



'''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


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


_UpperCamelCase											=			'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					super().__init__()

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

					__UpperCAmelCase   :      Union[str, Any]										=     None





		def 							__A       (     self ,       __UpperCAmelCase=0				)       ->     List[str]:

					'''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"""				)

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

					if self.device.type != "cpu":
								self.to("""cpu""" ,       silence_dtype_warnings=__UpperCAmelCase				)
								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(__UpperCAmelCase ,       __UpperCAmelCase				)

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





		def 							__A       (     self ,       __UpperCAmelCase=0				)       ->     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."""				)

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

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

					__UpperCAmelCase   :      Union[str, Any]										=     None
					for cpu_offloaded_model in [self.unet]:
								__UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     cpu_offload_with_hook(__UpperCAmelCase ,       __UpperCAmelCase ,       prev_module_hook=__UpperCAmelCase				)

					if self.safety_checker is not None:
								__UpperCAmelCase				,  __UpperCAmelCase   :      Optional[int]										=     cpu_offload_with_hook(self.safety_checker ,       __UpperCAmelCase ,       prev_module_hook=__UpperCAmelCase				)

					# We'll offload the last model manually.
					__UpperCAmelCase   :      List[str]										=     hook





		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					if not hasattr(self.unet ,       """_hf_hook"""				):
								return self.device
					for module in self.unet.modules():
								if (
								    hasattr(__UpperCAmelCase ,       """_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 ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     (batch_size, num_channels, height, width)
					if isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and len(__UpperCAmelCase				) != batch_size:
								raise ValueError(
								    f'You have passed a list of generators of length {len(__UpperCAmelCase				)}, 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:
								__UpperCAmelCase   :      str										=     randn_tensor(__UpperCAmelCase ,       generator=__UpperCAmelCase ,       device=__UpperCAmelCase ,       dtype=__UpperCAmelCase				)
					else:
								__UpperCAmelCase   :      List[Any]										=     latents.to(device=__UpperCAmelCase ,       dtype=__UpperCAmelCase				)

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





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase = "pil"				)       ->     Optional[int]:

					'''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
					__UpperCAmelCase   :      Optional[Any]										=     (sample / 2 + 0.5).clamp(0 ,       1				)
					if output_type == "pt":
								return sample

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

					# Output_type must be 'pil'
					__UpperCAmelCase   :      List[str]										=     self.numpy_to_pil(__UpperCAmelCase				)
					return sample





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     int:

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





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''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:
								__UpperCAmelCase   :      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(__UpperCAmelCase ,       __UpperCAmelCase				) or callback_steps <= 0)
					):
								raise ValueError(
								    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
								    f' {type(__UpperCAmelCase				)}.'				)





		@torch.no_grad()
		@replace_example_docstring(__UpperCAmelCase				)
		def __call__(     self ,       __UpperCAmelCase = 1 ,       __UpperCAmelCase = None ,       __UpperCAmelCase = 1 ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = "pil" ,       __UpperCAmelCase = True ,       __UpperCAmelCase = None ,       __UpperCAmelCase = 1 ,       )       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.unet.config.sample_size
					__UpperCAmelCase   :      Dict										=     self._execution_device

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

					# 2. Prepare image latents
					# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
					__UpperCAmelCase   :      List[Any]										=     self.prepare_latents(
					    batch_size=__UpperCAmelCase ,       num_channels=self.unet.config.in_channels ,       height=__UpperCAmelCase ,       width=__UpperCAmelCase ,       dtype=self.unet.dtype ,       device=__UpperCAmelCase ,       generator=__UpperCAmelCase ,       latents=__UpperCAmelCase ,       )

					# 3. Handle class_labels for class-conditional models
					__UpperCAmelCase   :      str										=     self.prepare_class_labels(__UpperCAmelCase ,       __UpperCAmelCase ,       class_labels=__UpperCAmelCase				)

					# 4. Prepare timesteps
					if timesteps is not None:
								self.scheduler.set_timesteps(timesteps=__UpperCAmelCase ,       device=__UpperCAmelCase				)
								__UpperCAmelCase   :      Any										=     self.scheduler.timesteps
								__UpperCAmelCase   :      Dict										=     len(__UpperCAmelCase				)
					else:
								self.scheduler.set_timesteps(__UpperCAmelCase				)
								__UpperCAmelCase   :      Union[str, Any]										=     self.scheduler.timesteps

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

											__UpperCAmelCase   :      List[str]										=     self.scheduler.step(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       generator=__UpperCAmelCase				)[0]

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

        # 6. Post-process image sample
					__UpperCAmelCase   :      Dict										=     self.postprocess_image(__UpperCAmelCase ,       output_type=__UpperCAmelCase				)

					# 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=__UpperCAmelCase				)









'''simple docstring'''
from itertools import permutations


def   lowercase_	(   lowerCAmelCase__	:  int = 5 ):

			"""simple docstring"""





			if gon_side < 3 or gon_side > 5:
						raise ValueError("""gon_side must be in the range [3, 5]""" )

			# Since it's 16, we know 10 is on the outer ring
			# Put the big numbers at the end so that they are never the first number
			__UpperCAmelCase   :      int										=     list(range(gon_side + 1			,							0			,							-1 ) )
			__UpperCAmelCase   :      int										=     list(range(gon_side + 2			,							gon_side * 2 + 1 ) )

			for perm in permutations(small_numbers + big_numbers ):
						__UpperCAmelCase   :      Optional[int]										=     generate_gon_ring(lowerCAmelCase__			,							list(lowerCAmelCase__ ) )
						if is_magic_gon(lowerCAmelCase__ ):
									return int("""""".join(str(lowerCAmelCase__ ) for n in numbers ) )

			__UpperCAmelCase   :      Optional[int]										=     f'Magic {gon_side}-gon ring is impossible'
			raise ValueError(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     [0] * (gon_side * 3)
			__UpperCAmelCase   :      Optional[Any]										=     perm[0:3]
			perm.append(perm[1] )

			__UpperCAmelCase   :      Union[str, Any]										=     1 if gon_side < 5 else 2

			for i in range(1			,							len(lowerCAmelCase__ ) // 3 + magic_number ):
						__UpperCAmelCase   :      List[str]										=     perm[2 * i + 1]
						__UpperCAmelCase   :      Optional[int]										=     result[3 * i - 1]
						__UpperCAmelCase   :      Optional[int]										=     perm[2 * i + 2]

			return result


def   lowercase_	(   lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			if len(lowerCAmelCase__ ) % 3 != 0:
						raise ValueError("""a gon ring should have a length that is a multiple of 3""" )

			if min(numbers[::3] ) != numbers[0]:
						return False

			__UpperCAmelCase   :      Optional[int]										=     sum(numbers[:3] )

			return all(sum(numbers[i : i + 3] ) == total for i in range(3			,							len(lowerCAmelCase__ )			,							3 ) )


if __name__ == "__main__":
					print(solution())




