





'''simple docstring'''






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__				:   Tuple      =logging.get_logger(__name__)




class  UpperCAmelCase     (			a__   ):
      _lowercase:			Tuple						     =   ["""pixel_values"""]




      def __init__(			self			: Tuple			,			__snake_case			: Dict = True			,			__snake_case			: List[Any] = None			,			__snake_case			: Dict = PILImageResampling.BILINEAR			,			__snake_case			: Optional[Any] = True			,			__snake_case			: Any = 1 / 2_55			,			__snake_case			: Optional[Any] = True			,			__snake_case			: int = None			,			__snake_case			: Any = True			,			**__snake_case			: Dict			,			)				->      None:
            super().__init__(**lowerCAmelCase__  )
            _lowerCAmelCase							     =							size if size is not None else {"shortest_edge": 2_24}
            _lowerCAmelCase							     =							get_size_dict(lowerCAmelCase__			,			default_to_square=lowerCAmelCase__  )
            _lowerCAmelCase							     =							crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
            _lowerCAmelCase							     =							get_size_dict(lowerCAmelCase__			,			param_name="""crop_size"""  )

            _lowerCAmelCase							     =							do_resize
            _lowerCAmelCase							     =							size
            _lowerCAmelCase							     =							resample
            _lowerCAmelCase							     =							do_rescale
            _lowerCAmelCase							     =							rescale_factor
            _lowerCAmelCase							     =							do_center_crop
            _lowerCAmelCase							     =							crop_size
            _lowerCAmelCase							     =							do_flip_channel_order




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: int			,			__snake_case			: List[Any]			,			__snake_case			: Optional[Any] = PIL.Image.BILINEAR			,			__snake_case			: Dict = None			,			**__snake_case			: Union[str, Any]			,			)				->      np.ndarray:
            _lowerCAmelCase							     =							get_size_dict(lowerCAmelCase__			,			default_to_square=lowerCAmelCase__  )
            if "shortest_edge" not in size:
                  raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"  )
            _lowerCAmelCase							     =							get_resize_output_image_size(lowerCAmelCase__			,			size=size["""shortest_edge"""]			,			default_to_square=lowerCAmelCase__  )
            return resize(lowerCAmelCase__			,			size=lowerCAmelCase__			,			resample=lowerCAmelCase__			,			data_format=lowerCAmelCase__			,			**lowerCAmelCase__  )




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: Optional[int]			,			__snake_case			: Optional[Any]			,			__snake_case			: Optional[int] = None			,			**__snake_case			: Optional[int]			,			)				->      np.ndarray:
            _lowerCAmelCase							     =							get_size_dict(lowerCAmelCase__  )
            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(lowerCAmelCase__			,			size=(size["""height"""], size["""width"""])			,			data_format=lowerCAmelCase__			,			**lowerCAmelCase__  )




      def 				lowercase__    (			self			: Dict			,			__snake_case			: Optional[int]			,			__snake_case			: List[Any]			,			__snake_case			: Any = None			,			**__snake_case			: Tuple			,			)				->      Optional[Any]:
            return rescale(lowerCAmelCase__			,			scale=lowerCAmelCase__			,			data_format=lowerCAmelCase__			,			**lowerCAmelCase__  )




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: Union[str, Any]			,			__snake_case			: Tuple = None  )				->      np.ndarray:
            return flip_channel_order(lowerCAmelCase__			,			data_format=lowerCAmelCase__  )




      def 				lowercase__    (			self			: List[str]			,			__snake_case			: Any			,			__snake_case			: Union[str, Any] = None			,			__snake_case			: Tuple = None			,			__snake_case			: List[str] = None			,			__snake_case			: str = None			,			__snake_case			: Dict = None			,			__snake_case			: List[Any] = None			,			__snake_case			: int = None			,			__snake_case			: Dict = None			,			__snake_case			: Union[str, Any] = None			,			__snake_case			: Dict = ChannelDimension.FIRST			,			**__snake_case			: Dict			,			)				->      PIL.Image.Image:
            _lowerCAmelCase							     =							do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase							     =							resample if resample is not None else self.resample
            _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_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase							     =							(
                do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
            )

            _lowerCAmelCase							     =							size if size is not None else self.size
            _lowerCAmelCase							     =							get_size_dict(lowerCAmelCase__			,			default_to_square=lowerCAmelCase__  )
            _lowerCAmelCase							     =							crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase							     =							get_size_dict(lowerCAmelCase__			,			param_name="""crop_size"""  )

            _lowerCAmelCase							     =							make_list_of_images(lowerCAmelCase__  )

            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."""  )

            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.
            _lowerCAmelCase							     =							[to_numpy_array(lowerCAmelCase__  ) for image in images]

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

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

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

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

            _lowerCAmelCase							     =							[to_channel_dimension_format(lowerCAmelCase__			,			lowerCAmelCase__  ) for image in images]

            _lowerCAmelCase							     =							{"pixel_values": images}
            return BatchFeature(data=lowerCAmelCase__			,			tensor_type=lowerCAmelCase__  )




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Any			,			__snake_case			: Dict = None  )				->      Tuple:
            _lowerCAmelCase							     =							outputs.logits

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

                  if is_torch_tensor(lowerCAmelCase__  ):
                        _lowerCAmelCase							     =							target_sizes.numpy()

                  _lowerCAmelCase							     =							[]

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

            return semantic_segmentation













"""simple docstring"""




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list					) ->int:






	'''simple docstring'''






	if not grid or not grid[0]:
		raise TypeError("The grid does not contain the appropriate information"					)

	for cell_n in range(1    ,       len(grid[0]					)					):
		grid[0][cell_n] += grid[0][cell_n - 1]
	a      :		Union[str, Any]	=				grid[0]

	for row_n in range(1    ,       len(_lowercase					)					):
		a      :		Optional[Any]	=				grid[row_n]
		a      :		Tuple	=				fill_row(_lowercase    ,       _lowercase					)
		a      :		List[Any]	=				grid[row_n]

	return grid[-1][-1]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    list					) ->list:






	'''simple docstring'''






	current_row[0] += row_above[0]
	for cell_n in range(1    ,       len(_lowercase					)					):
		current_row[cell_n] += min(current_row[cell_n - 1]    ,       row_above[cell_n]					)

	return current_row


if __name__ == "__main__":
		import doctest

		doctest.testmod()


