






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase__							=     logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase__							=     """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   Optional[Any]	,snake_case__     :   Optional[int]=8  ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __snake_case			:List[str]          =						width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__			,						) ->      int:






            '''simple docstring'''





            super().__init__()

            self.register_modules(
                unet=a__			,						scheduler=a__			,						movq=a__			,						)
            __snake_case			:Optional[int]          =						2 ** (len(self.movq.config.block_out_channels    ) - 1)



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__    ) ->      Tuple:






            '''simple docstring'''





            if latents is None:
                 __snake_case			:Optional[int]          =						randn_tensor(a__			,						generator=a__			,						device=a__			,						dtype=a__    )
            else:
                 if latents.shape != shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
                 __snake_case			:Optional[int]          =						latents.to(a__    )

            __snake_case			:List[str]          =						latents * scheduler.init_noise_sigma
            return latents



       def 					__lowercase						(							self			,						a__=0    ) ->      int:






            '''simple docstring'''





            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

            __snake_case			:Any          =						torch.device(F'''cuda:{gpu_id}'''    )

            __snake_case			:int          =						[
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                      cpu_offload(a__			,						a__    )



       def 					__lowercase						(							self			,						a__=0    ) ->      Optional[int]:






            '''simple docstring'''





            if is_accelerate_available() and is_accelerate_version(""">="""			,						"""0.17.0.dev0"""    ):
                 from accelerate import cpu_offload_with_hook
            else:
                 raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""    )

            __snake_case			:Tuple          =						torch.device(F'''cuda:{gpu_id}'''    )

            if self.device.type != "cpu":
                 self.to("""cpu"""			,						silence_dtype_warnings=a__    )
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

            __snake_case			:Optional[Any]          =						None
            for cpu_offloaded_model in [self.unet, self.movq]:
                 __snake_case       , __snake_case			:List[Any]          =						cpu_offload_with_hook(a__			,						a__			,						prev_module_hook=a__    )

            # We'll offload the last model manually.
            __snake_case			:Union[str, Any]          =						hook



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            if not hasattr(self.unet			,						"""_hf_hook"""    ):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(a__			,						"""_hf_hook"""    )
                     and hasattr(module._hf_hook			,						"""execution_device"""    )
                     and module._hf_hook.execution_device is not None
                 ):
                      return torch.device(module._hf_hook.execution_device    )
            return self.device



       @torch.no_grad()
       @replace_example_docstring(a__    )
       def __call__(							self			,						a__			,						a__			,						a__ = 5_12			,						a__ = 5_12			,						a__ = 1_00			,						a__ = 4.0			,						a__ = 1			,						a__ = None			,						a__ = None			,						a__ = "pil"			,						a__ = True			,						) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						self._execution_device

            __snake_case			:Dict          =						guidance_scale > 1.0

            if isinstance(a__			,						a__    ):
                 __snake_case			:Optional[int]          =						torch.cat(a__			,						dim=0    )
            __snake_case			:Optional[int]          =						image_embeds.shape[0] * num_images_per_prompt
            if isinstance(a__			,						a__    ):
                 __snake_case			:Optional[Any]          =						torch.cat(a__			,						dim=0    )

            if do_classifier_free_guidance:
                 __snake_case			:Any          =						image_embeds.repeat_interleave(a__			,						dim=0    )
                 __snake_case			:Any          =						negative_image_embeds.repeat_interleave(a__			,						dim=0    )

            __snake_case			:Tuple          =						torch.cat([negative_image_embeds, image_embeds]			,						dim=0    ).to(dtype=self.unet.dtype			,						device=a__    )

            self.scheduler.set_timesteps(a__			,						device=a__    )
            __snake_case			:Optional[Any]          =						self.scheduler.timesteps

            __snake_case			:Any          =						self.unet.config.in_channels

            __snake_case       , __snake_case			:Any          =						downscale_height_and_width(a__			,						a__			,						self.movq_scale_factor    )

            # create initial latent
            __snake_case			:Union[str, Any]          =						self.prepare_latents(
                (batch_size, num_channels_latents, height, width)			,						image_embeds.dtype			,						a__			,						a__			,						a__			,						self.scheduler			,						)

            for i, t in enumerate(self.progress_bar(a__    )    ):
                 # expand the latents if we are doing classifier free guidance
                 __snake_case			:Any          =						torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

                 __snake_case			:List[Any]          =						{"""image_embeds""": image_embeds}
                 __snake_case			:Any          =						self.unet(
                     sample=a__			,						timestep=a__			,						encoder_hidden_states=a__			,						added_cond_kwargs=a__			,						return_dict=a__			,						)[0]

                 if do_classifier_free_guidance:
                      __snake_case       , __snake_case			:int          =						noise_pred.split(latents.shape[1]			,						dim=1    )
                      __snake_case       , __snake_case			:Union[str, Any]          =						noise_pred.chunk(2    )
                      __snake_case       , __snake_case			:int          =						variance_pred.chunk(2    )
                      __snake_case			:Any          =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      __snake_case			:List[Any]          =						torch.cat([noise_pred, variance_pred_text]			,						dim=1    )

                 if not (
                     hasattr(self.scheduler.config			,						"""variance_type"""    )
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                      __snake_case       , __snake_case			:Union[str, Any]          =						noise_pred.split(latents.shape[1]			,						dim=1    )

                 # compute the previous noisy sample x_t -> x_t-1
                 __snake_case			:Union[str, Any]          =						self.scheduler.step(
                     a__			,						a__			,						a__			,						generator=a__			,						)[0]
            # post-processing
            __snake_case			:List[Any]          =						self.movq.decode(a__			,						force_not_quantize=a__    )["""sample"""]

            if output_type not in ["pt", "np", "pil"]:
                 raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''    )

            if output_type in ["np", "pil"]:
                 __snake_case			:int          =						image * 0.5 + 0.5
                 __snake_case			:Optional[int]          =						image.clamp(0			,						1    )
                 __snake_case			:Optional[int]          =						image.cpu().permute(0			,						2			,						3			,						1    ).float().numpy()

            if output_type == "pil":
                 __snake_case			:Optional[Any]          =						self.numpy_to_pil(a__    )

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=a__    )










from __future__ import annotations

lowerCamelCase__							=     list[list[int]]

# assigning initial values to the grid
lowerCamelCase__							=     [
    [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
lowerCamelCase__							=     [
    [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   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   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   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,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""" + """=""" * 20)
          print_solution(example_grid)
          print("""\nExample grid solution:""")
          lowerCamelCase__							=     sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")


