
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

lowerCAmelCase					:    Optional[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'
}







def 		A_(     A						:		str = "dhaka"		,     A						:		int = 5):
							UpperCamelCase   			=     min(A		,     50)  # Prevent abuse!
							UpperCamelCase   			=     {
							    'q': query,
							    'tbm': 'isch',
							    'hl': 'en',
							    'ijn': '0',
							}

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

							UpperCamelCase   			=     json.dumps(A)
							UpperCamelCase   			=     json.loads(A)

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

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

							UpperCamelCase   			=     re.findall(
							    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'		,     A		,     )
							for index, fixed_full_res_image in enumerate(A):
														if index >= max_images:
																					return index
														UpperCamelCase   			=     bytes(A		,     'ascii').decode(
														    'unicode-escape')
														UpperCamelCase   			=     bytes(A		,     '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(A)
														UpperCamelCase   			=     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:
												lowerCAmelCase					:    Dict           = 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'''




import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf




@require_vision
@require_torchvision
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				List[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Optional[int]		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				int		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							self.assertEqual(processor.image_processor.to_json_string()      ,						image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				List[str]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""np"""						)

							input_feat_extract.pop("""original_sizes"""						)  # pop original_sizes as it is popped in the processor
							input_feat_extract.pop("""reshaped_input_sizes"""						)  # pop original_sizes as it is popped in the processor

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2						)



					@require_torch
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			[torch.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				int		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(
							    lowerCamelCase_      ,						torch.tensor(lowerCamelCase_						)      ,						torch.tensor(lowerCamelCase_						)						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)

							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Tuple		=			[[1, 0], [0, 1]]
							with self.assertRaises(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)







@require_vision
@require_tf
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				str		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							self.assertEqual(processor.image_processor.to_json_string()      ,						image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			processor(images=lowerCamelCase_      ,						return_tensors="""np"""						)

							input_feat_extract.pop("""original_sizes"""						)  # pop original_sizes as it is popped in the processor
							input_feat_extract.pop("""reshaped_input_sizes"""						)  # pop reshaped_input_sizes as it is popped in the processor

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2						)



					@require_tf
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[tf.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						return_tensors="""tf"""      ,						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				List[str]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)

							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			[[1, 0], [0, 1]]
							with self.assertRaises(tf.errors.InvalidArgumentError						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
									    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)







@require_vision
@require_torchvision
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				int		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				str		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Tuple      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			np.random.randint(0      ,						2      ,						size=(1, 3, 5, 5)						).astype(np.floataa						)
							SCREAMING_SNAKE_CASE :				str		=			[tf.convert_to_tensor(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[torch.tensor(lowerCamelCase_						)]

							SCREAMING_SNAKE_CASE :				Tuple		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							SCREAMING_SNAKE_CASE :				Any		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""pt"""						)

							self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()						)						)



					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				int		=			image_processor(lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Tuple		=			processor(images=lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()

							SCREAMING_SNAKE_CASE :				Dict		=			image_processor(lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()

							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)
							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)
							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)



