
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[int]     =	["pixel_values"]





     def __init__(self   :					Union[str, Any]       ,					_A   :					bool = True       ,					_A   :					Union[int, float] = 1 / 2_5_5       ,					_A   :					bool = True       ,					_A   :					int = 8       ,					**_A   :					Dict       ,					)		->		None:
         super().__init__(**_A     )

         snake_case				  =      do_rescale
         snake_case				  =      rescale_factor
         snake_case				  =      do_pad
         snake_case				  =      pad_size





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





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					np.ndarray       ,					_A   :					int       ,					_A   :					Optional[Union[str, ChannelDimension]] = None     )		->		str:
         snake_case  ,							snake_case				  =      get_image_size(_A     )
         snake_case				  =      (old_height // size + 1) * size - old_height
         snake_case				  =      (old_width // size + 1) * size - old_width

         return pad(_A       ,					((0, pad_height), (0, pad_width))       ,					mode="symmetric"       ,					data_format=_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					ImageInput       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[float] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					_A   :					Union[str, ChannelDimension] = ChannelDimension.FIRST       ,					**_A   :					Optional[Any]       ,					)		->		List[Any]:
         snake_case				  =      do_rescale if do_rescale is not None else self.do_rescale
         snake_case				  =      rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case				  =      do_pad if do_pad is not None else self.do_pad
         snake_case				  =      pad_size if pad_size is not None else self.pad_size

         snake_case				  =      make_list_of_images(_A     )

         if not valid_images(_A     ):
             raise ValueError(
                 "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                 "torch.Tensor, tf.Tensor or jax.ndarray."     )

         if do_rescale and rescale_factor is None:
             raise ValueError("Rescale factor must be specified if do_rescale is True."     )

         # All transformations expect numpy arrays.
         snake_case				  =      [to_numpy_array(_A     ) for image in images]

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

         if do_pad:
             snake_case				  =      [self.pad(_A       ,					size=_A     ) for image in images]

         snake_case				  =      [to_channel_dimension_format(_A       ,					_A     ) for image in images]

         snake_case				  =      {"pixel_values": images}
         return BatchFeature(data=_A       ,					tensor_type=_A     )



import os

# Precomputes a list of the 100 first triangular numbers
_A						      =				[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      os.path.dirname(os.path.realpath(A__	)	)
    snake_case				  =      os.path.join(A__				,	"words.txt"	)

    snake_case				  =      ""
    with open(A__	) as f:
        snake_case				  =      f.readline()

    snake_case				  =      [word.strip("\""	) for word in words.strip("\r\n"	).split(","	)]
    snake_case				  =      [
        word
        for word in [sum(ord(A__	) - 64 for x in word	) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(A__	)


if __name__ == "__main__":
   print(solution())

