






"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
  import PIL




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  int						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  bool = True						,	**lowercase_     :  List[Any]						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		size if size is not None else {'''shortest_edge''': 224}
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_      :  int    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_						,	param_name='''crop_size''')

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		do_resize
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		size
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		resample
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		do_center_crop
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		crop_size
          SCREAMING_SNAKE_CASE_      :  str    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		do_normalize
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_std if image_std is not None else OPENAI_CLIP_STD
          SCREAMING_SNAKE_CASE_      :  Any    						=		do_convert_rgb
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          if "shortest_edge" not in size:
              raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          SCREAMING_SNAKE_CASE_      :  Any    						=		get_resize_output_image_size(lowercase_						,	size=size['''shortest_edge''']						,	default_to_square=lowercase_)
          return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Union[str, Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		get_size_dict(lowercase_)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}')
          return center_crop(lowercase_						,	size=(size['''height'''], size['''width'''])						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[int, float]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


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




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  ImageInput						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  int = None						,	lowercase_     :  bool = None						,	lowercase_     :  float = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	lowercase_     :  Optional[ChannelDimension] = ChannelDimension.FIRST						,	**lowercase_     :  str						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_      :  int    						=		size if size is not None else self.size
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_size_dict(lowercase_						,	param_name='''size'''						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_      :  Any    						=		do_center_crop if do_center_crop is not None else self.do_center_crop
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_size_dict(lowercase_						,	param_name='''crop_size'''						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_std if image_std is not None else self.image_std
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          SCREAMING_SNAKE_CASE_      :  int    						=		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.''')

          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.''')

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[convert_to_rgb(lowercase_) for image in images]

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[to_numpy_array(lowercase_) for image in images]

          if do_resize:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		[self.resize(image=lowercase_						,	size=lowercase_						,	resample=lowercase_) for image in images]

          if do_center_crop:
              SCREAMING_SNAKE_CASE_      :  str    						=		[self.center_crop(image=lowercase_						,	size=lowercase_) for image in images]

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		[self.rescale(image=lowercase_						,	scale=lowercase_) for image in images]

          if do_normalize:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		[self.normalize(image=lowercase_						,	mean=lowercase_						,	std=lowercase_) for image in images]

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[to_channel_dimension_format(lowercase_						,	lowercase_) for image in images]

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{'''pixel_values''': images}
          return BatchFeature(data=lowercase_						,	tensor_type=lowercase_)










"""simple docstring"""






from math import factorial



def 					_A			(__a = 20							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
    # 2, 3,...
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		n // 2

    return int(factorial(__a							) / (factorial(__a							) * factorial(n - k							))							)


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution(20))
  else:
    try:
      UpperCAmelCase_   : List[str]   			= int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number.""")


