




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


_lowerCAmelCase						:   List[Any]		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""pixel_values"""]
      def __init__(			self   :Tuple ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Dict[str, int] = None ,      lowerCamelCase   :PILImageResampling = PILImageResampling.BICUBIC ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Union[int, float] = 1 / 255 ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :bool = True ,      **lowerCamelCase   :Dict ,      )     ->			None:
       super().__init__(**lowerCamelCase      )
       UpperCAmelCase__							 =						size if size is not None else {"height": 384, "width": 384}
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=lowerCamelCase      )

       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						resample
       UpperCAmelCase__							 =						do_rescale
       UpperCAmelCase__							 =						rescale_factor
       UpperCAmelCase__							 =						do_normalize
       UpperCAmelCase__							 =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       UpperCAmelCase__							 =						image_std if image_std is not None else OPENAI_CLIP_STD
       UpperCAmelCase__							 =						do_convert_rgb
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Dict[str, int] ,      lowerCamelCase   :PILImageResampling = PILImageResampling.BICUBIC ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :Dict ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=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()}'''      )
       UpperCAmelCase__							 =						(size["height"], size["width"])
       return resize(lowerCamelCase ,      size=lowerCamelCase ,      resample=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Union[int, float] ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :int ,      )     ->			Optional[int]:
       return rescale(lowerCamelCase ,      scale=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Union[float, List[float]] ,      lowerCamelCase   :Union[float, List[float]] ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			np.ndarray:
       return normalize(lowerCamelCase ,      mean=lowerCamelCase ,      std=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :ImageInput ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[Dict[str, int]] = None ,      lowerCamelCase   :PILImageResampling = None ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[float] = None ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :bool = None ,      lowerCamelCase   :ChannelDimension = ChannelDimension.FIRST ,      **lowerCamelCase   :List[Any] ,      )     ->			PIL.Image.Image:
       UpperCAmelCase__							 =						do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase__							 =						resample if resample is not None else self.resample
       UpperCAmelCase__							 =						do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase__							 =						rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase__							 =						do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase__							 =						image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase__							 =						image_std if image_std is not None else self.image_std
       UpperCAmelCase__							 =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       UpperCAmelCase__							 =						size if size is not None else self.size
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=lowerCamelCase      )

       UpperCAmelCase__							 =						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 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:
        UpperCAmelCase__							 =						[convert_to_rgb(lowerCamelCase      ) for image in images]

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

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

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

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

       UpperCAmelCase__							 =						[to_channel_dimension_format(lowerCamelCase ,      lowerCamelCase      ) for image in images]

       UpperCAmelCase__							 =						BatchFeature(data={"pixel_values": images} ,      tensor_type=lowerCamelCase      )

       return encoded_outputs











from __future__ import annotations



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int = 4	):




 """simple docstring"""







 UpperCAmelCase__							 =						abs(_lowerCAmelCase	) or 4
 return [[1 + x + y * row_size for x in range(_lowerCAmelCase	)] for y in range(_lowerCAmelCase	)]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 return reverse_row(transpose(_lowerCAmelCase	)	)
 # OR.. transpose(reverse_column(matrix))



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 return reverse_row(reverse_column(_lowerCAmelCase	)	)
 # OR.. reverse_column(reverse_row(matrix))



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 return reverse_column(transpose(_lowerCAmelCase	)	)
 # OR.. transpose(reverse_row(matrix))



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 UpperCAmelCase__							 =						[list(_lowerCAmelCase	) for x in zip(*_lowerCAmelCase	)]
 return matrix



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 UpperCAmelCase__							 =						matrix[::-1]
 return matrix



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 UpperCAmelCase__							 =						[x[::-1] for x in matrix]
 return matrix



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 for i in matrix:
  print(*_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Optional[int]		=   make_matrix()
  print("\norigin:\n")
  print_matrix(matrix)
  print("\nrotate 90 counterclockwise:\n")
  print_matrix(rotate_aa(matrix))

  _lowerCAmelCase						:   Union[str, Any]		=   make_matrix()
  print("\norigin:\n")
  print_matrix(matrix)
  print("\nrotate 180:\n")
  print_matrix(rotate_aaa(matrix))

  _lowerCAmelCase						:   Optional[int]		=   make_matrix()
  print("\norigin:\n")
  print_matrix(matrix)
  print("\nrotate 270 counterclockwise:\n")
  print_matrix(rotate_aaa(matrix))





