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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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   :     Union[str, Any]       =				logging.get_logger(__name__)


class      lowerCamelCase    (__lowerCamelCase    ):
  """simple docstring"""







  lowerCamelCase__   =  ["pixel_values"]







  def __init__(							self       :				Tuple     ,   __magic_name__       :				Dict = True     ,   __magic_name__       :				int = None     ,   __magic_name__       :				Optional[Any] = PILImageResampling.BICUBIC     ,   __magic_name__       :				Dict = True     ,   __magic_name__       :				str = 1 / 255     ,   __magic_name__       :				Any = True     ,   __magic_name__       :				int = None     ,   __magic_name__       :				str = None     ,   __magic_name__       :				List[str] = True     ,   **__magic_name__       :				Dict     ,   )     ->		None:
        super().__init__(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     size if size is not None else {'''height''': 384, '''width''': 384}
        SCREAMING_SNAKE_CASE_      =     get_size_dict(__magic_name__     ,   default_to_square=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     do_resize
        SCREAMING_SNAKE_CASE_      =     size
        SCREAMING_SNAKE_CASE_      =     resample
        SCREAMING_SNAKE_CASE_      =     do_rescale
        SCREAMING_SNAKE_CASE_      =     rescale_factor
        SCREAMING_SNAKE_CASE_      =     do_normalize
        SCREAMING_SNAKE_CASE_      =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        SCREAMING_SNAKE_CASE_      =     image_std if image_std is not None else OPENAI_CLIP_STD
        SCREAMING_SNAKE_CASE_      =     do_convert_rgb







  def       __A   (							self       :				Dict     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Optional[Any] = PILImageResampling.BICUBIC     ,   __magic_name__       :				List[str] = None     ,   **__magic_name__       :				Optional[int]     ,   )     ->		np.ndarray:
        SCREAMING_SNAKE_CASE_      =     get_size_dict(__magic_name__     ,   default_to_square=__magic_name__      )
        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()}'''      )
        SCREAMING_SNAKE_CASE_      =     (size['''height'''], size['''width'''])
        return resize(__magic_name__     ,   size=__magic_name__     ,   resample=__magic_name__     ,   data_format=__magic_name__     ,   **__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Any = None     ,   **__magic_name__       :				List[Any]     ,   )     ->		List[Any]:
        return rescale(__magic_name__     ,   scale=__magic_name__     ,   data_format=__magic_name__     ,   **__magic_name__      )







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				str     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Any = None     ,   **__magic_name__       :				Union[str, Any]     ,   )     ->		np.ndarray:
        return normalize(__magic_name__     ,   mean=__magic_name__     ,   std=__magic_name__     ,   data_format=__magic_name__     ,   **__magic_name__      )







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				str     ,   __magic_name__       :				int = None     ,   __magic_name__       :				Optional[Any] = None     ,   __magic_name__       :				Optional[Any] = None     ,   __magic_name__       :				Dict = None     ,   __magic_name__       :				Tuple = None     ,   __magic_name__       :				List[str] = None     ,   __magic_name__       :				List[Any] = None     ,   __magic_name__       :				Dict = None     ,   __magic_name__       :				Union[str, Any] = None     ,   __magic_name__       :				int = None     ,   __magic_name__       :				int = ChannelDimension.FIRST     ,   **__magic_name__       :				Union[str, Any]     ,   )     ->		PIL.Image.Image:
        SCREAMING_SNAKE_CASE_      =     do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE_      =     resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE_      =     do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE_      =     rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE_      =     do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE_      =     image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE_      =     image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE_      =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        SCREAMING_SNAKE_CASE_      =     size if size is not None else self.size
        SCREAMING_SNAKE_CASE_      =     get_size_dict(__magic_name__     ,   default_to_square=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     make_list_of_images(__magic_name__      )

        if not valid_images(__magic_name__      ):
              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_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."      )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              SCREAMING_SNAKE_CASE_      =     [convert_to_rgb(__magic_name__      ) for image in images]

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

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

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

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

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

        SCREAMING_SNAKE_CASE_      =     BatchFeature(data={"pixel_values": images}     ,   tensor_type=__magic_name__      )

        return encoded_outputs









from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	list[list[int]]:
    UpperCamelCase__							:				list[list[int]]          =      []
    create_all_state(1		,   __UpperCAmelCase		,   __UpperCAmelCase		,   []		,   __UpperCAmelCase      )
    return result




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			list[list[int]]		,   )      ->	None:
    if level == 0:
        total_list.append(current_list[:]      )
        return

    for i in range(__UpperCAmelCase		,   total_number - level + 2      ):
        current_list.append(__UpperCAmelCase      )
        create_all_state(i + 1		,   __UpperCAmelCase		,   level - 1		,   __UpperCAmelCase		,   __UpperCAmelCase      )
        current_list.pop()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[list[int]]      )      ->	None:
    for i in total_list:
        print(*__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 4
  UpperCAmelCase_     							= 2
  UpperCAmelCase_     							= generate_all_combinations(n, k)
  print_all_state(total_list)

