





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

import requests
from bsa import BeautifulSoup

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'''
}






def 					lowerCamelCase_						(    _UpperCamelCase = "dhaka"			,    _UpperCamelCase = 5			)		->						int:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  min(_UpperCamelCase			,    50			)  # Prevent abuse!
				snake_case_						:   str           =  {
				    '''q''': query,
				    '''tbm''': '''isch''',
				    '''hl''': '''en''',
				    '''ijn''': '''0''',
				}

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

				snake_case_						:   Dict           =  json.dumps(_UpperCamelCase			)
				snake_case_						:   List[Any]           =  json.loads(_UpperCamelCase			)

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

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

				snake_case_						:   int           =  re.findall(
				    R'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'''			,    _UpperCamelCase			,    )
				for index, fixed_full_res_image in enumerate(_UpperCamelCase			):
								if index >= max_images:
												return index
								snake_case_						:   Optional[int]           =  bytes(_UpperCamelCase			,    '''ascii'''			).decode(
								    '''unicode-escape'''			)
								snake_case_						:   List[Any]           =  bytes(_UpperCamelCase			,    '''ascii'''			).decode(
								    '''unicode-escape'''			)
								snake_case_						:   str           =  urllib.request.build_opener()
								snake_case_						:   str           =  [
								    (
								        '''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			)
								snake_case_						:   Optional[int]           =  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:
		lowerCAmelCase_    =    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 os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if images is None and text is None:
											raise ValueError('''You have to specify at least images or text.'''   )

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__ ,							**__magic_name__   )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
