

'''simple docstring'''

def 				__UpperCamelCase							(	lowercase__						:  list[list[int]] ):






     '''simple docstring'''





     __lowercase					=[list(lowercase__ ) for row in matrix]
     if matrix and isinstance(lowercase__,					lowercase__ ):
          if isinstance(matrix[0],					lowercase__ ):
               __lowercase					=0
               for row in matrix:
                    if prev_len == 0:
                         __lowercase					=len(lowercase__ )
                         __lowercase					=True
                    else:
                         __lowercase					=prev_len == len(lowercase__ )
          else:
               __lowercase					=True
     else:
          __lowercase					=False

     return result

def 				__UpperCamelCase							(	lowercase__						:  list[list[int]] ):






     '''simple docstring'''





     if check_matrix(lowercase__ ) and len(lowercase__ ) > 0:
          __lowercase					=[list(lowercase__ ) for row in a]
          __lowercase					=len(lowercase__ )
          if isinstance(a[0],					lowercase__ ):
               __lowercase					=len(a[0] )
          else:
               for dat in a:
                    print(lowercase__ )
               return

          # horizotal printing increasing
          for i in range(0,					lowercase__ ):
               print(a[0][i] )
          # vertical printing down
          for i in range(1,					lowercase__ ):
               print(a[i][mat_col - 1] )
          # horizotal printing decreasing
          if mat_row > 1:
               for i in range(mat_col - 2,					-1,					-1 ):
                    print(a[mat_row - 1][i] )
        # vertical printing up
          for i in range(mat_row - 2,					0,					-1 ):
               print(a[i][0] )
          __lowercase					=[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
          if len(lowercase__ ) > 0:
               spiral_print_clockwise(lowercase__ )
          else:
               return
     else:
          print('Not a valid matrix' )
          return

def 				__UpperCamelCase							(	lowercase__						:  list[list] ):






     '''simple docstring'''





     if matrix:
          return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*lowercase__ ) )[::-1] )
     else:
          return []


# driver code
if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCAmelCase				   =  [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
      spiral_print_clockwise(a)





'''simple docstring'''

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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


UpperCAmelCase				   =  logging.get_logger(__name__)


class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= ["pixel_values"]







       def __init__(							self			: Dict						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: PILImageResampling = PILImageResampling.BICUBIC						,      __lowercase			: bool = True						,      __lowercase			: Union[int, float] = 1 / 255						,      __lowercase			: bool = True						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      **__lowercase			: Any						,      ):







            """simple docstring"""







            super().__init__(**__lowercase      )
            __lowercase					=crop_size if crop_size is not None else {'height': 256, 'width': 256}
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=size if size is not None else {'height': 224, 'width': 224}
            __lowercase					=get_size_dict(__lowercase      )

            __lowercase					=do_center_crop
            __lowercase					=crop_size
            __lowercase					=do_resize
            __lowercase					=size
            __lowercase					=resample
            __lowercase					=do_rescale
            __lowercase					=rescale_factor
            __lowercase					=do_normalize
            __lowercase					=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __lowercase					=image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def 			snake_case   (							self			: int						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: Optional[int] = None						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[Any]						,      ):







            """simple docstring"""







            __lowercase					=self.size if size is None else size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )

            __lowercase		,     __lowercase					=get_image_size(__lowercase      )
            __lowercase					=min(__lowercase						,      __lowercase      )
            __lowercase					=(size['height'] / crop_size['height']) * min_dim
            __lowercase					=(size['width'] / crop_size['width']) * min_dim
            return center_crop(__lowercase						,      size=(cropped_height, cropped_width)						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: str						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: PILImageResampling = PIL.Image.BICUBIC						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=get_size_dict(__lowercase      )
            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 resize(
                __lowercase						,      size=(size['height'], size['width'])						,      resample=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: np.ndarray						,      __lowercase			: Union[int, float]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Dict						,      ):







            """simple docstring"""







            return rescale(__lowercase						,      scale=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: np.ndarray						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: int						,      ):







            """simple docstring"""







            return normalize(__lowercase						,      mean=__lowercase						,      std=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: ImageInput						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: PILImageResampling = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[float] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[str, TensorType]] = None						,      __lowercase			: ChannelDimension = ChannelDimension.FIRST						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=do_center_crop if do_center_crop is not None else self.do_center_crop
            __lowercase					=crop_size if crop_size is not None else self.crop_size
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=do_resize if do_resize is not None else self.do_resize
            __lowercase					=size if size is not None else self.size
            __lowercase					=get_size_dict(__lowercase      )
            __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_normalize if do_normalize is not None else self.do_normalize
            __lowercase					=image_mean if image_mean is not None else self.image_mean
            __lowercase					=image_std if image_std is not None else self.image_std

            __lowercase					=make_list_of_images(__lowercase      )

            if not valid_images(__lowercase      ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'      )

            if do_center_crop and crop_size is None:
                 raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'      )

            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_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'      )

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

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

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

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

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

            __lowercase					=[to_channel_dimension_format(__lowercase						,      __lowercase      ) for image in images]

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


