




"""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__      (					lowerCamelCase_     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()

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

					snake_case						:  List[str]        =			None


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""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"							)

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

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

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""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						:  Union[str, Any]        =			torch.device(F'''cuda:{gpu_id}'''							)

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

					snake_case						:  List[str]        =			None
					for cpu_offloaded_model in [self.unet]:
							snake_case     ,			snake_case						:  Optional[int]        =			cpu_offload_with_hook(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

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

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


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



					"""simple docstring"""







					if not hasattr(self.unet			,	"_hf_hook"							):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(SCREAMING_SNAKE_CASE			,	"_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			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					snake_case						:  Any        =			(batch_size, num_channels, height, width)
					if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) != batch_size:
							raise ValueError(
							    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE							)}, 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						:  Any        =			randn_tensor(SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  Tuple        =			latents.to(device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)

					# scale the initial noise by the standard deviation required by the scheduler
					snake_case						:  Optional[int]        =			latents * self.scheduler.init_noise_sigma
					return latents


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"							):



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

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

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""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:
							snake_case						:  str        =			(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(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) or callback_steps <= 0)
					):
							raise ValueError(
							    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
							    F''' {type(SCREAMING_SNAKE_CASE							)}.'''							)



			@torch.no_grad()
			@replace_example_docstring(SCREAMING_SNAKE_CASE							)
			def __call__(       self			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.unet.config.sample_size
					snake_case						:  Dict        =			self._execution_device

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

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

					# 3. Handle class_labels for class-conditional models
					snake_case						:  Union[str, Any]        =			self.prepare_class_labels(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE							)

					# 4. Prepare timesteps
					if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			self.scheduler.timesteps
							snake_case						:  str        =			len(SCREAMING_SNAKE_CASE							)
					else:
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			self.scheduler.timesteps

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

									snake_case						:  List[Any]        =			self.scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)[0]

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

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

					# 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=SCREAMING_SNAKE_CASE							)







"""simple docstring"""

import matplotlib.pyplot as plt
import numpy as np



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Any        =			len(lowercase__				)  # Number of training samples
		snake_case						:  str        =			np.eye(lowercase__				)  # Initialize weights as identity matrix
		for j in range(lowercase__				):
				snake_case						:  Any        =			point - x_train[j]
				snake_case						:  Dict        =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

		return weights



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  str        =			weight_matrix(lowercase__    ,       lowercase__    ,       lowercase__				)
		snake_case						:  str        =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Optional[int]        =			np.zeros(len(lowercase__				)				)  # Initialize array of predictions
		for i, item in enumerate(lowercase__				):
				snake_case						:  Dict        =			item @ local_weight(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)

		return y_pred



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       str    ,       lowercase__					:       str				):
		import seaborn as sns

		snake_case						:  Union[str, Any]        =			sns.load_dataset(lowercase__				)
		snake_case						:  Any        =			np.array(data[x_name]				)
		snake_case						:  str        =			np.array(data[y_name]				)

		snake_case						:  Any        =			np.ones(len(lowercase__				)				)

		# pairing elements of one and x_data
		snake_case						:  Optional[int]        =			np.column_stack((one, x_data)				)

		return x_train, x_data, y_data



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       str    ,       lowercase__					:       str    ,       ):
		snake_case						:  List[str]        =			np.sort(lowercase__    ,       axis=0				)
		plt.scatter(lowercase__    ,       lowercase__    ,       color="blue"				)
		plt.plot(
		    x_train_sorted[:, 1]    ,       preds[x_train[:, 1].argsort(0				)]    ,       color="yellow"    ,       linewidth=5    ,       )
		plt.title("Local Weighted Regression"				)
		plt.xlabel(lowercase__				)
		plt.ylabel(lowercase__				)
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							__A						,    __A						,    __A							=     load_data("tips", "total_bill", "tip")
							__A							=     local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")

