


import unittest

import numpy as np

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

if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class      lowercase__	(       unittest.TestCase		):






  def __init__(       self   :       List[Any]		,    _lowercase   :       Dict		,    _lowercase   :       List[str]=7		,    _lowercase   :       Any=3		,    _lowercase   :       str=18		,    _lowercase   :       List[Any]=30		,    _lowercase   :       Optional[int]=4_00		,    _lowercase   :       int=True		,    _lowercase   :       int=None		,    _lowercase   :       Optional[Any]=True		,    _lowercase   :       Tuple=None		,    _lowercase   :       Tuple=True		,    ):




         """simple docstring"""







         UpperCAmelCase__					    =  size if size is not None else {"shortest_edge": 20}
         UpperCAmelCase__					    =  crop_size if crop_size is not None else {"height": 18, "width": 18}
         UpperCAmelCase__					    =  parent
         UpperCAmelCase__					    =  batch_size
         UpperCAmelCase__					    =  num_channels
         UpperCAmelCase__					    =  image_size
         UpperCAmelCase__					    =  min_resolution
         UpperCAmelCase__					    =  max_resolution
         UpperCAmelCase__					    =  do_resize
         UpperCAmelCase__					    =  size
         UpperCAmelCase__					    =  do_center_crop
         UpperCAmelCase__					    =  crop_size
         UpperCAmelCase__					    =  do_flip_channel_order






  def       _UpperCAmelCase	(       self   :       List[Any]			):




         """simple docstring"""







         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_flip_channel_order": self.do_flip_channel_order,
         }




@require_torch
@require_vision
class      lowercase__	(       lowerCAmelCase__       ,				unittest.TestCase		):
  A__=	MobileViTImageProcessor if is_vision_available() else None






  def       _UpperCAmelCase	(       self   :       Any			):




         """simple docstring"""







         UpperCAmelCase__					    =  MobileViTImageProcessingTester(self			)






  @property
  def       _UpperCAmelCase	(       self   :       Union[str, Any]			):




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def       _UpperCAmelCase	(       self   :       List[str]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(__lowerCAmelCase		,    "do_resize"			)			)
         self.assertTrue(hasattr(__lowerCAmelCase		,    "size"			)			)
         self.assertTrue(hasattr(__lowerCAmelCase		,    "do_center_crop"			)			)
         self.assertTrue(hasattr(__lowerCAmelCase		,    "center_crop"			)			)
         self.assertTrue(hasattr(__lowerCAmelCase		,    "do_flip_channel_order"			)			)






  def       _UpperCAmelCase	(       self   :       Optional[int]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size		,    {"shortest_edge": 20}			)
         self.assertEqual(image_processor.crop_size		,    {"height": 18, "width": 18}			)

         UpperCAmelCase__					    =  self.image_processing_class.from_dict(self.image_processor_dict		,    size=42		,    crop_size=84			)
         self.assertEqual(image_processor.size		,    {"shortest_edge": 42}			)
         self.assertEqual(image_processor.crop_size		,    {"height": 84, "width": 84}			)






  def       _UpperCAmelCase	(       self   :       int			):




         """simple docstring"""







         pass






  def       _UpperCAmelCase	(       self   :       Union[str, Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.image_processing_class(**self.image_processor_dict			)
         # create random PIL images
         UpperCAmelCase__					    =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__lowerCAmelCase			)
         for image in image_inputs:
                self.assertIsInstance(__lowerCAmelCase		,    Image.Image			)

         # Test not batched input
         UpperCAmelCase__					    =  image_processing(image_inputs[0]		,    return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape		,    (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,    )

         # Test batched
         UpperCAmelCase__					    =  image_processing(__lowerCAmelCase		,    return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape		,    (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,    )






  def       _UpperCAmelCase	(       self   :       Optional[Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         UpperCAmelCase__					    =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__lowerCAmelCase		,    numpify=__lowerCAmelCase			)
         for image in image_inputs:
                self.assertIsInstance(__lowerCAmelCase		,    np.ndarray			)

         # Test not batched input
         UpperCAmelCase__					    =  image_processing(image_inputs[0]		,    return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape		,    (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,    )

         # Test batched
         UpperCAmelCase__					    =  image_processing(__lowerCAmelCase		,    return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape		,    (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,    )






  def       _UpperCAmelCase	(       self   :       List[Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         UpperCAmelCase__					    =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__lowerCAmelCase		,    torchify=__lowerCAmelCase			)
         for image in image_inputs:
                self.assertIsInstance(__lowerCAmelCase		,    torch.Tensor			)

         # Test not batched input
         UpperCAmelCase__					    =  image_processing(image_inputs[0]		,    return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape		,    (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,    )

         # Test batched
         UpperCAmelCase__					    =  image_processing(__lowerCAmelCase		,    return_tensors="pt"			).pixel_values
         self.assertEqual(
             encoded_images.shape		,    (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )		,    )






'''simple docstring'''




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

import requests
from bsa import BeautifulSoup

__lowercase        =    {
    '''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 							snake_case__				(     _A:   str = "dhaka" ,  _A:   int = 5					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    min(_A ,  50					)  # Prevent abuse!
		lowerCAmelCase     =    {
		    """q""": query,
		    """tbm""": """isch""",
		    """hl""": """en""",
		    """ijn""": """0""",
		}

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

		lowerCAmelCase     =    json.dumps(_A					)
		lowerCAmelCase     =    json.loads(_A					)

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

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

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


if __name__ == "__main__":
						try:
												__lowercase        =    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




