






def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 return len_board * len_board_column * row + column



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		list[list[str]]			,    SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		set[int]			,    )					->       bool:

 if board[row][column] != word[word_index]:
  return False

 if word_index == len(SCREAMING_SNAKE_CASE__			) - 1:
  return True

 _snake_case      :					Union[str, Any]					=   [(0, 1), (0, -1), (-1, 0), (1, 0)]
 _snake_case      :					Union[str, Any]					=   len(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Any					=   len(board[0]			)
 for direction in traverts_directions:
  _snake_case      :					Any					=   row + direction[0]
  _snake_case      :					Optional[int]					=   column + direction[1]
  if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
   continue

  _snake_case      :					Optional[int]					=   get_point_key(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
  if key in visited_points_set:
   continue

  visited_points_set.add(SCREAMING_SNAKE_CASE__			)
  if exits_word(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    word_index + 1			,    SCREAMING_SNAKE_CASE__			):
   return True

  visited_points_set.remove(SCREAMING_SNAKE_CASE__			)

 return False



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		list[list[str]]			,    SCREAMING_SNAKE_CASE__    :		str			)					->       bool:
 _snake_case      :					Any					=   (
     """The board should be a non empty matrix of single chars strings."""
 )

 _snake_case      :					str					=   len(SCREAMING_SNAKE_CASE__			)
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			) or len(SCREAMING_SNAKE_CASE__			) == 0:
  raise ValueError(SCREAMING_SNAKE_CASE__			)

 for row in board:
  if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			) or len(SCREAMING_SNAKE_CASE__			) == 0:
   raise ValueError(SCREAMING_SNAKE_CASE__			)

  for item in row:
   if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			) or len(SCREAMING_SNAKE_CASE__			) != 1:
    raise ValueError(SCREAMING_SNAKE_CASE__			)

    # Validate word
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			) or len(SCREAMING_SNAKE_CASE__			) == 0:
  raise ValueError(
      """The word parameter should be a string of length greater than 0."""			)

 _snake_case      :					List[str]					=   len(board[0]			)
 for i in range(SCREAMING_SNAKE_CASE__			):
  for j in range(SCREAMING_SNAKE_CASE__			):
   if exits_word(
       SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    0			,    {get_point_key(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)}			):
    return True

 return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







@slow
@require_torch_gpu
class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
