



def 						__lowerCamelCase       (      __magic_name__	:  list[list[int]]     , __magic_name__	:  list[list[int]]						):
							a__:       Dict											=[]
							a__:       List[Any]											=len(lowercase__						)
							for i in range(lowercase__						):
														a__:       str											=[]
														for j in range(lowercase__						):
																					a__:       Union[str, Any]											=0
																					for k in range(lowercase__						):
																												a__:       Any											=val + matrix_a[i][k] * matrix_b[k][j]
																					list_a.append(lowercase__						)
														matrix_c.append(lowercase__						)
							return matrix_c





def 						__lowerCamelCase       (      __magic_name__	:  int						):
							return [[int(row == column						) for column in range(lowercase__						)] for row in range(lowercase__						)]





def 						__lowerCamelCase       (      __magic_name__	:  int						):
							if n <= 1:
														return n
							a__:       int											=identity(2						)
							a__:       Optional[Any]											=[[1, 1], [1, 0]]
							a__:       Optional[int]											=n - 1
							while n > 0:
														if n % 2 == 1:
																					a__:       Dict											=multiply(lowercase__     , lowercase__						)
														a__:       List[str]											=multiply(lowercase__     , lowercase__						)
														a__:       List[Any]											=int(n / 2						)
							return res_matrix[0][0]





def 						__lowerCamelCase       (      __magic_name__	:  int						):
							if n <= 1:
														return n
							a__:       List[Any]											=0
							a__:       List[str]											=1
							for _ in range(2     , n + 1						):
														a__							,			a__:       Optional[Any]											=fiba, fiba + fiba
							return fiba





def 						__lowerCamelCase       (      ):
							for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
														a__:       Dict											=int("".join(c for c in ordinal if c in "0123456789"						)						)  # 1000th --> 1000
														print(
														    F"{ordinal} fibonacci number using matrix exponentiation is "
														    F"{nth_fibonacci_matrix(lowercase__						)} and using bruteforce is "
														    F"{nth_fibonacci_bruteforce(lowercase__						)}\n"						)
							# from timeit import timeit
							# print(timeit("nth_fibonacci_matrix(1000000)",
							#              "from main import nth_fibonacci_matrix", number=5))
							# print(timeit("nth_fibonacci_bruteforce(1000000)",
							#              "from main import nth_fibonacci_bruteforce", number=5))
							# 2.3342058970001744
							# 57.256506615000035


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()










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 	lowerCamelCase__ (		_a   ):


						def __init__( self						:  str      ,       _a						:  UNetaDModel      ,       _a						:  CMStochasticIterativeScheduler			):
													super().__init__()

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

													a__:       Union[str, Any]											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  Any=0			):
													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"			)

													a__:       Optional[Any]											=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)

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

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


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Optional[int]=0			):
													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."			)

													a__:       Union[str, Any]											=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)

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

													if self.safety_checker is not None:
																				a__							,			a__:       Optional[Any]											=cpu_offload_with_hook(self.safety_checker      ,       _a      ,       prev_module_hook=_a			)

													# We'll offload the last model manually.
													a__:       Optional[Any]											=hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   _lowerCamelCase			( self						:  Tuple			):
													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


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  List[Any]      ,       _a						:  Optional[int]      ,       _a						:  Dict      ,       _a						:  List[Any]      ,       _a						:  List[str]      ,       _a						:  Union[str, Any]      ,       _a						:  str      ,       _a						:  int=None			):
													a__:       List[str]											=(batch_size, num_channels, height, width)
													if isinstance(_a      ,       _a			) and len(_a			) != batch_size:
																				raise ValueError(
																				    F"You have passed a list of generators of length {len(_a			)}, 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:
																				a__:       Dict											=randn_tensor(_a      ,       generator=_a      ,       device=_a      ,       dtype=_a			)
													else:
																				a__:       List[Any]											=latents.to(device=_a      ,       dtype=_a			)

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


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  torch.FloatTensor      ,       _a						:  str = "pil"			):
													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
													a__:       Tuple											=(sample / 2 + 0.5).clamp(0      ,       1			)
													if output_type == "pt":
																				return sample

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

													# Output_type must be 'pil'
													a__:       List[Any]											=self.numpy_to_pil(_a			)
													return sample


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


						def   _lowerCamelCase			( self						:  Union[str, Any]      ,       _a						:  Optional[Any]      ,       _a						:  Optional[int]      ,       _a						:  List[Any]      ,       _a						:  List[str]      ,       _a						:  Optional[int]      ,       _a						:  Optional[Any]			):
													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:
																				a__:       Tuple											=(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(_a      ,       _a			) or callback_steps <= 0)
													):
																				raise ValueError(
																				    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
																				    F" {type(_a			)}."			)







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

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

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

													# 3. Handle class_labels for class-conditional models
													a__:       int											=self.prepare_class_labels(_a      ,       _a      ,       class_labels=_a			)

													# 4. Prepare timesteps
													if timesteps is not None:
																				self.scheduler.set_timesteps(timesteps=_a      ,       device=_a			)
																				a__:       str											=self.scheduler.timesteps
																				a__:       List[str]											=len(_a			)
													else:
																				self.scheduler.set_timesteps(_a			)
																				a__:       Optional[int]											=self.scheduler.timesteps

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

																											a__:       int											=self.scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a			)[0]

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

        # 6. Post-process image sample
													a__:       List[str]											=self.postprocess_image(_a      ,       output_type=_a			)

													# 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=_a			)





