



'''simple docstring'''



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        UpperCAmelCase__						:
					a   :  CommonSchedulerState

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

					# running values
					a   :  Optional[jnp.ndarray]   =				None



					@classmethod
					def 							UpperCAmelCase_			(     cls      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Any:
						return cls(common=UpperCamelCase      ,					init_noise_sigma=UpperCamelCase      ,					timesteps=UpperCamelCase      ,					sigmas=UpperCamelCase  )






@dataclass
class        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  LMSDiscreteSchedulerState






class        UpperCAmelCase__						(		UpperCamelCase__				,							UpperCamelCase__			):
					a   :  List[str]   =				[e.name for e in FlaxKarrasDiffusionSchedulers]

					a   :  jnp.dtype



					@property
					def 							UpperCAmelCase_			(     self  )  ->      int:
						return True



					@register_to_config
					def __init__(     self      ,					UpperCamelCase = 1000      ,					UpperCamelCase = 0.00_01      ,					UpperCamelCase = 0.02      ,					UpperCamelCase = "linear"      ,					UpperCamelCase = None      ,					UpperCamelCase = "epsilon"      ,					UpperCamelCase = jnp.floataa      ,					)  ->      int:
						__lowerCAmelCase       				=	dtype



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase = None  )  ->      LMSDiscreteSchedulerState:
						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=UpperCamelCase      ,					init_noise_sigma=UpperCamelCase      ,					timesteps=UpperCamelCase      ,					sigmas=UpperCamelCase      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      jnp.ndarray:
						((__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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Optional[Any]:

						def lms_derivative(UpperCamelCase  ):
							__lowerCAmelCase       				=	1.0
							for k in range(UpperCamelCase  ):
								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(UpperCamelCase      ,					state.sigmas[t]      ,					state.sigmas[t + 1]      ,					epsrel=1E-4  )[0]

						return integrated_coeff



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = ()  )  ->      LMSDiscreteSchedulerState:
						__lowerCAmelCase       				=	jnp.linspace(self.config.num_train_timesteps - 1      ,					0      ,					UpperCamelCase      ,					dtype=self.dtype  )

						__lowerCAmelCase       				=	jnp.floor(UpperCamelCase  ).astype(jnp.intaa  )
						__lowerCAmelCase       				=	jnp.ceil(UpperCamelCase  ).astype(jnp.intaa  )

						__lowerCAmelCase       				=	jnp.mod(UpperCamelCase      ,					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=UpperCamelCase      ,					sigmas=UpperCamelCase      ,					num_inference_steps=UpperCamelCase      ,					derivatives=UpperCamelCase      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = 4      ,					UpperCamelCase = True      ,					)  ->      Union[FlaxLMSSchedulerOutput, Tuple]:
						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      ,					UpperCamelCase  )  )
						if len(state.derivatives  ) > order:
							__lowerCAmelCase       				=	state.replace(derivatives=jnp.delete(state.derivatives      ,					0  )  )

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

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

						if not return_dict:
							return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase      ,					state=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					)  ->      jnp.ndarray:
						__lowerCAmelCase       				=	state.sigmas[timesteps].flatten()
						__lowerCAmelCase       				=	broadcast_to_shape_from_left(UpperCamelCase      ,					noise.shape  )

						__lowerCAmelCase       				=	original_samples + noise * sigma

						return noisy_samples



					def __len__(     self  )  ->      str:
						return self.config.num_train_timesteps




'''simple docstring'''



lowerCAmelCase						:		Tuple						      = '''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

lowerCAmelCase						:		Tuple						      = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]