


"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


lowercase__  :     str    					=    logging.get_logger(__name__)
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[Any]          =["""pixel_values"""]

							def __init__(  self					,     __a = True					,     __a = None					,     __a = 0.9					,     __a = PILImageResampling.BICUBIC					,     __a = True					,     __a = None					,     __a = 1 / 2_55					,     __a = True					,     __a = True					,     __a = None					,     __a = None					,     **__a					,     ):
										super().__init__(**__a			)
										__lowerCAmelCase       					=    size if size is not None else {"shortest_edge": 2_24}
										__lowerCAmelCase       					=    get_size_dict(__a					,     default_to_square=__a			)
										__lowerCAmelCase       					=    crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
										__lowerCAmelCase       					=    get_size_dict(__a					,     param_name="crop_size"			)

										__lowerCAmelCase       					=    do_resize
										__lowerCAmelCase       					=    size
										__lowerCAmelCase       					=    crop_pct
										__lowerCAmelCase       					=    resample
										__lowerCAmelCase       					=    do_center_crop
										__lowerCAmelCase       					=    crop_size
										__lowerCAmelCase       					=    do_rescale
										__lowerCAmelCase       					=    rescale_factor
										__lowerCAmelCase       					=    do_normalize
										__lowerCAmelCase       					=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__lowerCAmelCase       					=    image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def     snake_case       (  self					,     __a					,     __a					,     __a = None					,     __a = PILImageResampling.BICUBIC					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    get_size_dict(__a					,     default_to_square=__a			)
										if "shortest_edge" not in size and ("height" not in size or "width" not in size):
													raise ValueError(f"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"			)
										if crop_pct is not None:
													if "shortest_edge" in size:
																__lowerCAmelCase       					=    int(size["shortest_edge"] / crop_pct			)
													elif "height" in size and "width" in size:
																if size["height"] == size["width"]:
																			__lowerCAmelCase       					=    int(size["height"] / crop_pct			)
																else:
																			__lowerCAmelCase       					=    (int(size["height"] / crop_pct			), int(size["width"] / crop_pct			))
													else:
																raise ValueError("Invalid size for resize: {}".format(__a			)			)

													__lowerCAmelCase       					=    get_resize_output_image_size(__a					,     size=__a					,     default_to_square=__a			)
										else:
													if "shortest_edge" in size:
																__lowerCAmelCase       					=    get_resize_output_image_size(__a					,     size=size["shortest_edge"]					,     default_to_square=__a			)
													elif "height" in size and "width" in size:
																__lowerCAmelCase       					=    (size["height"], size["width"])
													else:
																raise ValueError("Invalid size for resize: {}".format(__a			)			)

										return resize(__a					,     size=__a					,     resample=__a					,     data_format=__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    get_size_dict(__a			)
										if "height" not in size or "width" not in size:
													raise ValueError(f"size must contain 'height' and 'width' as keys. Got {size.keys()}"			)
										return center_crop(__a					,     size=(size["height"], size["width"])					,     data_format=__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a = None					,     **__a					,     ):
										return rescale(__a					,     scale=__a					,     data_format=__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a = None					,     **__a					,     ):
										return normalize(__a					,     mean=__a					,     std=__a					,     data_format=__a					,     **__a			)



							def     snake_case       (  self					,     __a					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = ChannelDimension.FIRST					,     **__a					,     ):
										__lowerCAmelCase       					=    do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase       					=    crop_pct if crop_pct is not None else self.crop_pct
										__lowerCAmelCase       					=    resample if resample is not None else self.resample
										__lowerCAmelCase       					=    do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase       					=    do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase       					=    rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase       					=    do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase       					=    image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase       					=    image_std if image_std is not None else self.image_std

										__lowerCAmelCase       					=    size if size is not None else self.size
										__lowerCAmelCase       					=    get_size_dict(__a					,     default_to_square=__a			)
										__lowerCAmelCase       					=    crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase       					=    get_size_dict(__a					,     param_name="crop_size"			)

										__lowerCAmelCase       					=    make_list_of_images(__a			)

										if not valid_images(__a			):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."			)

										if do_resize and size is None or resample is None:
													raise ValueError("Size and resample must be specified if do_resize is True."			)

										if do_center_crop and crop_pct is None:
													raise ValueError("Crop_pct must be specified if do_center_crop is True."			)

										if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."			)

										if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."			)

										# All transformations expect numpy arrays.
										__lowerCAmelCase       					=    [to_numpy_array(__a			) for image in images]

										if do_resize:
													__lowerCAmelCase       					=    [self.resize(image=__a					,     size=__a					,     crop_pct=__a					,     resample=__a			) for image in images]

										if do_center_crop:
													__lowerCAmelCase       					=    [self.center_crop(image=__a					,     size=__a			) for image in images]

										if do_rescale:
													__lowerCAmelCase       					=    [self.rescale(image=__a					,     scale=__a			) for image in images]

										if do_normalize:
													__lowerCAmelCase       					=    [self.normalize(image=__a					,     mean=__a					,     std=__a			) for image in images]

										__lowerCAmelCase       					=    [to_channel_dimension_format(__a					,     __a			) for image in images]

										__lowerCAmelCase       					=    {"pixel_values": images}
										return BatchFeature(data=__a					,     tensor_type=__a			)








"""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




