






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
	import transformers


def        _a  (     UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						random.seed(UpperCamelCase_   )
						np.random.seed(UpperCamelCase_   )
						torch.manual_seed(UpperCamelCase_   )
						torch.cuda.manual_seed_all(UpperCamelCase_   )
						# ^^ safe to call this function even if cuda is not available





class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 0.9_999		,    __UpperCAmelCase = 0.0		,    __UpperCAmelCase = 0		,    __UpperCAmelCase = False		,    __UpperCAmelCase = 1.0		,    __UpperCAmelCase = 2 / 3		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					Optional[Any]:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase		,    torch.nn.Module       ):
													lowerCAmelCase__       =						(
													    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
													    "Please pass the parameters of the module instead."
													)
													deprecate(
													    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"		,    "1.0.0"		,    __UpperCAmelCase		,    standard_warn=__UpperCAmelCase		,    )
													lowerCAmelCase__       =						parameters.parameters()

													# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
													lowerCAmelCase__       =						True

							if kwargs.get("max_value"		,    __UpperCAmelCase       ) is not None:
													lowerCAmelCase__       =						"The `max_value` argument is deprecated. Please use `decay` instead."
													deprecate("max_value"		,    "1.0.0"		,    __UpperCAmelCase		,    standard_warn=__UpperCAmelCase       )
													lowerCAmelCase__       =						kwargs["max_value"]

							if kwargs.get("min_value"		,    __UpperCAmelCase       ) is not None:
													lowerCAmelCase__       =						"The `min_value` argument is deprecated. Please use `min_decay` instead."
													deprecate("min_value"		,    "1.0.0"		,    __UpperCAmelCase		,    standard_warn=__UpperCAmelCase       )
													lowerCAmelCase__       =						kwargs["min_value"]

							lowerCAmelCase__       =						list(__UpperCAmelCase       )
							lowerCAmelCase__       =						[p.clone().detach() for p in parameters]

							if kwargs.get("device"		,    __UpperCAmelCase       ) is not None:
													lowerCAmelCase__       =						"The `device` argument is deprecated. Please use `to` instead."
													deprecate("device"		,    "1.0.0"		,    __UpperCAmelCase		,    standard_warn=__UpperCAmelCase       )
													self.to(device=kwargs["device"]       )

							lowerCAmelCase__       =						None

							lowerCAmelCase__       =						decay
							lowerCAmelCase__       =						min_decay
							lowerCAmelCase__       =						update_after_step
							lowerCAmelCase__       =						use_ema_warmup
							lowerCAmelCase__       =						inv_gamma
							lowerCAmelCase__       =						power
							lowerCAmelCase__       =						0
							lowerCAmelCase__       =						None  # set in `step()`

							lowerCAmelCase__       =						model_cls
							lowerCAmelCase__       =						model_config

	@classmethod
	def 		UpperCAmelCase  (							cls		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					"EMAModel":







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						model_cls.load_config(__UpperCAmelCase		,    return_unused_kwargs=__UpperCAmelCase       )
							lowerCAmelCase__       =						model_cls.from_pretrained(__UpperCAmelCase       )

							lowerCAmelCase__       =						cls(model.parameters()		,    model_cls=__UpperCAmelCase		,    model_config=model.config       )

							ema_model.load_state_dict(__UpperCAmelCase       )
							return ema_model

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					int:







							'''simple docstring'''





							if self.model_cls is None:
													raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."       )

							if self.model_config is None:
													raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."       )

							lowerCAmelCase__       =						self.model_cls.from_config(self.model_config       )
							lowerCAmelCase__       =						self.state_dict()
							state_dict.pop("shadow_params"		,    __UpperCAmelCase       )

							model.register_to_config(**__UpperCAmelCase       )
							self.copy_to(model.parameters()       )
							model.save_pretrained(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					float:







							'''simple docstring'''





							lowerCAmelCase__       =						max(0		,    optimization_step - self.update_after_step - 1       )

							if step <= 0:
													return 0.0

							if self.use_ema_warmup:
													lowerCAmelCase__       =						1 - (1 + step / self.inv_gamma) ** -self.power
							else:
													lowerCAmelCase__       =						(1 + step) / (10 + step)

							lowerCAmelCase__       =						min(__UpperCAmelCase		,    self.decay       )
							# make sure decay is not smaller than min_decay
							lowerCAmelCase__       =						max(__UpperCAmelCase		,    self.min_decay       )
							return cur_decay_value

	@torch.no_grad()
	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase		,    torch.nn.Module       ):
													lowerCAmelCase__       =						(
													    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
													    "Please pass the parameters of the module instead."
													)
													deprecate(
													    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"		,    "1.0.0"		,    __UpperCAmelCase		,    standard_warn=__UpperCAmelCase		,    )
													lowerCAmelCase__       =						parameters.parameters()

							lowerCAmelCase__       =						list(__UpperCAmelCase       )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							lowerCAmelCase__       =						self.get_decay(self.optimization_step       )
							lowerCAmelCase__       =						decay
							lowerCAmelCase__       =						1 - decay

							lowerCAmelCase__       =						contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													import deepspeed

							for s_param, param in zip(self.shadow_params		,    __UpperCAmelCase       ):
													if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																			lowerCAmelCase__       =						deepspeed.zero.GatheredParameters(__UpperCAmelCase		,    modifier_rank=__UpperCAmelCase       )

													with context_manager():
																			if param.requires_grad:
																									s_param.sub_(one_minus_decay * (s_param - param)       )
																			else:
																									s_param.copy_(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					None:







							'''simple docstring'''





							lowerCAmelCase__       =						list(__UpperCAmelCase       )
							for s_param, param in zip(self.shadow_params		,    __UpperCAmelCase       ):
													param.data.copy_(s_param.to(param.device       ).data       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None       )->					None:







							'''simple docstring'''





							lowerCAmelCase__       =						[
							    p.to(device=__UpperCAmelCase		,    dtype=__UpperCAmelCase       ) if p.is_floating_point() else p.to(device=__UpperCAmelCase       )
							    for p in self.shadow_params
							]

	def 		UpperCAmelCase  (							self       )->					dict:







							'''simple docstring'''





							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					None:







							'''simple docstring'''





							lowerCAmelCase__       =						[param.detach().cpu().clone() for param in parameters]

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					None:







							'''simple docstring'''





							if self.temp_stored_params is None:
													raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"       )
							for c_param, param in zip(self.temp_stored_params		,    __UpperCAmelCase       ):
													param.data.copy_(c_param.data       )

							# Better memory-wise.
							lowerCAmelCase__       =						None


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					None:







							'''simple docstring'''





							lowerCAmelCase__       =						copy.deepcopy(__UpperCAmelCase       )

							lowerCAmelCase__       =						state_dict.get("decay"		,    self.decay       )
							if self.decay < 0.0 or self.decay > 1.0:
													raise ValueError("Decay must be between 0 and 1"       )

							lowerCAmelCase__       =						state_dict.get("min_decay"		,    self.min_decay       )
							if not isinstance(self.min_decay		,    __UpperCAmelCase       ):
													raise ValueError("Invalid min_decay"       )

							lowerCAmelCase__       =						state_dict.get("optimization_step"		,    self.optimization_step       )
							if not isinstance(self.optimization_step		,    __UpperCAmelCase       ):
													raise ValueError("Invalid optimization_step"       )

							lowerCAmelCase__       =						state_dict.get("update_after_step"		,    self.update_after_step       )
							if not isinstance(self.update_after_step		,    __UpperCAmelCase       ):
													raise ValueError("Invalid update_after_step"       )

							lowerCAmelCase__       =						state_dict.get("use_ema_warmup"		,    self.use_ema_warmup       )
							if not isinstance(self.use_ema_warmup		,    __UpperCAmelCase       ):
													raise ValueError("Invalid use_ema_warmup"       )

							lowerCAmelCase__       =						state_dict.get("inv_gamma"		,    self.inv_gamma       )
							if not isinstance(self.inv_gamma		,    (float, int)       ):
													raise ValueError("Invalid inv_gamma"       )

							lowerCAmelCase__       =						state_dict.get("power"		,    self.power       )
							if not isinstance(self.power		,    (float, int)       ):
													raise ValueError("Invalid power"       )

							lowerCAmelCase__       =						state_dict.get("shadow_params"		,    __UpperCAmelCase       )
							if shadow_params is not None:
													lowerCAmelCase__       =						shadow_params
													if not isinstance(self.shadow_params		,    __UpperCAmelCase       ):
																			raise ValueError("shadow_params must be a list"       )
													if not all(isinstance(__UpperCAmelCase		,    torch.Tensor       ) for p in self.shadow_params       ):
																			raise ValueError("shadow_params must all be Tensors"       )














from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





							super().__init__()
							self.register_modules(transformer=__UpperCAmelCase		,    vae=__UpperCAmelCase		,    scheduler=__UpperCAmelCase       )

							# create a imagenet -> id dictionary for easier use
							lowerCAmelCase__       =						{}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(","       ):
																									lowerCAmelCase__       =						int(__UpperCAmelCase       )
													lowerCAmelCase__       =						dict(sorted(self.labels.items()       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[int]:







							'''simple docstring'''





							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						list(__UpperCAmelCase       )

							for l in label:
													if l not in self.labels:
																			raise ValueError(
																			    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."       )

							return [self.labels[l] for l in label]


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 4.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.transformer.config.sample_size
							lowerCAmelCase__       =						self.transformer.config.in_channels

							lowerCAmelCase__       =						randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=self.transformer.dtype		,    )
							lowerCAmelCase__       =						torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

							lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    device=self.device       ).reshape(-1       )
							lowerCAmelCase__       =						torch.tensor([1000] * batch_size		,    device=self.device       )
							lowerCAmelCase__       =						torch.cat([class_labels, class_null]		,    0       ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													if guidance_scale > 1:
																			lowerCAmelCase__       =						latent_model_input[: len(__UpperCAmelCase       ) // 2]
																			lowerCAmelCase__       =						torch.cat([half, half]		,    dim=0       )
													lowerCAmelCase__       =						self.scheduler.scale_model_input(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						t
													if not torch.is_tensor(__UpperCAmelCase       ):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			lowerCAmelCase__       =						latent_model_input.device.type == "mps"
																			if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									lowerCAmelCase__       =						torch.floataa if is_mps else torch.floataa
																			else:
																									lowerCAmelCase__       =						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__       =						torch.tensor([timesteps]		,    dtype=__UpperCAmelCase		,    device=latent_model_input.device       )
													elif len(timesteps.shape       ) == 0:
																			lowerCAmelCase__       =						timesteps[None].to(latent_model_input.device       )
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													lowerCAmelCase__       =						timesteps.expand(latent_model_input.shape[0]       )
													# predict noise model_output
													lowerCAmelCase__       =						self.transformer(
													    __UpperCAmelCase		,    timestep=__UpperCAmelCase		,    class_labels=__UpperCAmelCase       ).sample

													# perform guidance
													if guidance_scale > 1:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    len(__UpperCAmelCase       ) // 2		,    dim=0       )

																			lowerCAmelCase__       =						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__       =						torch.cat([half_eps, half_eps]		,    dim=0       )

																			lowerCAmelCase__       =						torch.cat([eps, rest]		,    dim=1       )

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    __UpperCAmelCase		,    dim=1       )
													else:
																			lowerCAmelCase__       =						noise_pred

													# compute previous image: x_t -> x_t-1
													lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ).prev_sample

							if guidance_scale > 1:
													lowerCAmelCase__      ,							lowerCAmelCase__       =						latent_model_input.chunk(2		,    dim=0       )
							else:
													lowerCAmelCase__       =						latent_model_input

							lowerCAmelCase__       =						1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

							lowerCAmelCase__       =						(samples / 2 + 0.5).clamp(0		,    1       )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__       =						samples.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )






