



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


a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     List[List[ImageInput]]:
 if isinstance(SCREAMING_SNAKE_CASE__				,       (list, tuple)				) and isinstance(videos[0]				,       (list, tuple)				) and is_valid_image(videos[0][0]				):
  return videos

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

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

 raise ValueError(F'''Could not make batched video from {videos}'''				)







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		["""pixel_values"""]

   def __init__(					self							:			Union[str, Any]				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = PILImageResampling.BILINEAR				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Dict[str, int] = None				,		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							:			str				,		)				->				None:

    """simple docstring"""







    super().__init__(**lowerCAmelCase)
    _snake_case    :  Tuple					=					size if size is not None else {"""shortest_edge""": 224}
    _snake_case    :  Optional[int]					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    _snake_case    :  Optional[int]					=					get_size_dict(lowerCAmelCase				,		param_name="""crop_size""")

    _snake_case    :  List[str]					=					do_resize
    _snake_case    :  List[Any]					=					size
    _snake_case    :  str					=					do_center_crop
    _snake_case    :  Dict					=					crop_size
    _snake_case    :  Optional[int]					=					resample
    _snake_case    :  Tuple					=					do_rescale
    _snake_case    :  Optional[Any]					=					rescale_factor
    _snake_case    :  Any					=					do_normalize
    _snake_case    :  List[Any]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _snake_case    :  Tuple					=					image_std if image_std is not None else IMAGENET_STANDARD_STD

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

    """simple docstring"""







    _snake_case    :  Any					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
    if "shortest_edge" in size:
     _snake_case    :  List[str]					=					get_resize_output_image_size(lowerCAmelCase				,		size["""shortest_edge"""]				,		default_to_square=lowerCAmelCase)
    elif "height" in size and "width" in size:
     _snake_case    :  Dict					=					(size["""height"""], size["""width"""])
    else:
     raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
    return resize(lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

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

    """simple docstring"""







    _snake_case    :  int					=					get_size_dict(lowerCAmelCase)
    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(lowerCAmelCase				,		size=(size["""height"""], size["""width"""])				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

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

    """simple docstring"""







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

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

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Dict				,		lowerCAmelCase							:			ImageInput				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = 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[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.
    _snake_case    :  Tuple					=					to_numpy_array(lowerCAmelCase)

    if do_resize:
     _snake_case    :  Any					=					self.resize(image=lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase)

    if do_center_crop:
     _snake_case    :  List[str]					=					self.center_crop(lowerCAmelCase				,		size=lowerCAmelCase)

    if do_rescale:
     _snake_case    :  str					=					self.rescale(image=lowerCAmelCase				,		scale=lowerCAmelCase)

    if do_normalize:
     _snake_case    :  List[str]					=					self.normalize(image=lowerCAmelCase				,		mean=lowerCAmelCase				,		std=lowerCAmelCase)

    _snake_case    :  Tuple					=					to_channel_dimension_format(lowerCAmelCase				,		lowerCAmelCase)
    return image


   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			ImageInput				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = 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							:			List[Any]				,		)				->				PIL.Image.Image:

    """simple docstring"""







    _snake_case    :  Any					=					do_resize if do_resize is not None else self.do_resize
    _snake_case    :  Any					=					resample if resample is not None else self.resample
    _snake_case    :  Optional[int]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
    _snake_case    :  Dict					=					do_rescale if do_rescale is not None else self.do_rescale
    _snake_case    :  Tuple					=					rescale_factor if rescale_factor is not None else self.rescale_factor
    _snake_case    :  List[str]					=					do_normalize if do_normalize is not None else self.do_normalize
    _snake_case    :  List[Any]					=					image_mean if image_mean is not None else self.image_mean
    _snake_case    :  Dict					=					image_std if image_std is not None else self.image_std

    _snake_case    :  Any					=					size if size is not None else self.size
    _snake_case    :  Tuple					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
    _snake_case    :  str					=					crop_size if crop_size is not None else self.crop_size
    _snake_case    :  Union[str, Any]					=					get_size_dict(lowerCAmelCase				,		param_name="""crop_size""")

    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.""")

    _snake_case    :  Optional[Any]					=					make_batched(lowerCAmelCase)

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

    _snake_case    :  int					=					{"""pixel_values""": videos}
    return BatchFeature(data=lowerCAmelCase				,		tensor_type=lowerCAmelCase)





def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000				)  ->     int:
 _snake_case	,    _snake_case    :  str					=					1, 1
 _snake_case    :  List[Any]					=					2
 while True:
  _snake_case    :  Union[str, Any]					=					0
  _snake_case    :  int					=					fa + fa
  _snake_case	,    _snake_case    :  Union[str, Any]					=					fa, f
  index += 1
  for _ in str(SCREAMING_SNAKE_CASE__				):
   i += 1
  if i == n:
   break
 return index


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))
