
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		Optional[Any]=0.999	,						__lowerCAmelCase			:		str="cosine"	,						)    ->     Any:
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__lowerCAmelCase			:		int							):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__lowerCAmelCase			:		Any							):
												return math.exp(t * -12.0							)

				else:
								raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

				__lowerCamelCase					      =			[]
				for i in range(__lowerCAmelCase							):
								__lowerCamelCase					      =			i / num_diffusion_timesteps
								__lowerCamelCase					      =			(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase							) / alpha_bar_fn(__lowerCAmelCase							)	,						__lowerCAmelCase							)							)
				return torch.tensor(__lowerCAmelCase	,						dtype=torch.floataa							)







class      lowerCAmelCase__  (			__lowercase						,			__lowercase    ):
		a__ :		int   						=	[e.name for e in KarrasDiffusionSchedulers]
		a__ :		int   						=	2






		@register_to_config
		def __init__(	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							int = 10_00							,    SCREAMING_SNAKE_CASE__	:							float = 0.00085							,    SCREAMING_SNAKE_CASE__	:							float = 0.012							,    SCREAMING_SNAKE_CASE__	:							str = "linear"							,    SCREAMING_SNAKE_CASE__	:							Optional[Union[np.ndarray, List[float]]] = None							,    SCREAMING_SNAKE_CASE__	:							str = "epsilon"							,    SCREAMING_SNAKE_CASE__	:							str = "linspace"							,    SCREAMING_SNAKE_CASE__	:							int = 0							,    )  ->							List[Any]:
						if trained_betas is not None:
										__lowerCamelCase					      =			torch.tensor(SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						)
						elif beta_schedule == "linear":
										__lowerCamelCase					      =			torch.linspace(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						)
						elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										__lowerCamelCase					      =			(
										    torch.linspace(beta_start**0.5							,    beta_end**0.5							,    SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						) ** 2
										)
						elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										__lowerCamelCase					      =			betas_for_alpha_bar(SCREAMING_SNAKE_CASE__						)
						else:
										raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

						__lowerCamelCase					      =			1.0 - self.betas
						__lowerCamelCase					      =			torch.cumprod(self.alphas							,    dim=0						)

						#  set all values
						self.set_timesteps(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							List[str]=None						)  ->							str:
						if schedule_timesteps is None:
										__lowerCamelCase					      =			self.timesteps

						__lowerCamelCase					      =			(schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter						) == 0:
										__lowerCamelCase					      =			1 if len(SCREAMING_SNAKE_CASE__						) > 1 else 0
						else:
										__lowerCamelCase					      =			timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE__						) else timestep
										__lowerCamelCase					      =			self._index_counter[timestep_int]

						return indices[pos].item()






		@property
		def 				__A (	self	:							int						)  ->							int:
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
										return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5






		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Union[float, torch.FloatTensor]							,    )  ->							torch.FloatTensor:
						__lowerCamelCase					      =			self.index_for_timestep(SCREAMING_SNAKE_CASE__						)

						if self.state_in_first_order:
										__lowerCamelCase					      =			self.sigmas[step_index]
						else:
										__lowerCamelCase					      =			self.sigmas_interpol[step_index - 1]

						__lowerCamelCase					      =			sample / ((sigma**2 + 1) ** 0.5)
						return sample






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, torch.device] = None							,    SCREAMING_SNAKE_CASE__	:							Optional[int] = None							,    )  ->							Dict:
						__lowerCamelCase					      =			num_inference_steps

						__lowerCamelCase					      =			num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
										__lowerCamelCase					      =			np.linspace(0							,    num_train_timesteps - 1							,    SCREAMING_SNAKE_CASE__							,    dtype=SCREAMING_SNAKE_CASE__						)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
										__lowerCamelCase					      =			num_train_timesteps // self.num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# casting to int to avoid issues when num_inference_step is power of 3
										__lowerCamelCase					      =			(np.arange(0							,    SCREAMING_SNAKE_CASE__						) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE__						)
										timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
										__lowerCamelCase					      =			num_train_timesteps / self.num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# casting to int to avoid issues when num_inference_step is power of 3
										__lowerCamelCase					      =			(np.arange(SCREAMING_SNAKE_CASE__							,    0							,    -step_ratio						)).round().copy().astype(SCREAMING_SNAKE_CASE__						)
										timesteps -= 1
						else:
										raise ValueError(
										    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

						__lowerCamelCase					      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
						__lowerCamelCase					      =			torch.from_numpy(np.log(SCREAMING_SNAKE_CASE__						)						).to(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			np.interp(SCREAMING_SNAKE_CASE__							,    np.arange(0							,    len(SCREAMING_SNAKE_CASE__						)						)							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
						__lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(device=SCREAMING_SNAKE_CASE__						)

						# compute up and down sigmas
						__lowerCamelCase					      =			sigmas.roll(-1						)
						__lowerCamelCase					      =			0.0
						__lowerCamelCase					      =			(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
						__lowerCamelCase					      =			(sigmas_next**2 - sigmas_up**2) ** 0.5
						__lowerCamelCase					      =			0.0

						# compute interpolated sigmas
						__lowerCamelCase					      =			sigmas.log().lerp(sigmas_down.log()							,    0.5						).exp()
						__lowerCamelCase					      =			0.0

						# set sigmas
						__lowerCamelCase					      =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
						__lowerCamelCase					      =			torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)
						__lowerCamelCase					      =			torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2						), sigmas_up[-1:]]						)
						__lowerCamelCase					      =			torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2						), sigmas_down[-1:]]						)

						if str(SCREAMING_SNAKE_CASE__						).startswith('''mps'''						):
										# mps does not support float64
										__lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						)
						else:
										__lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			self.sigma_to_t(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__							,    dtype=timesteps.dtype						)
						__lowerCamelCase					      =			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])							,    dim=-1						).flatten()

						__lowerCamelCase					      =			torch.cat([timesteps[:1], interleaved_timesteps]						)

						__lowerCamelCase					      =			None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						__lowerCamelCase					      =			defaultdict(SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							List[Any]:
						# get log sigma
						__lowerCamelCase					      =			sigma.log()

						# get distribution
						__lowerCamelCase					      =			log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						__lowerCamelCase					      =			dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
						__lowerCamelCase					      =			low_idx + 1

						__lowerCamelCase					      =			self.log_sigmas[low_idx]
						__lowerCamelCase					      =			self.log_sigmas[high_idx]

						# interpolate sigmas
						__lowerCamelCase					      =			(low - log_sigma) / (low - high)
						__lowerCamelCase					      =			w.clamp(0							,    1						)

						# transform interpolation to time range
						__lowerCamelCase					      =			(1 - w) * low_idx + w * high_idx
						__lowerCamelCase					      =			t.view(sigma.shape						)
						return t






		@property
		def 				__A (	self	:							Dict						)  ->							Union[str, Any]:
						return self.sample is None






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[torch.FloatTensor, np.ndarray]							,    SCREAMING_SNAKE_CASE__	:							Union[float, torch.FloatTensor]							,    SCREAMING_SNAKE_CASE__	:							Union[torch.FloatTensor, np.ndarray]							,    SCREAMING_SNAKE_CASE__	:							Optional[torch.Generator] = None							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    )  ->							Union[SchedulerOutput, Tuple]:
						__lowerCamelCase					      =			self.index_for_timestep(SCREAMING_SNAKE_CASE__						)

						# advance index counter by 1
						__lowerCamelCase					      =			timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE__						) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
										__lowerCamelCase					      =			self.sigmas[step_index]
										__lowerCamelCase					      =			self.sigmas_interpol[step_index]
										__lowerCamelCase					      =			self.sigmas_up[step_index]
										__lowerCamelCase					      =			self.sigmas_down[step_index - 1]
						else:
										# 2nd order / KPDM2's method
										__lowerCamelCase					      =			self.sigmas[step_index - 1]
										__lowerCamelCase					      =			self.sigmas_interpol[step_index - 1]
										__lowerCamelCase					      =			self.sigmas_up[step_index - 1]
										__lowerCamelCase					      =			self.sigmas_down[step_index - 1]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						__lowerCamelCase					      =			model_output.device
						__lowerCamelCase					      =			randn_tensor(model_output.shape							,    dtype=model_output.dtype							,    device=SCREAMING_SNAKE_CASE__							,    generator=SCREAMING_SNAKE_CASE__						)

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										__lowerCamelCase					      =			sigma_hat if self.state_in_first_order else sigma_interpol
										__lowerCamelCase					      =			sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
										__lowerCamelCase					      =			sigma_hat if self.state_in_first_order else sigma_interpol
										__lowerCamelCase					      =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
										    sample / (sigma_input**2 + 1)
										)
						elif self.config.prediction_type == "sample":
										raise NotImplementedError('''prediction_type not implemented yet: sample'''						)
						else:
										raise ValueError(
										    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

						if self.state_in_first_order:
										# 2. Convert to an ODE derivative for 1st order
										__lowerCamelCase					      =			(sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										__lowerCamelCase					      =			sigma_interpol - sigma_hat

										# store for 2nd order step
										__lowerCamelCase					      =			sample
										__lowerCamelCase					      =			dt
										__lowerCamelCase					      =			sample + derivative * dt
						else:
										# DPM-Solver-2
										# 2. Convert to an ODE derivative for 2nd order
										__lowerCamelCase					      =			(sample - pred_original_sample) / sigma_interpol
										# 3. delta timestep
										__lowerCamelCase					      =			sigma_down - sigma_hat

										__lowerCamelCase					      =			self.sample
										__lowerCamelCase					      =			None

										__lowerCamelCase					      =			sample + derivative * dt
										__lowerCamelCase					      =			prev_sample + noise * sigma_up

						if not return_dict:
										return (prev_sample,)

						return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    )  ->							torch.FloatTensor:
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__lowerCamelCase					      =			self.sigmas.to(device=original_samples.device							,    dtype=original_samples.dtype						)
						if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE__						):
										# mps does not support float64
										__lowerCamelCase					      =			self.timesteps.to(original_samples.device							,    dtype=torch.floataa						)
										__lowerCamelCase					      =			timesteps.to(original_samples.device							,    dtype=torch.floataa						)
						else:
										__lowerCamelCase					      =			self.timesteps.to(original_samples.device						)
										__lowerCamelCase					      =			timesteps.to(original_samples.device						)

						__lowerCamelCase					      =			[self.index_for_timestep(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) for t in timesteps]

						__lowerCamelCase					      =			sigmas[step_indices].flatten()
						while len(sigma.shape						) < len(original_samples.shape						):
										__lowerCamelCase					      =			sigma.unsqueeze(-1						)

						__lowerCamelCase					      =			original_samples + noise * sigma
						return noisy_samples







		def __len__(	self	:							Tuple						)  ->							List[str]:
						return self.config.num_train_timesteps





from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AttentionProcessor

class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							int						)  ->							List[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    **SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							Optional[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							List[Any]							,    **SCREAMING_SNAKE_CASE__	:							List[str]						)  ->							List[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[int]							,    *SCREAMING_SNAKE_CASE__	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							int							,    **SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							List[str]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Tuple							,    **SCREAMING_SNAKE_CASE__	:							Any						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							int:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							Any							,    **SCREAMING_SNAKE_CASE__	:							str						)  ->							Dict:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)



