
'''simple docstring'''





from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


A           =   "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"



class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  def __init__(self  : List[Any]	, lowerCAmelCase_  : UNetaDModel	, lowerCAmelCase_  : CMStochasticIterativeScheduler    )   ->       None:
        super().__init__()

        self.register_modules(
            unet=lowerCAmelCase_	, scheduler=lowerCAmelCase_	, )

        snake_case_						       =							None

  def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : Optional[Any]=0    )   ->       Dict:
        if is_accelerate_available() and is_accelerate_version(">="	, "0.14.0"    ):
              from accelerate import cpu_offload
        else:
              raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"    )

        snake_case_						       =							torch.device(f'''cuda:{gpu_id}'''    )

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

        for cpu_offloaded_model in [self.unet]:
              cpu_offload(lowerCAmelCase_	, lowerCAmelCase_    )

        if self.safety_checker is not None:
              cpu_offload(self.safety_checker	, execution_device=lowerCAmelCase_	, offload_buffers=lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : str	, lowerCAmelCase_  : Tuple=0    )   ->       Optional[int]:
        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_						       =							torch.device(f'''cuda:{gpu_id}'''    )

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

        snake_case_						       =							None
        for cpu_offloaded_model in [self.unet]:
              snake_case_       ,	snake_case_						       =							cpu_offload_with_hook(lowerCAmelCase_	, lowerCAmelCase_	, prev_module_hook=lowerCAmelCase_    )

        if self.safety_checker is not None:
              snake_case_       ,	snake_case_						       =							cpu_offload_with_hook(self.safety_checker	, lowerCAmelCase_	, prev_module_hook=lowerCAmelCase_    )

        # We'll offload the last model manually.
        snake_case_						       =							hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
        if not hasattr(self.unet	, "_hf_hook"    ):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(lowerCAmelCase_	, "_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

  def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : Any=None    )   ->       str:
        snake_case_						       =							(batch_size, num_channels, height, width)
        if isinstance(lowerCAmelCase_	, lowerCAmelCase_    ) and len(lowerCAmelCase_    ) != batch_size:
              raise ValueError(
                  f'''You have passed a list of generators of length {len(lowerCAmelCase_    )}, but requested an effective batch'''
                  f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''    )

        if latents is None:
              snake_case_						       =							randn_tensor(lowerCAmelCase_	, generator=lowerCAmelCase_	, device=lowerCAmelCase_	, dtype=lowerCAmelCase_    )
        else:
              snake_case_						       =							latents.to(device=lowerCAmelCase_	, dtype=lowerCAmelCase_    )

        # scale the initial noise by the standard deviation required by the scheduler
        snake_case_						       =							latents * self.scheduler.init_noise_sigma
        return latents

  def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : torch.FloatTensor	, lowerCAmelCase_  : str = "pil"    )   ->       Optional[int]:
        if output_type not in ["pt", "np", "pil"]:
              raise ValueError(
                  f'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''    )

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        snake_case_						       =							(sample / 2 + 0.5).clamp(0	, 1    )
        if output_type == "pt":
              return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        snake_case_						       =							sample.cpu().permute(0	, 2	, 3	, 1    ).numpy()
        if output_type == "np":
              return sample

        # Output_type must be 'pil'
        snake_case_						       =							self.numpy_to_pil(lowerCAmelCase_    )
        return sample

  def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : List[Any]=None    )   ->       Union[str, Any]:
        if self.unet.config.num_class_embeds is not None:
              if isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                    snake_case_						       =							torch.tensor(lowerCAmelCase_	, dtype=torch.int    )
              elif isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                    assert batch_size == 1, "Batch size must be 1 if classes is an int"
                    snake_case_						       =							torch.tensor([class_labels]	, dtype=torch.int    )
              elif class_labels is None:
                    # Randomly generate batch_size class labels
                    # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                    snake_case_						       =							torch.randint(0	, self.unet.config.num_class_embeds	, size=(batch_size,)    )
              snake_case_						       =							class_labels.to(lowerCAmelCase_    )
        else:
              snake_case_						       =							None
        return class_labels

  def 		_lowerCamelCase	(self  : List[Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Optional[Any]    )   ->       List[str]:
        if num_inference_steps is None and timesteps is None:
              raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."    )

        if num_inference_steps is not None and timesteps is not None:
              logger.warning(
                  f'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
                  " `timesteps` will be used over `num_inference_steps`."    )

        if latents is not None:
              snake_case_						       =							(batch_size, 3, img_size, img_size)
              if latents.shape != expected_shape:
                    raise ValueError(f'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''    )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(lowerCAmelCase_	, lowerCAmelCase_    ) or callback_steps <= 0)
        ):
              raise ValueError(
                  f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  f''' {type(lowerCAmelCase_    )}.'''    )


  @torch.no_grad()
  @replace_example_docstring(lowerCAmelCase_    )
  def __call__(self  : Tuple	, lowerCAmelCase_  : int = 1	, lowerCAmelCase_  : Optional[Union[torch.Tensor, List[int], int]] = None	, lowerCAmelCase_  : int = 1	, lowerCAmelCase_  : List[int] = None	, lowerCAmelCase_  : Optional[Union[torch.Generator, List[torch.Generator]]] = None	, lowerCAmelCase_  : Optional[torch.FloatTensor] = None	, lowerCAmelCase_  : Optional[str] = "pil"	, lowerCAmelCase_  : bool = True	, lowerCAmelCase_  : Optional[Callable[[int, int, torch.FloatTensor], None]] = None	, lowerCAmelCase_  : int = 1	, )   ->       Union[str, Any]:
        snake_case_						       =							self.unet.config.sample_size
        snake_case_						       =							self._execution_device

        # 1. Check inputs
        self.check_inputs(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        snake_case_						       =							self.prepare_latents(
            batch_size=lowerCAmelCase_	, num_channels=self.unet.config.in_channels	, height=lowerCAmelCase_	, width=lowerCAmelCase_	, dtype=self.unet.dtype	, device=lowerCAmelCase_	, generator=lowerCAmelCase_	, latents=lowerCAmelCase_	, )

        # 3. Handle class_labels for class-conditional models
        snake_case_						       =							self.prepare_class_labels(lowerCAmelCase_	, lowerCAmelCase_	, class_labels=lowerCAmelCase_    )

        # 4. Prepare timesteps
        if timesteps is not None:
              self.scheduler.set_timesteps(timesteps=lowerCAmelCase_	, device=lowerCAmelCase_    )
              snake_case_						       =							self.scheduler.timesteps
              snake_case_						       =							len(lowerCAmelCase_    )
        else:
              self.scheduler.set_timesteps(lowerCAmelCase_    )
              snake_case_						       =							self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=lowerCAmelCase_    ) as progress_bar:
              for i, t in enumerate(lowerCAmelCase_    ):
                    snake_case_						       =							self.scheduler.scale_model_input(lowerCAmelCase_	, lowerCAmelCase_    )
                    snake_case_						       =							self.unet(lowerCAmelCase_	, lowerCAmelCase_	, class_labels=lowerCAmelCase_	, return_dict=lowerCAmelCase_    )[0]

                    snake_case_						       =							self.scheduler.step(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, generator=lowerCAmelCase_    )[0]

                    # call the callback, if provided
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                          callback(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )

        # 6. Post-process image sample
        snake_case_						       =							self.postprocess_image(lowerCAmelCase_	, output_type=lowerCAmelCase_    )

        # Offload last model to CPU
        if hasattr(self	, "final_offload_hook"    ) and self.final_offload_hook is not None:
              self.final_offload_hook.offload()

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase_    )



'''simple docstring'''





from __future__ import annotations

import random



class  lowerCAmelCase__   :



  '''simple docstring'''






  _UpperCamelCase						= 6

  def __init__(self  : List[Any]    )   ->       Optional[Any]:
        snake_case_						       =							list(range(1	, Dice.NUM_SIDES + 1    )    )


  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
        return random.choice(self.sides    )

def   snake_case_       (							_a     :							int ,					_a     :							int = 2			):
      snake_case_						       =							[Dice() for i in range(_a			)]
      snake_case_						       =							[0] * (len(_a			) * Dice.NUM_SIDES + 1)
      for _ in range(_a			):
            count_of_sum[sum(dice.roll() for dice in dices			)] += 1
      snake_case_						       =							[round((count * 100) / num_throws ,					2			) for count in count_of_sum]
      return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()

