'''simple docstring'''





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

lowerCAmelCase_	:		Any			=     logging.get_logger(__name__)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						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							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase = None					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						tesseract_config if tesseract_config is not None else ""

						# apply OCR
						_UpperCamelCase			:			str   			=						to_pil_image(UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						pil_image.size
						_UpperCamelCase			:			str   			=						pytesseract.image_to_data(UpperCAmelCase     ,lang=UpperCAmelCase     ,output_type="dict"     ,config=UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

						return words, normalized_boxes






class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							['''pixel_values''']





				def __init__(						self					:						List[str]       ,		lowercase__					:						bool = True       ,		lowercase__					:						Dict[str, int] = None       ,		lowercase__					:						PILImageResampling = PILImageResampling.BILINEAR       ,		lowercase__					:						bool = True       ,		lowercase__					:						Optional[str] = None       ,		lowercase__					:						Optional[str] = ""       ,		**lowercase__					:						Optional[Any]       ,		)							->None:


										'''simple docstring'''




										super().__init__(**lowercase__					)
										_UpperCamelCase			:			Any   			=						size if size is not None else {"height": 224, "width": 224}
										_UpperCamelCase			:			Union[str, Any]   			=						get_size_dict(lowercase__					)

										_UpperCamelCase			:			Tuple   			=						do_resize
										_UpperCamelCase			:			Optional[Any]   			=						size
										_UpperCamelCase			:			str   			=						resample
										_UpperCamelCase			:			Optional[int]   			=						apply_ocr
										_UpperCamelCase			:			Any   			=						ocr_lang
										_UpperCamelCase			:			str   			=						tesseract_config





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						np.ndarray       ,		lowercase__					:						Dict[str, int]       ,		lowercase__					:						PILImageResampling = PILImageResampling.BILINEAR       ,		lowercase__					:						Optional[Union[str, ChannelDimension]] = None       ,		**lowercase__					:						Dict       ,		)							->np.ndarray:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						get_size_dict(lowercase__					)
										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			:			Tuple   			=						(size["height"], size["width"])
										return resize(lowercase__       ,		size=lowercase__       ,		resample=lowercase__       ,		data_format=lowercase__       ,		**lowercase__					)







				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						ImageInput       ,		lowercase__					:						bool = None       ,		lowercase__					:						Dict[str, int] = None       ,		lowercase__					:						PILImageResampling = None       ,		lowercase__					:						bool = None       ,		lowercase__					:						Optional[str] = None       ,		lowercase__					:						Optional[str] = None       ,		lowercase__					:						Optional[Union[str, TensorType]] = None       ,		lowercase__					:						ChannelDimension = ChannelDimension.FIRST       ,		**lowercase__					:						int       ,		)							->PIL.Image.Image:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						do_resize if do_resize is not None else self.do_resize
										_UpperCamelCase			:			str   			=						size if size is not None else self.size
										_UpperCamelCase			:			Union[str, Any]   			=						get_size_dict(lowercase__					)
										_UpperCamelCase			:			List[str]   			=						resample if resample is not None else self.resample
										_UpperCamelCase			:			Any   			=						apply_ocr if apply_ocr is not None else self.apply_ocr
										_UpperCamelCase			:			List[Any]   			=						ocr_lang if ocr_lang is not None else self.ocr_lang
										_UpperCamelCase			:			Dict   			=						tesseract_config if tesseract_config is not None else self.tesseract_config

										_UpperCamelCase			:			List[Any]   			=						make_list_of_images(lowercase__					)

										if not valid_images(lowercase__					):
																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			:			Any   			=						[to_numpy_array(lowercase__					) for image in images]

										if apply_ocr:
																requires_backends(self       ,		"pytesseract"					)
																_UpperCamelCase			:			Union[str, Any]   			=						[]
																_UpperCamelCase			:			Tuple   			=						[]
																for image in images:
																						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						apply_tesseract(lowercase__       ,		lowercase__       ,		lowercase__					)
																						words_batch.append(lowercase__					)
																						boxes_batch.append(lowercase__					)

										if do_resize:
																_UpperCamelCase			:			Any   			=						[self.resize(image=lowercase__       ,		size=lowercase__       ,		resample=lowercase__					) for image in images]

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										_UpperCamelCase			:			Optional[Any]   			=						[flip_channel_order(lowercase__					) for image in images]
										_UpperCamelCase			:			Optional[int]   			=						[to_channel_dimension_format(lowercase__       ,		lowercase__					) for image in images]

										_UpperCamelCase			:			Optional[int]   			=						BatchFeature(data={"pixel_values": images}       ,		tensor_type=lowercase__					)

										if apply_ocr:
																_UpperCamelCase			:			Tuple   			=						words_batch
																_UpperCamelCase			:			Optional[Any]   			=						boxes_batch
										return data



'''simple docstring'''





import requests
from bsa import BeautifulSoup
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						BeautifulSoup(requests.get(UpperCAmelCase     ,params=UpperCAmelCase					).content     ,"html.parser"					)
						_UpperCamelCase			:			Union[str, Any]   			=						soup.find("div"     ,attrs={"class": "gs_ri"}					)
						_UpperCamelCase			:			Optional[Any]   			=						div.find("div"     ,attrs={"class": "gs_fl"}					).find_all("a"					)
						return anchors[2].get_text()


if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[int]			=     {
			    """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))


