from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

__lowercase											=      logging.get_logger(__name__)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return [
        int(1_000 * (box[0] / width)			),
        int(1_000 * (box[1] / height)			),
        int(1_000 * (box[2] / width)			),
        int(1_000 * (box[3] / height)			),
    ]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = None			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	tesseract_config if tesseract_config is not None else ''''''

    # apply OCR
    __UpperCamelCase  :str    						=	to_pil_image(_lowerCamelCase			)
    __UpperCamelCase						, __UpperCamelCase  :Union[str, Any]    						=	pil_image.size
    __UpperCamelCase  :List[str]    						=	pytesseract.image_to_data(_lowerCamelCase						,    lang=_lowerCamelCase						,    output_type='''dict'''						,    config=_lowerCamelCase			)
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

    # filter empty words and corresponding coordinates
    __UpperCamelCase  :str    						=	[idx for idx, word in enumerate(_lowerCamelCase			) if not word.strip()]
    __UpperCamelCase  :Optional[Any]    						=	[word for idx, word in enumerate(_lowerCamelCase			) if idx not in irrelevant_indices]
    __UpperCamelCase  :Optional[Any]    						=	[coord for idx, coord in enumerate(_lowerCamelCase			) if idx not in irrelevant_indices]
    __UpperCamelCase  :List[Any]    						=	[coord for idx, coord in enumerate(_lowerCamelCase			) if idx not in irrelevant_indices]
    __UpperCamelCase  :str    						=	[coord for idx, coord in enumerate(_lowerCamelCase			) if idx not in irrelevant_indices]
    __UpperCamelCase  :Any    						=	[coord for idx, coord in enumerate(_lowerCamelCase			) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    __UpperCamelCase  :str    						=	[]
    for x, y, w, h in zip(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase			):
        __UpperCamelCase  :Union[str, Any]    						=	[x, y, x + w, y + h]
        actual_boxes.append(_lowerCamelCase			)

    # finally, normalize the bounding boxes
    __UpperCamelCase  :str    						=	[]
    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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   ["pixel_values"]







    def __init__(			self				,				__lowercase = True				,				__lowercase = None				,				__lowercase = PILImageResampling.BILINEAR				,				__lowercase = True				,				__lowercase = None				,				__lowercase = ""				,				**__lowercase				,				) ->			None:
        super().__init__(**UpperCamelCase__)
        __UpperCamelCase  :Tuple    						=	size if size is not None else {'''height''': 224, '''width''': 224}
        __UpperCamelCase  :List[Any]    						=	get_size_dict(UpperCamelCase__)

        __UpperCamelCase  :List[str]    						=	do_resize
        __UpperCamelCase  :Tuple    						=	size
        __UpperCamelCase  :List[Any]    						=	resample
        __UpperCamelCase  :Optional[int]    						=	apply_ocr
        __UpperCamelCase  :Dict    						=	ocr_lang
        __UpperCamelCase  :List[Any]    						=	tesseract_config







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase = PILImageResampling.BILINEAR				,				__lowercase = None				,				**__lowercase				,				) ->			np.ndarray:
        __UpperCamelCase  :List[str]    						=	get_size_dict(UpperCamelCase__)
        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()}""")
        __UpperCamelCase  :Union[str, Any]    						=	(size['''height'''], size['''width'''])
        return resize(UpperCamelCase__				,				size=UpperCamelCase__				,				resample=UpperCamelCase__				,				data_format=UpperCamelCase__				,				**UpperCamelCase__)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = ChannelDimension.FIRST				,				**__lowercase				,				) ->			PIL.Image.Image:
        __UpperCamelCase  :Union[str, Any]    						=	do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase  :Any    						=	size if size is not None else self.size
        __UpperCamelCase  :Optional[Any]    						=	get_size_dict(UpperCamelCase__)
        __UpperCamelCase  :str    						=	resample if resample is not None else self.resample
        __UpperCamelCase  :int    						=	apply_ocr if apply_ocr is not None else self.apply_ocr
        __UpperCamelCase  :Dict    						=	ocr_lang if ocr_lang is not None else self.ocr_lang
        __UpperCamelCase  :Tuple    						=	tesseract_config if tesseract_config is not None else self.tesseract_config

        __UpperCamelCase  :Tuple    						=	make_list_of_images(UpperCamelCase__)

        if not valid_images(UpperCamelCase__):
            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.''')

        # All transformations expect numpy arrays.
        __UpperCamelCase  :List[str]    						=	[to_numpy_array(UpperCamelCase__) for image in images]

        if apply_ocr:
            requires_backends(self				,				'''pytesseract''')
            __UpperCamelCase  :Optional[int]    						=	[]
            __UpperCamelCase  :List[Any]    						=	[]
            for image in images:
                __UpperCamelCase						, __UpperCamelCase  :Dict    						=	apply_tesseract(UpperCamelCase__				,				UpperCamelCase__				,				UpperCamelCase__)
                words_batch.append(UpperCamelCase__)
                boxes_batch.append(UpperCamelCase__)

        if do_resize:
            __UpperCamelCase  :List[Any]    						=	[self.resize(image=UpperCamelCase__				,				size=UpperCamelCase__				,				resample=UpperCamelCase__) for image in images]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        __UpperCamelCase  :List[Any]    						=	[flip_channel_order(UpperCamelCase__) for image in images]
        __UpperCamelCase  :Union[str, Any]    						=	[to_channel_dimension_format(UpperCamelCase__				,				UpperCamelCase__) for image in images]

        __UpperCamelCase  :List[Any]    						=	BatchFeature(data={'''pixel_values''': images}				,				tensor_type=UpperCamelCase__)

        if apply_ocr:
            __UpperCamelCase  :List[Any]    						=	words_batch
            __UpperCamelCase  :List[str]    						=	boxes_batch
        return data






import requests
from bsa import BeautifulSoup



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE						,    params=SCREAMING_SNAKE_CASE			).content						,    '''html.parser'''			)
    __UpperCamelCase  :int    						=	soup.find('''div'''						,    attrs={'''class''': '''gs_ri'''}			)
    __UpperCamelCase  :Union[str, Any]    						=	div.find('''div'''						,    attrs={'''class''': '''gs_fl'''}			).find_all('''a'''			)
    return anchors[2].get_text()


if __name__ == "__main__":
    __lowercase											=      {
        '''title''': (
            '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
            '''capacitance, volumetric capacitance, and energy density'''
        ),
        '''journal''': '''Chem. Mater.''',
        '''volume''': 30,
        '''pages''': '''3979-3990''',
        '''year''': 2018,
        '''hl''': '''en''',
    }
    print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))





