

"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):







			@register_to_config
			def __init__(			self			: str			,  lowercase			: int = 6_55_36			,  lowercase			: Optional[int] = None			,  lowercase			: int = 2			,  lowercase			: int = 2			,  lowercase			: int = 0			,  lowercase			: str = "fourier"			,  lowercase			: bool = True			,  lowercase			: bool = False			,  lowercase			: float = 0.0			,  lowercase			: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,  lowercase			: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,  lowercase			: Tuple[str] = "UNetMidBlock1D"			,  lowercase			: str = None			,  lowercase			: Tuple[int] = (32, 32, 64)			,  lowercase			: str = None			,  lowercase			: int = 8			,  lowercase			: int = 1			,  lowercase			: bool = False			,  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Tuple      =				sample_size

								# time
								if time_embedding_type == "fourier":
													UpperCAmelCase			:   int      =				GaussianFourierProjection(
													    embedding_size=8			,  set_W_to_weight=lowercase			,  log=lowercase			,  flip_sin_to_cos=lowercase  )
													UpperCAmelCase			:   int      =				2 * block_out_channels[0]
								elif time_embedding_type == "positional":
													UpperCAmelCase			:   Tuple      =				Timesteps(
													    block_out_channels[0]			,  flip_sin_to_cos=lowercase			,  downscale_freq_shift=lowercase  )
													UpperCAmelCase			:   Dict      =				block_out_channels[0]

								if use_timestep_embedding:
													UpperCAmelCase			:   Optional[Any]      =				block_out_channels[0] * 4
													UpperCAmelCase			:   Optional[Any]      =				TimestepEmbedding(
													    in_channels=lowercase			,  time_embed_dim=lowercase			,  act_fn=lowercase			,  out_dim=block_out_channels[0]			,  )

								UpperCAmelCase			:   Dict      =				nn.ModuleList([]  )
								UpperCAmelCase			:   str      =				None
								UpperCAmelCase			:   Any      =				nn.ModuleList([]  )
								UpperCAmelCase			:   Tuple      =				None

								# down
								UpperCAmelCase			:   Optional[Any]      =				in_channels
								for i, down_block_type in enumerate(lowercase  ):
													UpperCAmelCase			:   Optional[Any]      =				output_channel
													UpperCAmelCase			:   Optional[int]      =				block_out_channels[i]

													if i == 0:
																		input_channel += extra_in_channels

													UpperCAmelCase			:   int      =				i == len(lowercase  ) - 1

													UpperCAmelCase			:   Dict      =				get_down_block(
													    lowercase			,  num_layers=lowercase			,  in_channels=lowercase			,  out_channels=lowercase			,  temb_channels=block_out_channels[0]			,  add_downsample=not is_final_block or downsample_each_block			,  )
													self.down_blocks.append(lowercase  )

								# mid
								UpperCAmelCase			:   Optional[Any]      =				get_mid_block(
								    lowercase			,  in_channels=block_out_channels[-1]			,  mid_channels=block_out_channels[-1]			,  out_channels=block_out_channels[-1]			,  embed_dim=block_out_channels[0]			,  num_layers=lowercase			,  add_downsample=lowercase			,  )

								# up
								UpperCAmelCase			:   Tuple      =				list(reversed(lowercase  )  )
								UpperCAmelCase			:   str      =				reversed_block_out_channels[0]
								if out_block_type is None:
													UpperCAmelCase			:   Optional[int]      =				out_channels
								else:
													UpperCAmelCase			:   int      =				block_out_channels[0]

								for i, up_block_type in enumerate(lowercase  ):
													UpperCAmelCase			:   Any      =				output_channel
													UpperCAmelCase			:   str      =				(
													    reversed_block_out_channels[i + 1] if i < len(lowercase  ) - 1 else final_upsample_channels
													)

													UpperCAmelCase			:   Dict      =				i == len(lowercase  ) - 1

													UpperCAmelCase			:   Tuple      =				get_up_block(
													    lowercase			,  num_layers=lowercase			,  in_channels=lowercase			,  out_channels=lowercase			,  temb_channels=block_out_channels[0]			,  add_upsample=not is_final_block			,  )
													self.up_blocks.append(lowercase  )
													UpperCAmelCase			:   List[Any]      =				output_channel

								# out
								UpperCAmelCase			:   List[Any]      =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,  32  )
								UpperCAmelCase			:   Any      =				get_out_block(
								    out_block_type=lowercase			,  num_groups_out=lowercase			,  embed_dim=block_out_channels[0]			,  out_channels=lowercase			,  act_fn=lowercase			,  fc_dim=block_out_channels[-1] // 4			,  )







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: torch.FloatTensor			,  lowercase			: Union[torch.Tensor, float, int]			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				timestep
								if not torch.is_tensor(lowercase  ):
													UpperCAmelCase			:   Optional[int]      =				torch.tensor([timesteps]			,  dtype=torch.long			,  device=sample.device  )
								elif torch.is_tensor(lowercase  ) and len(timesteps.shape  ) == 0:
													UpperCAmelCase			:   Optional[Any]      =				timesteps[None].to(sample.device  )

								UpperCAmelCase			:   List[Any]      =				self.time_proj(lowercase  )
								if self.config.use_timestep_embedding:
													UpperCAmelCase			:   str      =				self.time_mlp(lowercase  )
								else:
													UpperCAmelCase			:   Union[str, Any]      =				timestep_embed[..., None]
													UpperCAmelCase			:   Dict      =				timestep_embed.repeat([1, 1, sample.shape[2]]  ).to(sample.dtype  )
													UpperCAmelCase			:   List[str]      =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])  )

								# 2. down
								UpperCAmelCase			:   Union[str, Any]      =				()
								for downsample_block in self.down_blocks:
													UpperCAmelCase      ,							UpperCAmelCase			:   Tuple      =				downsample_block(hidden_states=lowercase			,  temb=lowercase  )
													down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
													UpperCAmelCase			:   Optional[Any]      =				self.mid_block(lowercase			,  lowercase  )

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks  ):
													UpperCAmelCase			:   Any      =				down_block_res_samples[-1:]
													UpperCAmelCase			:   int      =				down_block_res_samples[:-1]
													UpperCAmelCase			:   List[Any]      =				upsample_block(lowercase			,  res_hidden_states_tuple=lowercase			,  temb=lowercase  )

								# 5. post-process
								if self.out_block:
													UpperCAmelCase			:   int      =				self.out_block(lowercase			,  lowercase  )

								if not return_dict:
													return (sample,)

								return UNetaDOutput(sample=lowercase  )





"""simple docstring"""






from __future__ import annotations

snake_case_   :   str        =       list[list[int]]

# assigning initial values to the grid
snake_case_   :   Matrix        =       [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
snake_case_   :   Matrix        =       [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]



def   lowercase_	(    _lowercase       :		Matrix							,      _lowercase       :		int							,      _lowercase       :		int							,      _lowercase       :		int	):




					'''simple docstring'''






					for i in range(9	):
										if grid[row][i] == n or grid[i][column] == n:
															return False

					for i in range(3	):
										for j in range(3	):
															if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																				return False

					return True



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for i in range(9	):
										for j in range(9	):
															if grid[i][j] == 0:
																				return i, j
					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					if location := find_empty_location(_lowercase	):
										UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1							,      10	):
										if is_safe(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
															UpperCAmelCase			:   int      =				digit

															if sudoku(_lowercase	) is not None:
																				return grid

															UpperCAmelCase			:   Optional[Any]      =				0

					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for row in grid:
										for cell in row:
															print(_lowercase							,      end=" "	)
										print()


if __name__ == "__main__":
	# make a copy of grid so that you can compare with the unmodified grid
	for example_grid in (initial_grid, no_solution):
		print("""\nExample grid:\n""" + """=""" * 2_0)
		print_solution(example_grid)
		print("""\nExample grid solution:""")
		snake_case_   :   Dict        =       sudoku(example_grid)
		if solution is not None:
			print_solution(solution)
		else:
			print("""Cannot find a solution.""")


