
"""simple docstring"""





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

import requests
from bsa import BeautifulSoup

_a														=		{
    """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__		(      __snake_case = "dhaka",					__snake_case = 5     )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  min(__snake_case,					50     )  # Prevent abuse!
					_UpperCamelCase										=  {
					    '''q''': query,
					    '''tbm''': '''isch''',
					    '''hl''': '''en''',
					    '''ijn''': '''0''',
					}

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

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

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

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

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


if __name__ == "__main__":
				try:
								_a														=		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"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				(
		    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
		    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
		    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
		)
		lowercase__      =				'CIDAS/clipseg-rd64-refined'
		lowercase__      =				'image_segmenter'
		lowercase__      =				CLIPSegForImageSegmentation

		lowercase__      =				['image', 'text']
		lowercase__      =				['image']



		def __init__( self    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(text=[label]    ,		images=[image]    ,		padding=__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												_UpperCamelCase										=  self.model(**__a).logits
							return logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.cpu().detach().numpy()
							_UpperCamelCase										=  0
							_UpperCamelCase										=  1
							return Image.fromarray((array * 2_55).astype(np.uinta))






