"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowerCAmelCase__  :  List[str]						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["pixel_values"]







					def __init__(				self   :	List[Any]    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[int, float] = 1 / 255    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									super().__init__(**lowerCamelCase__			)
									UpperCAmelCase__             =							size if size is not None else {'shortest_edge': 384}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)

									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									# Default value set here for backwards compatibility where the value in config is None
									UpperCAmelCase__             =							crop_pct if crop_pct is not None else 224 / 256
									UpperCAmelCase__             =							resample
									UpperCAmelCase__             =							do_rescale
									UpperCAmelCase__             =							rescale_factor
									UpperCAmelCase__             =							do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCAmelCase__             =							image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	float    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BICUBIC    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									if "shortest_edge" not in size:
													raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''			)
									UpperCAmelCase__             =							size['shortest_edge']

									if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													UpperCAmelCase__             =							int(shortest_edge / crop_pct			)
													UpperCAmelCase__             =							get_resize_output_image_size(lowerCamelCase__    ,size=lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
													UpperCAmelCase__             =							resize(image=lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=lowerCamelCase__    ,size=(shortest_edge, shortest_edge)    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)
									else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    lowerCamelCase__    ,size=(shortest_edge, shortest_edge)    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







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







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[float, List[float]]    ,lowerCamelCase__   :	Union[float, List[float]]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	List[str]    ,):
									return normalize(lowerCamelCase__    ,mean=lowerCamelCase__    ,std=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	PILImageResampling = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	ChannelDimension = ChannelDimension.FIRST    ,**lowerCamelCase__   :	Any    ,):
									UpperCAmelCase__             =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__             =							crop_pct if crop_pct is not None else self.crop_pct
									UpperCAmelCase__             =							resample if resample is not None else self.resample
									UpperCAmelCase__             =							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__             =							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__             =							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__             =							image_std if image_std is not None else self.image_std

									UpperCAmelCase__             =							size if size is not None else self.size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)

									UpperCAmelCase__             =							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 or resample is None:
													raise ValueError('Size and resample must be specified if do_resize is True.'			)

									if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError('crop_pct must be specified if size < 384.'			)

									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.
									UpperCAmelCase__             =							[to_numpy_array(lowerCamelCase__			) for image in images]

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

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

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

									UpperCAmelCase__             =							[to_channel_dimension_format(lowerCamelCase__    ,lowerCamelCase__			) for image in images]

									UpperCAmelCase__             =							{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__    ,tensor_type=lowerCamelCase__			)




"""simple docstring"""






import requests
from bsa import BeautifulSoup



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BeautifulSoup(requests.get(lowerCamelCase					,			params=lowerCamelCase       ).content					,			'html.parser'       )
				UpperCAmelCase__             =							soup.find('div'					,			attrs={'class': 'gs_ri'}       )
				UpperCAmelCase__             =							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': 2_018,
						    'hl': 'en',
						}
						print(get_citation('https://scholar.google.com/scholar_lookup', params=params))



