
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:int         =	['pixel_values']
	def __init__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = PILImageResampling.BILINEAR       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			Union[int, float] = 1 / 2_5_5       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							**SCREAMING_SNAKE_CASE_  :			Dict       ,							)       ->     None:
					super().__init__(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					size if size is not None else {'''shortest_edge''': 2_2_4}
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       ,							default_to_square=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       ,							param_name='''crop_size'''       )

					lowercase_       =					do_resize
					lowercase_       =					size
					lowercase_       =					resample
					lowercase_       =					do_rescale
					lowercase_       =					rescale_factor
					lowercase_       =					do_center_crop
					lowercase_       =					crop_size
					lowercase_       =					do_flip_channel_order
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			np.ndarray       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int]       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = PIL.Image.BILINEAR       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, ChannelDimension]] = None       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     np.ndarray:
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       ,							default_to_square=SCREAMING_SNAKE_CASE_       )
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
					lowercase_       =					get_resize_output_image_size(SCREAMING_SNAKE_CASE_       ,							size=size['''shortest_edge''']       ,							default_to_square=SCREAMING_SNAKE_CASE_       )
					return resize(SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_       ,							resample=SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			np.ndarray       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, ChannelDimension]] = None       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							)       ->     np.ndarray:
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       )
					if "height" not in size or "width" not in size:
									raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
					return center_crop(SCREAMING_SNAKE_CASE_       ,							size=(size['''height'''], size['''width'''])       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			np.ndarray       ,							SCREAMING_SNAKE_CASE_  :			Union[int, float]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, ChannelDimension]] = None       ,							**SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							)       ->     List[Any]:
					return rescale(SCREAMING_SNAKE_CASE_       ,							scale=SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			np.ndarray       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, ChannelDimension]] = None       )       ->     np.ndarray:
					return flip_channel_order(SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			ImageInput       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = None       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			float = None       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, TensorType]] = None       ,							SCREAMING_SNAKE_CASE_  :			ChannelDimension = ChannelDimension.FIRST       ,							**SCREAMING_SNAKE_CASE_  :			Dict       ,							)       ->     PIL.Image.Image:
					lowercase_       =					do_resize if do_resize is not None else self.do_resize
					lowercase_       =					resample if resample is not None else self.resample
					lowercase_       =					do_rescale if do_rescale is not None else self.do_rescale
					lowercase_       =					rescale_factor if rescale_factor is not None else self.rescale_factor
					lowercase_       =					do_center_crop if do_center_crop is not None else self.do_center_crop
					lowercase_       =					(
					    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
					)

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

					lowercase_       =					make_list_of_images(SCREAMING_SNAKE_CASE_       )

					if not valid_images(SCREAMING_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:
									raise ValueError('''Size must be specified if do_resize is True.'''       )

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

					if do_center_crop and crop_size is None:
									raise ValueError('''Crop size must be specified if do_center_crop is True.'''       )

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

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

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

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

					# the pretrained checkpoints assume images are BGR, not RGB
					if do_flip_channel_order:
									lowercase_       =					[self.flip_channel_order(image=SCREAMING_SNAKE_CASE_       ) for image in images]

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

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




	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[Tuple] = None       )       ->     str:
					lowercase_       =					outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
									if len(SCREAMING_SNAKE_CASE_       ) != len(SCREAMING_SNAKE_CASE_       ):
													raise ValueError(
													    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

									if is_torch_tensor(SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					target_sizes.numpy()

									lowercase_       =					[]

									for idx in range(len(SCREAMING_SNAKE_CASE_       )       ):
													lowercase_       =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0       )       ,							size=target_sizes[idx]       ,							mode='''bilinear'''       ,							align_corners=SCREAMING_SNAKE_CASE_       )
													lowercase_       =					resized_logits[0].argmax(dim=0       )
													semantic_segmentation.append(SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					logits.argmax(dim=1       )
									lowercase_       =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

					return semantic_segmentation






import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


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




