

def  _snake_case		(snake_case_					:     list[list[int]]  )							->		bool:
  # must be
  __UpperCAmelCase	:  int		=     [list(snake_case_  ) for row in matrix]
  if matrix and isinstance(snake_case_    ,       snake_case_  ):
    if isinstance(matrix[0]    ,       snake_case_  ):
      __UpperCAmelCase	:  Tuple		=     0
      for row in matrix:
        if prev_len == 0:
          __UpperCAmelCase	:  Dict		=     len(snake_case_  )
          __UpperCAmelCase	:  Optional[Any]		=     True
        else:
          __UpperCAmelCase	:  Union[str, Any]		=     prev_len == len(snake_case_  )
    else:
      __UpperCAmelCase	:  int		=     True
  else:
    __UpperCAmelCase	:  str		=     False

  return result





def  _snake_case		(snake_case_					:     list[list[int]]  )							->		None:
  if check_matrix(snake_case_  ) and len(snake_case_  ) > 0:
    __UpperCAmelCase	:  List[str]		=     [list(snake_case_  ) for row in a]
    __UpperCAmelCase	:  Union[str, Any]		=     len(snake_case_  )
    if isinstance(a[0]    ,       snake_case_  ):
      __UpperCAmelCase	:  Tuple		=     len(a[0]  )
    else:
      for dat in a:
        print(snake_case_  )
      return

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





def  _snake_case		(snake_case_					:     list[list]  )							->		list[int]:
  if matrix:
    return list(matrix.pop(0  )  ) + spiral_traversal(list(zip(*snake_case_  )  )[::-1]  )
  else:
    return []


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

      doctest.testmod()

      _SCREAMING_SNAKE_CASE							 =			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 1_0, 1_1, 1_2]]
      spiral_print_clockwise(a)







'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )