


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A  :     List[str]    					=    logging.get_logger(__name__)

A  :     Dict    					=    {}
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""llama"""
							__UpperCAmelCase		:					Union[str, Any]          =["""past_key_values"""]

							def __init__(  self					,     __a=3_20_00					,     __a=40_96					,     __a=1_10_08					,     __a=32					,     __a=32					,     __a=None					,     __a="silu"					,     __a=20_48					,     __a=0.0_2					,     __a=1e-6					,     __a=True					,     __a=0					,     __a=1					,     __a=2					,     __a=1					,     __a=False					,     __a=None					,     **__a					,     ):
										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    max_position_embeddings
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads

										# for backward compatibility
										if num_key_value_heads is None:
													__lowerCAmelCase       					=    num_attention_heads

										__lowerCAmelCase       					=    num_key_value_heads
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    rms_norm_eps
										__lowerCAmelCase       					=    pretraining_tp
										__lowerCAmelCase       					=    use_cache
										__lowerCAmelCase       					=    rope_scaling
										self._rope_scaling_validation()

										super().__init__(
										    pad_token_id=__a					,     bos_token_id=__a					,     eos_token_id=__a					,     tie_word_embeddings=__a					,     **__a					,     )



							def     snake_case       (  self			):
										if self.rope_scaling is None:
													return

										if not isinstance(self.rope_scaling					,     __a			) or len(self.rope_scaling			) != 2:
													raise ValueError(
													    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
													    f"got {self.rope_scaling}"			)
										__lowerCAmelCase       					=    self.rope_scaling.get("type"					,     __a			)
										__lowerCAmelCase       					=    self.rope_scaling.get("factor"					,     __a			)
										if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"			)
										if rope_scaling_factor is None or not isinstance(__a					,     __a			) or rope_scaling_factor <= 1.0:
													raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)








"""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




