




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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_lowerCamelCase	:		List[Any]							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[List[ImageInput]]:



       """simple docstring"""

       if isinstance(lowercase_ ,				(list, tuple)						) and isinstance(videos[0] ,				(list, tuple)						) and is_valid_image(videos[0][0]						):
              return videos

       elif isinstance(lowercase_ ,				(list, tuple)						) and is_valid_image(videos[0]						):
              return [videos]

       elif is_valid_image(lowercase_						):
              return [[videos]]

       raise ValueError(f"""Could not make batched video from {videos}"""						)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''pixel_values''']


    def __init__(			self      :	Any					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	PILImageResampling = PILImageResampling.BILINEAR					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Union[int, float] = 1 / 255					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, **UpperCAmelCase__      :	Tuple					, )						->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__       =    size if size is not None else {'''shortest_edge''': 224}
           A__       =    get_size_dict(UpperCAmelCase__					, default_to_square=UpperCAmelCase__)
           A__       =    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A__       =    get_size_dict(UpperCAmelCase__					, param_name='''crop_size''')

           A__       =    do_resize
           A__       =    size
           A__       =    do_center_crop
           A__       =    crop_size
           A__       =    resample
           A__       =    do_rescale
           A__       =    rescale_factor
           A__       =    do_normalize
           A__       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A__       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


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





           '''simple docstring'''

           A__       =    get_size_dict(UpperCAmelCase__					, default_to_square=UpperCAmelCase__)
           if "shortest_edge" in size:
                  A__       =    get_resize_output_image_size(UpperCAmelCase__					, size['''shortest_edge''']					, default_to_square=UpperCAmelCase__)
           elif "height" in size and "width" in size:
                  A__       =    (size['''height'''], size['''width'''])
           else:
                  raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
           return resize(UpperCAmelCase__					, size=UpperCAmelCase__					, resample=UpperCAmelCase__					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Dict[str, int]					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	Union[str, Any]					, )						->np.ndarray:





           '''simple docstring'''

           A__       =    get_size_dict(UpperCAmelCase__)
           if "height" not in size or "width" not in size:
                  raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""")
           return center_crop(UpperCAmelCase__					, size=(size['''height'''], size['''width'''])					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Union[int, float]					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	Optional[Any]					, )						->Union[str, Any]:





           '''simple docstring'''

           return rescale(UpperCAmelCase__					, scale=UpperCAmelCase__					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Union[float, List[float]]					, UpperCAmelCase__      :	Union[float, List[float]]					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	List[Any]					, )						->np.ndarray:





           '''simple docstring'''

           return normalize(UpperCAmelCase__					, mean=UpperCAmelCase__					, std=UpperCAmelCase__					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, UpperCAmelCase__      :	ImageInput					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	PILImageResampling = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	float = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[ChannelDimension] = ChannelDimension.FIRST					, )						->np.ndarray:





           '''simple docstring'''

           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_size is None:
                  raise ValueError('''Crop size 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.
           A__       =    to_numpy_array(UpperCAmelCase__)

           if do_resize:
                  A__       =    self.resize(image=UpperCAmelCase__					, size=UpperCAmelCase__					, resample=UpperCAmelCase__)

           if do_center_crop:
                  A__       =    self.center_crop(UpperCAmelCase__					, size=UpperCAmelCase__)

           if do_rescale:
                  A__       =    self.rescale(image=UpperCAmelCase__					, scale=UpperCAmelCase__)

           if do_normalize:
                  A__       =    self.normalize(image=UpperCAmelCase__					, mean=UpperCAmelCase__					, std=UpperCAmelCase__)

           A__       =    to_channel_dimension_format(UpperCAmelCase__					, UpperCAmelCase__)
           return image





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	ImageInput					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	PILImageResampling = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	float = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	ChannelDimension = ChannelDimension.FIRST					, **UpperCAmelCase__      :	Optional[Any]					, )						->PIL.Image.Image:





           '''simple docstring'''

           A__       =    do_resize if do_resize is not None else self.do_resize
           A__       =    resample if resample is not None else self.resample
           A__       =    do_center_crop if do_center_crop is not None else self.do_center_crop
           A__       =    do_rescale if do_rescale is not None else self.do_rescale
           A__       =    rescale_factor if rescale_factor is not None else self.rescale_factor
           A__       =    do_normalize if do_normalize is not None else self.do_normalize
           A__       =    image_mean if image_mean is not None else self.image_mean
           A__       =    image_std if image_std is not None else self.image_std

           A__       =    size if size is not None else self.size
           A__       =    get_size_dict(UpperCAmelCase__					, default_to_square=UpperCAmelCase__)
           A__       =    crop_size if crop_size is not None else self.crop_size
           A__       =    get_size_dict(UpperCAmelCase__					, param_name='''crop_size''')

           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.''')

           A__       =    make_batched(UpperCAmelCase__)

           A__       =    [
               [
                   self._preprocess_image(
                       image=UpperCAmelCase__					, do_resize=UpperCAmelCase__					, size=UpperCAmelCase__					, resample=UpperCAmelCase__					, do_center_crop=UpperCAmelCase__					, crop_size=UpperCAmelCase__					, do_rescale=UpperCAmelCase__					, rescale_factor=UpperCAmelCase__					, do_normalize=UpperCAmelCase__					, image_mean=UpperCAmelCase__					, image_std=UpperCAmelCase__					, data_format=UpperCAmelCase__					, )
                   for img in video
               ]
               for video in videos
           ]

           A__       =    {'''pixel_values''': videos}
           return BatchFeature(data=UpperCAmelCase__					, tensor_type=UpperCAmelCase__)








import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """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))


