



"""simple docstring"""
import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

SCREAMING_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"""
}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = "dhaka", SCREAMING_SNAKE_CASE__ = 5	)			->      int:
		a_     :      List[Any] 						=     min(SCREAMING_SNAKE_CASE__, 50	)  # Prevent abuse!
		a_     :      List[str] 						=     {
		    "q": query,
		    "tbm": "isch",
		    "hl": "en",
		    "ijn": "0",
		}

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

		a_     :      List[str] 						=     json.dumps(SCREAMING_SNAKE_CASE__	)
		a_     :      List[str] 						=     json.loads(SCREAMING_SNAKE_CASE__	)

		a_     :      List[str] 						=     re.findall(
		    r"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",", SCREAMING_SNAKE_CASE__, )
		if not matched_google_image_data:
				return 0

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

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


if __name__ == "__main__":
					try:
										SCREAMING_SNAKE_CASE_				= 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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)