





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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


_a 						=      logging.get_logger(__name__)






class 					__A    (    UpperCAmelCase__ ):

			'''simple docstring'''







			lowerCAmelCase_							     =						["""pixel_values"""]





			def __init__(    self       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = 0.9       ,  __lowerCAmelCase = PILImageResampling.BICUBIC       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = 1 / 2_5_5       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(**snake_case_							)
				lowerCamelCase__								=		size if size is not None else {'shortest_edge': 2_2_4}
				lowerCamelCase__								=		get_size_dict(snake_case_       ,  default_to_square=snake_case_							)
				lowerCamelCase__								=		crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
				lowerCamelCase__								=		get_size_dict(snake_case_       ,  param_name='''crop_size'''							)

				lowerCamelCase__								=		do_resize
				lowerCamelCase__								=		size
				lowerCamelCase__								=		crop_pct
				lowerCamelCase__								=		resample
				lowerCamelCase__								=		do_center_crop
				lowerCamelCase__								=		crop_size
				lowerCamelCase__								=		do_rescale
				lowerCamelCase__								=		rescale_factor
				lowerCamelCase__								=		do_normalize
				lowerCamelCase__								=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				lowerCamelCase__								=		image_std if image_std is not None else IMAGENET_DEFAULT_STD





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = PILImageResampling.BICUBIC       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		get_size_dict(snake_case_       ,  default_to_square=snake_case_							)
				if "shortest_edge" not in size and ("height" not in size or "width" not in size):
					raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'							)
				if crop_pct is not None:
					if "shortest_edge" in size:
						lowerCamelCase__								=		int(size['''shortest_edge'''] / crop_pct							)
					elif "height" in size and "width" in size:
						if size["height"] == size["width"]:
							lowerCamelCase__								=		int(size['''height'''] / crop_pct							)
						else:
							lowerCamelCase__								=		(int(size['''height'''] / crop_pct							), int(size['''width'''] / crop_pct							))
					else:
						raise ValueError('''Invalid size for resize: {}'''.format(snake_case_							)							)

					lowerCamelCase__								=		get_resize_output_image_size(snake_case_       ,  size=snake_case_       ,  default_to_square=snake_case_							)
				else:
					if "shortest_edge" in size:
						lowerCamelCase__								=		get_resize_output_image_size(snake_case_       ,  size=size['''shortest_edge''']       ,  default_to_square=snake_case_							)
					elif "height" in size and "width" in size:
						lowerCamelCase__								=		(size['height'], size['width'])
					else:
						raise ValueError('''Invalid size for resize: {}'''.format(snake_case_							)							)

				return resize(snake_case_       ,  size=snake_case_       ,  resample=snake_case_       ,  data_format=snake_case_       ,  **snake_case_							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		get_size_dict(snake_case_							)
				if "height" not in size or "width" not in size:
					raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'							)
				return center_crop(snake_case_       ,  size=(size['''height'''], size['''width'''])       ,  data_format=snake_case_       ,  **snake_case_							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = ChannelDimension.FIRST       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







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

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

				lowerCamelCase__								=		make_list_of_images(snake_case_							)

				if not valid_images(snake_case_							):
					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_center_crop and crop_pct is None:
					raise ValueError('''Crop_pct 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.
				lowerCamelCase__								=		[to_numpy_array(snake_case_							) for image in images]

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

				if do_center_crop:
					lowerCamelCase__								=		[self.center_crop(image=snake_case_       ,  size=snake_case_							) for image in images]

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

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

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

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










"""simple docstring"""




import os





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Any										=  os.path.join(os.path.dirname(_UpperCAmelCase					) ,					'num.txt'					)
  with open(_UpperCAmelCase					) as file_hand:
    return str(sum(int(_UpperCAmelCase					) for line in file_hand					)					)[:10]


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