




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


__a  :		List[Any]		     =   '\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 				UpperCAmelCase(					A__					):



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase	,				lowerCamelCase    ) ->      Any:





								"""simple docstring"""


								super().__init__()

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

								lowercase__  :	str        =							None

	def    __a     (					self	,				lowerCamelCase=0    ) ->      Union[str, Any]:





								"""simple docstring"""


								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"    )

								lowercase__  :	Any        =							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    __a     (					self	,				lowerCamelCase=0    ) ->      Any:





								"""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."    )

								lowercase__  :	Tuple        =							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)

								lowercase__  :	Optional[Any]        =							None
								for cpu_offloaded_model in [self.unet]:
															lowercase__ ,       lowercase__  :	Optional[Any]        =							cpu_offload_with_hook(lowerCamelCase	,				lowerCamelCase	,				prev_module_hook=lowerCamelCase    )

								if self.safety_checker is not None:
															lowercase__ ,       lowercase__  :	List[str]        =							cpu_offload_with_hook(self.safety_checker	,				lowerCamelCase	,				prev_module_hook=lowerCamelCase    )

								# We'll offload the last model manually.
								lowercase__  :	Dict        =							hook

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								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    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	int        =							(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:
															lowercase__  :	List[Any]        =							randn_tensor(lowerCamelCase	,				generator=lowerCamelCase	,				device=lowerCamelCase	,				dtype=lowerCamelCase    )
								else:
															lowercase__  :	Any        =							latents.to(device=lowerCamelCase	,				dtype=lowerCamelCase    )

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

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase = "pil"    ) ->      Tuple:





								"""simple docstring"""


								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
								lowercase__  :	List[str]        =							(sample / 2 + 0.5).clamp(0	,				1    )
								if output_type == "pt":
															return sample

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

								# Output_type must be 'pil'
								lowercase__  :	str        =							self.numpy_to_pil(lowerCamelCase    )
								return sample

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      List[str]:





								"""simple docstring"""


								if self.unet.config.num_class_embeds is not None:
															if isinstance(lowerCamelCase	,				lowerCamelCase    ):
																						lowercase__  :	Union[str, Any]        =							torch.tensor(lowerCamelCase	,				dtype=torch.int    )
															elif isinstance(lowerCamelCase	,				lowerCamelCase    ):
																						assert batch_size == 1, "Batch size must be 1 if classes is an int"
																						lowercase__  :	int        =							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
																						lowercase__  :	Optional[int]        =							torch.randint(0	,				self.unet.config.num_class_embeds	,				size=(batch_size,)    )
															lowercase__  :	Any        =							class_labels.to(lowerCamelCase    )
								else:
															lowercase__  :	Tuple        =							None
								return class_labels

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								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:
															lowercase__  :	Optional[Any]        =							(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	,				lowerCamelCase = 1	,				lowerCamelCase = None	,				lowerCamelCase = 1	,				lowerCamelCase = None	,				lowerCamelCase = None	,				lowerCamelCase = None	,				lowerCamelCase = "pil"	,				lowerCamelCase = True	,				lowerCamelCase = None	,				lowerCamelCase = 1	,				) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Tuple        =							self.unet.config.sample_size
								lowercase__  :	List[str]        =							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)
								lowercase__  :	Optional[int]        =							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
								lowercase__  :	Any        =							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    )
															lowercase__  :	List[Any]        =							self.scheduler.timesteps
															lowercase__  :	Tuple        =							len(lowerCamelCase    )
								else:
															self.scheduler.set_timesteps(lowerCamelCase    )
															lowercase__  :	Tuple        =							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    ):
																						lowercase__  :	Any        =							self.scheduler.scale_model_input(lowerCamelCase	,				lowerCamelCase    )
																						lowercase__  :	List[str]        =							self.unet(lowerCamelCase	,				lowerCamelCase	,				class_labels=lowerCamelCase	,				return_dict=lowerCamelCase    )[0]

																						lowercase__  :	Optional[Any]        =							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
								lowercase__  :	Dict        =							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    )





from __future__ import annotations

import math
import random
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			list[int]:
							lowercase__  :	List[Any]        =							d * e - 1
							lowercase__  :	Any        =							0
							lowercase__  :	Dict        =							0
							while p == 0:
														lowercase__  :	Dict        =							random.randint(2    ,n - 1    )
														lowercase__  :	int        =							k
														while True:
																					if t % 2 == 0:
																												lowercase__  :	str        =							t // 2
																												lowercase__  :	int        =							(g**t) % n
																												lowercase__  :	List[Any]        =							math.gcd(x - 1    ,SCREAMING_SNAKE_CASE_    )
																												if x > 1 and y > 1:
																																			lowercase__  :	Any        =							y
																																			lowercase__  :	Optional[int]        =							n // y
																																			break  # find the correct factors
																					else:
																												break  # t is not divisible by 2, break and choose another g
							return sorted([p, q]    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()