*__magic_name__      ).astype(v.dtype      )
									__lowercase       =       data

						dummy_data.append((i, example)      )

			return dummy_data







def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__=1_0_0			,   __magic_name__=None      )						->Any:
			__lowercase       =       generate_examples(__magic_name__			,   num_examples=__magic_name__			,   seq_shapes=__magic_name__      )

			with ArrowWriter(features=__magic_name__			,   path=__magic_name__      ) as writer:
						for key, record in dummy_data:
									__lowercase       =       features.encode_example(__magic_name__      )
									writer.write(__magic_name__      )

						__lowercase   ,					__lowercase       =       writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''      )

			__lowercase       =       datasets.Dataset.from_file(filename=__magic_name__			,   info=datasets.DatasetInfo(features=__magic_name__      )      )

			return dataset







"""simple docstring"""






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

import requests
from bsa import BeautifulSoup

_lowercase							=				{
    '''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__			(		__magic_name__ = "dhaka"			,   __magic_name__ = 5      )						->int:
			__lowercase       =       min(__magic_name__			,   5_0      )  # Prevent abuse!
			__lowercase       =       {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

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

			__lowercase       =       json.dumps(__magic_name__      )
			__lowercase       =       json.loads(__magic_name__      )

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

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

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


if __name__ == "__main__":
					try:
										_lowercase							=				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





