
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		torch.FloatTensor
		a__ :		Optional[torch.FloatTensor]   						=	None





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		str=0.999	,						__lowerCAmelCase			:		str="cosine"	,						)    ->     Any:
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__lowerCAmelCase			:		List[Any]							):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__lowerCAmelCase			:		Dict							):
												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__ :		Any   						=	[e.name for e in KarrasDiffusionSchedulers]
		a__ :		Optional[Any]   						=	1






		@register_to_config
		def __init__(	self	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							int = 10_00							,    SCREAMING_SNAKE_CASE__	:							float = 0.0001							,    SCREAMING_SNAKE_CASE__	:							float = 0.02							,    SCREAMING_SNAKE_CASE__	:							str = "linear"							,    SCREAMING_SNAKE_CASE__	:							Optional[Union[np.ndarray, List[float]]] = None							,    SCREAMING_SNAKE_CASE__	:							str = "epsilon"							,    SCREAMING_SNAKE_CASE__	:							str = "linear"							,    SCREAMING_SNAKE_CASE__	:							Optional[bool] = False							,    SCREAMING_SNAKE_CASE__	:							str = "linspace"							,    SCREAMING_SNAKE_CASE__	:							int = 0							,    )  ->							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						)

						__lowerCamelCase					      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
						__lowerCamelCase					      =			np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
						__lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						)

						# setable values
						__lowerCamelCase					      =			None
						__lowerCamelCase					      =			np.linspace(0							,    num_train_timesteps - 1							,    SCREAMING_SNAKE_CASE__							,    dtype=SCREAMING_SNAKE_CASE__						)[::-1].copy()
						__lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			False
						__lowerCamelCase					      =			use_karras_sigmas






		@property
		def 				__A (	self	:							Union[str, Any]						)  ->							Optional[Any]:
						# 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	:							int							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Union[float, torch.FloatTensor]						)  ->							torch.FloatTensor:
						if isinstance(SCREAMING_SNAKE_CASE__							,    torch.Tensor						):
										__lowerCamelCase					      =			timestep.to(self.timesteps.device						)
						__lowerCamelCase					      =			(self.timesteps == timestep).nonzero().item()
						__lowerCamelCase					      =			self.sigmas[step_index]

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

						__lowerCamelCase					      =			True
						return sample






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

						# "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							,    self.config.num_train_timesteps - 1							,    SCREAMING_SNAKE_CASE__							,    dtype=SCREAMING_SNAKE_CASE__						)[
										    ::-1
										].copy()
						elif self.config.timestep_spacing == "leading":
										__lowerCamelCase					      =			self.config.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					      =			self.config.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(self.config.num_train_timesteps							,    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					      =			np.log(SCREAMING_SNAKE_CASE__						)

						if self.config.interpolation_type == "linear":
										__lowerCamelCase					      =			np.interp(SCREAMING_SNAKE_CASE__							,    np.arange(0							,    len(SCREAMING_SNAKE_CASE__						)						)							,    SCREAMING_SNAKE_CASE__						)
						elif self.config.interpolation_type == "log_linear":
										__lowerCamelCase					      =			torch.linspace(np.log(sigmas[-1]						)							,    np.log(sigmas[0]						)							,    num_inference_steps + 1						).exp()
						else:
										raise ValueError(
										    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
										    ''' \'linear\' or \'log_linear\''''						)

						if self.use_karras_sigmas:
										__lowerCamelCase					      =			self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE__							,    num_inference_steps=self.num_inference_steps						)
										__lowerCamelCase					      =			np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) for sigma in sigmas]						)

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






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

						# get distribution
						__lowerCamelCase					      =			log_sigma - log_sigmas[:, np.newaxis]

						# get sigmas range
						__lowerCamelCase					      =			np.cumsum((dists >= 0)							,    axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
						__lowerCamelCase					      =			low_idx + 1

						__lowerCamelCase					      =			log_sigmas[low_idx]
						__lowerCamelCase					      =			log_sigmas[high_idx]

						# interpolate sigmas
						__lowerCamelCase					      =			(low - log_sigma) / (low - high)
						__lowerCamelCase					      =			np.clip(SCREAMING_SNAKE_CASE__							,    0							,    1						)

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






		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							torch.FloatTensor:
						__lowerCamelCase					      =			in_sigmas[-1].item()
						__lowerCamelCase					      =			in_sigmas[0].item()

						__lowerCamelCase					      =			7.0  # 7.0 is the value used in the paper
						__lowerCamelCase					      =			np.linspace(0							,    1							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			sigma_min ** (1 / rho)
						__lowerCamelCase					      =			sigma_max ** (1 / rho)
						__lowerCamelCase					      =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas






		def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Union[float, torch.FloatTensor]							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							float = 0.0							,    SCREAMING_SNAKE_CASE__	:							float = 0.0							,    SCREAMING_SNAKE_CASE__	:							float = float('''inf'''						)							,    SCREAMING_SNAKE_CASE__	:							float = 1.0							,    SCREAMING_SNAKE_CASE__	:							Optional[torch.Generator] = None							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    )  ->							Union[EulerDiscreteSchedulerOutput, Tuple]:

						if (
						    isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						    or isinstance(SCREAMING_SNAKE_CASE__							,    torch.IntTensor						)
						    or isinstance(SCREAMING_SNAKE_CASE__							,    torch.LongTensor						)
						):
										raise ValueError(
										    (
										        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
										        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
										        ''' one of the `scheduler.timesteps` as a timestep.'''
										    )							,    )

						if not self.is_scale_input_called:
										logger.warning(
										    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
										    '''See `StableDiffusionPipeline` for a usage example.'''						)

						if isinstance(SCREAMING_SNAKE_CASE__							,    torch.Tensor						):
										__lowerCamelCase					      =			timestep.to(self.timesteps.device						)

						__lowerCamelCase					      =			(self.timesteps == timestep).nonzero().item()
						__lowerCamelCase					      =			self.sigmas[step_index]

						__lowerCamelCase					      =			min(s_churn / (len(self.sigmas						) - 1)							,    2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

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

						__lowerCamelCase					      =			noise * s_noise
						__lowerCamelCase					      =			sigma * (gamma + 1)

						if gamma > 0:
										__lowerCamelCase					      =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						# NOTE: "original_sample" should not be an expected prediction_type but is left in for
						# backwards compatibility
						if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
										__lowerCamelCase					      =			model_output
						elif self.config.prediction_type == "epsilon":
										__lowerCamelCase					      =			sample - sigma_hat * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										__lowerCamelCase					      =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

						# 2. Convert to an ODE derivative
						__lowerCamelCase					      =			(sample - pred_original_sample) / sigma_hat

						__lowerCamelCase					      =			self.sigmas[step_index + 1] - sigma_hat

						__lowerCamelCase					      =			sample + derivative * dt

						if not return_dict:
										return (prev_sample,)

						return EulerDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__							,    pred_original_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					      =			[(schedule_timesteps == t).nonzero().item() 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	:							int						)  ->							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__						)



