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

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE__		:					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"
}







def     _a				(      lowercase__  :			str = "dhaka"     ,  lowercase__  :			int = 5 )     ->   List[Any]:







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		min(lowercase__     ,  50 )  # Prevent abuse!
     SCREAMING_SNAKE_CASE__				:				List[str]			=		{
         'q': query,
         'tbm': 'isch',
         'hl': 'en',
         'ijn': '0',
     }

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

     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		json.dumps(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		json.loads(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		re.findall(
         r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'     ,  lowercase__     ,  )
     if not matched_google_image_data:
          return 0

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

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


if __name__ == "__main__":
      try:
            SCREAMING_SNAKE_CASE__		:					Optional[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




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      SCREAMING_SNAKE_CASE__		:					Any						=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	List[Any]     , a_     :	Optional[int]     , a_     :	Dict=7     , a_     :	Any=3     , a_     :	Any=18     , a_     :	int=30     , a_     :	int=400     , a_     :	List[Any]=None     , a_     :	int=True     , a_     :	int=True     , a_     :	Dict=None     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'height': 20, 'width': 20}
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_convert_rgb
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[512, 1024, 2048, 4096]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size if patch_size is not None else {'height': 16, 'width': 16}




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE__				:				str			=		Image.open(requests.get(a_     , stream=a_				).raw				).convert('RGB'				)
       return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		PixaStructImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor_tester.prepare_dummy_image()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		2048

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(a_     , return_tensors='pt'     , max_patches=a_				)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0606				)     , atol=1e-3     , rtol=1e-3				)				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE__				:				int			=		True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(a_				):
                 SCREAMING_SNAKE_CASE__				:				Dict			=		image_processor(
                     image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Hello'

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Any			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , numpify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , np.ndarray				)

       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				str			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				int			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , torchify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , torch.Tensor				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Any			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		PixaStructImageProcessingTester(self     , num_channels=4				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		3




  @property
  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	str				)->       Union[str, Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Tuple			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )



