




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
			import PIL

# soft dependency
if is_pytesseract_available():
			import pytesseract

__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							return [
							    int(1_0_0_0 * (box[0] / width)     ),
							    int(1_0_0_0 * (box[1] / height)     ),
							    int(1_0_0_0 * (box[2] / width)     ),
							    int(1_0_0_0 * (box[3] / height)     ),
							]

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    to_pil_image(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    pil_image.size
							SCREAMING_SNAKE_CASE_   	=    pytesseract.image_to_data(_lowerCAmelCase  ,    lang=_lowerCAmelCase  ,    output_type='''dict'''  ,    config=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

							# filter empty words and corresponding coordinates
							SCREAMING_SNAKE_CASE_   	=    [idx for idx, word in enumerate(_lowerCAmelCase     ) if not word.strip()]
							SCREAMING_SNAKE_CASE_   	=    [word for idx, word in enumerate(_lowerCAmelCase     ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_   	=    [coord for idx, coord in enumerate(_lowerCAmelCase     ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_   	=    [coord for idx, coord in enumerate(_lowerCAmelCase     ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_   	=    [coord for idx, coord in enumerate(_lowerCAmelCase     ) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE_   	=    [coord for idx, coord in enumerate(_lowerCAmelCase     ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							SCREAMING_SNAKE_CASE_   	=    []
							for x, y, w, h in zip(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE_   	=    [x, y, x + w, y + h]
														actual_boxes.append(_lowerCAmelCase     )

							# finally, normalize the bounding boxes
							SCREAMING_SNAKE_CASE_   	=    []
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )     )

							assert len(_lowerCAmelCase     ) == len(_lowerCAmelCase     ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["pixel_values"]


				def __init__(							self:   Optional[Any]		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Dict[str, int] = None		,					_lowerCamelCase:   PILImageResampling = PILImageResampling.BILINEAR		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   float = 1 / 2_55		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Union[float, Iterable[float]] = None		,					_lowerCamelCase:   Union[float, Iterable[float]] = None		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   Optional[str] = ""		,					**_lowerCamelCase:   Dict		,					):
											super().__init__(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    size if size is not None else {'''height''': 2_24, '''width''': 2_24}
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    do_resize
											SCREAMING_SNAKE_CASE_   	=    size
											SCREAMING_SNAKE_CASE_   	=    resample
											SCREAMING_SNAKE_CASE_   	=    do_rescale
											SCREAMING_SNAKE_CASE_   	=    rescale_value
											SCREAMING_SNAKE_CASE_   	=    do_normalize
											SCREAMING_SNAKE_CASE_   	=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											SCREAMING_SNAKE_CASE_   	=    image_std if image_std is not None else IMAGENET_STANDARD_STD
											SCREAMING_SNAKE_CASE_   	=    apply_ocr
											SCREAMING_SNAKE_CASE_   	=    ocr_lang
											SCREAMING_SNAKE_CASE_   	=    tesseract_config


				def     _A							(							self:   str		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Dict[str, int]		,					_lowerCamelCase:   PILImageResampling = PILImageResampling.BILINEAR		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		,					**_lowerCamelCase:   List[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		)
											if "height" not in size or "width" not in size:
																		raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"		)
											SCREAMING_SNAKE_CASE_   	=    (size['''height'''], size['''width'''])
											return resize(_lowerCamelCase		,					size=_lowerCamelCase		,					resample=_lowerCamelCase		,					data_format=_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Union[int, float]		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		,					**_lowerCamelCase:   Union[str, Any]		,					):
											return rescale(_lowerCamelCase		,					scale=_lowerCamelCase		,					data_format=_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Union[float, Iterable[float]]		,					_lowerCamelCase:   Union[float, Iterable[float]]		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		,					**_lowerCamelCase:   Any		,					):
											return normalize(_lowerCamelCase		,					mean=_lowerCamelCase		,					std=_lowerCamelCase		,					data_format=_lowerCamelCase		,					**_lowerCamelCase		)





				def     _A							(							self:   List[str]		,					_lowerCamelCase:   ImageInput		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   Dict[str, int] = None		,					_lowerCamelCase:   Optional[Any]=None		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   float = None		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   Union[float, Iterable[float]] = None		,					_lowerCamelCase:   Union[float, Iterable[float]] = None		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   Optional[Union[str, TensorType]] = None		,					_lowerCamelCase:   ChannelDimension = ChannelDimension.FIRST		,					**_lowerCamelCase:   Any		,					):
											SCREAMING_SNAKE_CASE_   	=    do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE_   	=    size if size is not None else self.size
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE_   	=    do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE_   	=    rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE_   	=    do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE_   	=    image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE_   	=    image_std if image_std is not None else self.image_std
											SCREAMING_SNAKE_CASE_   	=    apply_ocr if apply_ocr is not None else self.apply_ocr
											SCREAMING_SNAKE_CASE_   	=    ocr_lang if ocr_lang is not None else self.ocr_lang
											SCREAMING_SNAKE_CASE_   	=    tesseract_config if tesseract_config is not None else self.tesseract_config

											SCREAMING_SNAKE_CASE_   	=    make_list_of_images(_lowerCamelCase		)

											if not valid_images(_lowerCamelCase		):
																		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:
																		raise ValueError('''Size must be specified if do_resize 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('''If do_normalize is True, image_mean and image_std must be specified.'''		)

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

											# Tesseract OCR to get words + normalized bounding boxes
											if apply_ocr:
																		requires_backends(self		,					'''pytesseract'''		)
																		SCREAMING_SNAKE_CASE_   	=    []
																		SCREAMING_SNAKE_CASE_   	=    []
																		for image in images:
																									SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    apply_tesseract(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)
																									words_batch.append(_lowerCamelCase		)
																									boxes_batch.append(_lowerCamelCase		)

											if do_resize:
																		SCREAMING_SNAKE_CASE_   	=    [self.resize(image=_lowerCamelCase		,					size=_lowerCamelCase		,					resample=_lowerCamelCase		) for image in images]

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

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

											SCREAMING_SNAKE_CASE_   	=    [to_channel_dimension_format(_lowerCamelCase		,					_lowerCamelCase		) for image in images]

											SCREAMING_SNAKE_CASE_   	=    BatchFeature(data={'''pixel_values''': images}		,					tensor_type=_lowerCamelCase		)

											if apply_ocr:
																		SCREAMING_SNAKE_CASE_   	=    words_batch
																		SCREAMING_SNAKE_CASE_   	=    boxes_batch
											return data








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						="""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

			print("""Googling.....""")

			__SCREAMING_SNAKE_CASE 						=f"""https://www.google.com/search?q={query}&num=100"""

			__SCREAMING_SNAKE_CASE 						=requests.get(
			    url,
			    headers={"""User-Agent""": str(UserAgent().random)},
			)

			try:
						__SCREAMING_SNAKE_CASE 						=(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """yuRUbf"""})
						    .find("""a""")
						    .get("""href""")
						)

			except AttributeError:
						__SCREAMING_SNAKE_CASE 						=parse_qs(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """kCrYT"""})
						    .find("""a""")
						    .get("""href""")
						)["""url"""][0]

			webbrowser.open(link)


