



"""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


_lowerCAmelCase    : Tuple							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			['pixel_values']



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

           _UpperCAmelCase      :     Union[str, Any]        =   do_resize
           _UpperCAmelCase      :     Dict        =   size
           _UpperCAmelCase      :     Any        =   resample
           _UpperCAmelCase      :     List[str]        =   do_rescale
           _UpperCAmelCase      :     Optional[int]        =   rescale_factor
           _UpperCAmelCase      :     Tuple        =   do_center_crop
           _UpperCAmelCase      :     Dict        =   crop_size
           _UpperCAmelCase      :     int        =   do_flip_channel_order



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



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Tuple					,     ):
           _UpperCAmelCase      :     Any        =   get_size_dict(A						)
           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(A					,     size=(size["height"], size["width"])					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Dict					,     A					:     np.ndarray					,     A					:     Union[int, float]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     int					,     ):
           return rescale(A					,     scale=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     np.ndarray					,     A					:     Optional[Union[str, ChannelDimension]] = None						):
           return flip_channel_order(A					,     data_format=A						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     ImageInput					,     A					:     bool = None					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = None					,     A					:     bool = None					,     A					:     float = None					,     A					:     bool = None					,     A					:     Dict[str, int] = None					,     A					:     bool = None					,     A					:     Optional[Union[str, TensorType]] = None					,     A					:     ChannelDimension = ChannelDimension.FIRST					,     **A					:     Optional[int]					,     ):
           _UpperCAmelCase      :     List[str]        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     Dict        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     Optional[Any]        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Union[str, Any]        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     List[str]        =   (
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           _UpperCAmelCase      :     List[str]        =   size if size is not None else self.size
           _UpperCAmelCase      :     List[Any]        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     Any        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     Any        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     Optional[int]        =   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_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.
           _UpperCAmelCase      :     List[str]        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     Union[str, Any]        =   [self.resize(image=A					,     size=A					,     resample=A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     Tuple        =   [self.center_crop(image=A					,     size=A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     List[Any]        =   [self.rescale(image=A					,     scale=A						) for image in images]

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

           _UpperCAmelCase      :     Optional[Any]        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     Tuple        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)



      def 						snake_case_ (					self					:     Any					,     A					:     Union[str, Any]					,     A					:     List[Tuple] = None						):
           _UpperCAmelCase      :     Tuple        =   outputs.logits

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

                if is_torch_tensor(A						):
                     _UpperCAmelCase      :     Dict        =   target_sizes.numpy()

                _UpperCAmelCase      :     List[Any]        =   []

                for idx in range(len(A						)						):
                     _UpperCAmelCase      :     Union[str, Any]        =   torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0						)					,     size=target_sizes[idx]					,     mode="bilinear"					,     align_corners=A						)
                     _UpperCAmelCase      :     str        =   resized_logits[0].argmax(dim=0						)
                     semantic_segmentation.append(A						)
           else:
                _UpperCAmelCase      :     Dict        =   logits.argmax(dim=1						)
                _UpperCAmelCase      :     List[str]        =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

           return semantic_segmentation





"""simple docstring"""




from __future__ import annotations

from typing import Any

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]    )  ->       None:





     '''simple docstring'''

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  []						,  0    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       None:





     '''simple docstring'''

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
 _lowerCAmelCase    : list[Any]							=							[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(["A", "B", "C"])
 generate_all_subsequences(seq)
