


"""simple docstring"""


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_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VivitImageProcessor
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7					,     __a=3					,     __a=10					,     __a=18					,     __a=30					,     __a=4_00					,     __a=True					,     __a=None					,     __a=True					,     __a=[0.5, 0.5, 0.5]					,     __a=[0.5, 0.5, 0.5]					,     __a=None					,     ):
										__lowerCAmelCase       					=    size if size is not None else {"shortest_edge": 18}
										__lowerCAmelCase       					=    crop_size if crop_size is not None else {"height": 18, "width": 18}

										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    num_frames
										__lowerCAmelCase       					=    image_size
										__lowerCAmelCase       					=    min_resolution
										__lowerCAmelCase       					=    max_resolution
										__lowerCAmelCase       					=    do_resize
										__lowerCAmelCase       					=    size
										__lowerCAmelCase       					=    do_normalize
										__lowerCAmelCase       					=    image_mean
										__lowerCAmelCase       					=    image_std
										__lowerCAmelCase       					=    crop_size



							def     snake_case       (  self			):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}
@require_torch
@require_vision
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =VivitImageProcessor if is_vision_available() else None

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    VivitImageProcessingTester(self			)

							@property
							def     snake_case       (  self			):
										return self.image_processor_tester.prepare_image_processor_dict()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__a					,     "image_mean"			)			)
										self.assertTrue(hasattr(__a					,     "image_std"			)			)
										self.assertTrue(hasattr(__a					,     "do_normalize"			)			)
										self.assertTrue(hasattr(__a					,     "do_resize"			)			)
										self.assertTrue(hasattr(__a					,     "do_center_crop"			)			)
										self.assertTrue(hasattr(__a					,     "size"			)			)

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

										__lowerCAmelCase       					=    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     snake_case       (  self			):
										# Initialize image_processing
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										# create random PIL videos
										__lowerCAmelCase       					=    prepare_video_inputs(self.image_processor_tester					,     equal_resolution=__a			)
										for video in video_inputs:
													self.assertIsInstance(__a					,     __a			)
													self.assertIsInstance(video[0]					,     Image.Image			)

										# Test not batched input
										__lowerCAmelCase       					=    image_processing(video_inputs[0]					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

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

							def     snake_case       (  self			):
										# Initialize image_processing
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										__lowerCAmelCase       					=    prepare_video_inputs(self.image_processor_tester					,     equal_resolution=__a					,     numpify=__a			)
										for video in video_inputs:
													self.assertIsInstance(__a					,     __a			)
													self.assertIsInstance(video[0]					,     np.ndarray			)

										# Test not batched input
										__lowerCAmelCase       					=    image_processing(video_inputs[0]					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

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



							def     snake_case       (  self			):
										# Initialize image_processing
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										__lowerCAmelCase       					=    prepare_video_inputs(self.image_processor_tester					,     equal_resolution=__a					,     torchify=__a			)
										for video in video_inputs:
													self.assertIsInstance(__a					,     __a			)
													self.assertIsInstance(video[0]					,     torch.Tensor			)

										# Test not batched input
										__lowerCAmelCase       					=    image_processing(video_inputs[0]					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

										# Test batched
										__lowerCAmelCase       					=    image_processing(__a					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        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

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




