




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 SchedulerMixin


__magic_name__     :     Optional[int]  							=					logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			torch.FloatTensor





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   1
     @register_to_config
     def __init__(	self  :		List[str]					,	__lowerCamelCase  :		int = 40					,	__lowerCamelCase  :		float = 0.002					,	__lowerCamelCase  :		float = 80.0					,	__lowerCamelCase  :		float = 0.5					,	__lowerCamelCase  :		float = 1.0					,	__lowerCamelCase  :		float = 7.0					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       # standard deviation of the initial noise distribution
       lowerCAmelCase__								=			sigma_max

       lowerCAmelCase__								=			np.linspace(0					,	1					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self._convert_to_karras(__lowerCamelCase	)
       lowerCAmelCase__								=			self.sigma_to_t(__lowerCamelCase	)

       # setable values
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	)
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	)
       lowerCAmelCase__								=			False
       lowerCAmelCase__								=			False
     def 					A__			(	self  :		Any					,	__lowerCamelCase  :		Optional[Any]					,	__lowerCamelCase  :		Optional[Any]=None	):



       """simple docstring"""
       if schedule_timesteps is None:
         lowerCAmelCase__								=			self.timesteps

       lowerCAmelCase__								=			(schedule_timesteps == timestep).nonzero()
       return indices.item()
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Union[float, torch.FloatTensor]	):



       """simple docstring"""
       # Get sigma corresponding to timestep
       if isinstance(__lowerCamelCase					,	torch.Tensor	):
         lowerCAmelCase__								=			timestep.to(self.timesteps.device	)
       lowerCAmelCase__								=			self.index_for_timestep(__lowerCamelCase	)
       lowerCAmelCase__								=			self.sigmas[step_idx]

       lowerCAmelCase__								=			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       lowerCAmelCase__								=			True
       return sample
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		Union[float, np.ndarray]	):



       """simple docstring"""
       if not isinstance(__lowerCamelCase					,	np.ndarray	):
         lowerCAmelCase__								=			np.array(__lowerCamelCase					,	dtype=np.floataa	)

       lowerCAmelCase__								=			10_00 * 0.25 * np.log(sigmas + 1e-44	)

       return timesteps
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		Optional[int] = None					,	__lowerCamelCase  :		Union[str, torch.device] = None					,	__lowerCamelCase  :		Optional[List[int]] = None					,	):



       """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:
         raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''	)

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
         for i in range(1					,	len(__lowerCamelCase	)	):
           if timesteps[i] >= timesteps[i - 1]:
             raise ValueError('''`timesteps` must be in descending order.'''	)

         if timesteps[0] >= self.config.num_train_timesteps:
           raise ValueError(
               F"""`timesteps` must start before `self.config.train_timesteps`:"""
               F""" {self.config.num_train_timesteps}."""	)

         lowerCAmelCase__								=			np.array(__lowerCamelCase					,	dtype=np.intaa	)
         lowerCAmelCase__								=			True
       else:
         if num_inference_steps > self.config.num_train_timesteps:
           raise ValueError(
               F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
               F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
               F""" maximal {self.config.num_train_timesteps} timesteps."""	)

         lowerCAmelCase__								=			num_inference_steps

         lowerCAmelCase__								=			self.config.num_train_timesteps // self.num_inference_steps
         lowerCAmelCase__								=			(np.arange(0					,	__lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
         lowerCAmelCase__								=			False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       lowerCAmelCase__								=			self.config.num_train_timesteps
       lowerCAmelCase__								=			timesteps[::-1].copy()
       lowerCAmelCase__								=			ramp / (num_train_timesteps - 1)
       lowerCAmelCase__								=			self._convert_to_karras(__lowerCamelCase	)
       lowerCAmelCase__								=			self.sigma_to_t(__lowerCamelCase	)

       lowerCAmelCase__								=			np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	).to(device=__lowerCamelCase	)

       if str(__lowerCamelCase	).startswith('''mps'''	):
         # mps does not support float64
         lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	).to(__lowerCamelCase					,	dtype=torch.floataa	)
       else:
         lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	).to(device=__lowerCamelCase	)
     def 					A__			(	self  :		int					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			self.config.sigma_min
       lowerCAmelCase__								=			self.config.sigma_max

       lowerCAmelCase__								=			self.config.rho
       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  :		Union[str, Any]					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.config.sigma_data

       lowerCAmelCase__								=			sigma_data**2 / (sigma**2 + sigma_data**2)
       lowerCAmelCase__								=			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		Dict	):



       """simple docstring"""
       lowerCAmelCase__								=			self.config.sigma_min
       lowerCAmelCase__								=			self.config.sigma_data

       lowerCAmelCase__								=			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       lowerCAmelCase__								=			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Union[float, torch.FloatTensor]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Optional[torch.Generator] = None					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""

       if (
           isinstance(__lowerCamelCase					,	__lowerCamelCase	)
           or isinstance(__lowerCamelCase					,	torch.IntTensor	)
           or isinstance(__lowerCamelCase					,	torch.LongTensor	)
       ):
         raise ValueError(
             (
                 '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                 F""" `{self.__class__}.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(__lowerCamelCase					,	torch.Tensor	):
         lowerCAmelCase__								=			timestep.to(self.timesteps.device	)

       lowerCAmelCase__								=			self.config.sigma_min
       lowerCAmelCase__								=			self.config.sigma_max

       lowerCAmelCase__								=			self.index_for_timestep(__lowerCamelCase	)

       # sigma_next corresponds to next_t in original implementation
       lowerCAmelCase__								=			self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
         lowerCAmelCase__								=			self.sigmas[step_index + 1]
       else:
         # Set sigma_next to sigma_min
         lowerCAmelCase__								=			self.sigmas[-1]

       # Get scalings for boundary conditions
       lowerCAmelCase__							,			lowerCAmelCase__								=			self.get_scalings_for_boundary_condition(__lowerCamelCase	)

       # 1. Denoise model output using boundary conditions
       lowerCAmelCase__								=			c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
         lowerCAmelCase__								=			denoised.clamp(-1					,	1	)

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps	) > 1:
         lowerCAmelCase__								=			randn_tensor(
             model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__lowerCamelCase	)
       else:
         lowerCAmelCase__								=			torch.zeros_like(__lowerCamelCase	)
       lowerCAmelCase__								=			noise * self.config.s_noise

       lowerCAmelCase__								=			sigma_next.clamp(min=__lowerCamelCase					,	max=__lowerCamelCase	)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       lowerCAmelCase__								=			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
         return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=__lowerCamelCase	)
     def 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		torch.FloatTensor					,	):



       """simple docstring"""
       # 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(__lowerCamelCase	):
         # 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  :		List[str]	):



       """simple docstring"""
       return self.config.num_train_timesteps







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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 						SCREAMING_SNAKE_CASE__		:
     lowercase_   :			CommonSchedulerState

     # setable values
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			Optional[int]   =   None

     # running values
     lowercase_   :			Optional[jnp.ndarray]   =   None
     @classmethod
     def 					A__			(	cls  :		List[str]					,	__lowerCamelCase  :		CommonSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray	):



       """simple docstring"""
       return cls(common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase	)





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			LMSDiscreteSchedulerState





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase_   :			jnp.dtype
     @property
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       return True
     @register_to_config
     def __init__(	self  :		int					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[jnp.ndarray] = None					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		jnp.dtype = jnp.floataa					,	):



       """simple docstring"""
       lowerCAmelCase__								=			dtype
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Optional[CommonSchedulerState] = None	):



       """simple docstring"""
       if common is None:
         lowerCAmelCase__								=			CommonSchedulerState.create(self	)

       lowerCAmelCase__								=			jnp.arange(0					,	self.config.num_train_timesteps	).round()[::-1]
       lowerCAmelCase__								=			((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowerCAmelCase__								=			sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       ((lowerCAmelCase__)							,			)								=			jnp.where(state.timesteps == timestep					,	size=1	)
       lowerCAmelCase__								=			step_index[0]

       lowerCAmelCase__								=			state.sigmas[step_index]
       lowerCAmelCase__								=			sample / ((sigma**2 + 1) ** 0.5)
       return sample
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""

       def lms_derivative(__lowerCamelCase  :		int	):
         lowerCAmelCase__								=			1.0
         for k in range(__lowerCamelCase	):
           if current_order == k:
             continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

       lowerCAmelCase__								=			integrate.quad(__lowerCamelCase					,	state.sigmas[t]					,	state.sigmas[t + 1]					,	epsrel=1e-4	)[0]

       return integrated_coeff
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple = ()	):



       """simple docstring"""
       lowerCAmelCase__								=			jnp.linspace(self.config.num_train_timesteps - 1					,	0					,	__lowerCamelCase					,	dtype=self.dtype	)

       lowerCAmelCase__								=			jnp.floor(__lowerCamelCase	).astype(jnp.intaa	)
       lowerCAmelCase__								=			jnp.ceil(__lowerCamelCase	).astype(jnp.intaa	)

       lowerCAmelCase__								=			jnp.mod(__lowerCamelCase					,	1.0	)

       lowerCAmelCase__								=			((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowerCAmelCase__								=			(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowerCAmelCase__								=			jnp.concatenate([sigmas, jnp.array([0.0]					,	dtype=self.dtype	)]	)

       lowerCAmelCase__								=			timesteps.astype(jnp.intaa	)

       # initial running values
       lowerCAmelCase__								=			jnp.zeros((0,) + shape					,	dtype=self.dtype	)

       return state.replace(
           timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	num_inference_steps=__lowerCamelCase					,	derivatives=__lowerCamelCase					,	)
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int = 4					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       if state.num_inference_steps is None:
         raise ValueError(
             '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''	)

       lowerCAmelCase__								=			state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         lowerCAmelCase__								=			sample - sigma * 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
       lowerCAmelCase__								=			state.replace(derivatives=jnp.append(state.derivatives					,	__lowerCamelCase	)	)
       if len(state.derivatives	) > order:
         lowerCAmelCase__								=			state.replace(derivatives=jnp.delete(state.derivatives					,	0	)	)

       # 3. Compute linear multistep coefficients
       lowerCAmelCase__								=			min(timestep + 1					,	__lowerCamelCase	)
       lowerCAmelCase__								=			[self.get_lms_coefficient(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	) for curr_order in range(__lowerCamelCase	)]

       # 4. Compute previous sample based on the derivatives path
       lowerCAmelCase__								=			sample + sum(
           coeff * derivative for coeff, derivative in zip(__lowerCamelCase					,	reversed(state.derivatives	)	)	)

       if not return_dict:
         return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase					,	state=__lowerCamelCase	)
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       lowerCAmelCase__								=			state.sigmas[timesteps].flatten()
       lowerCAmelCase__								=			broadcast_to_shape_from_left(__lowerCamelCase					,	noise.shape	)

       lowerCAmelCase__								=			original_samples + noise * sigma

       return noisy_samples



     def __len__(	self  :		str	):



       """simple docstring"""
       return self.config.num_train_timesteps

