






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 __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42

	# setable values
	UpperCamelCase__								=				42
	UpperCamelCase__								=				42
	UpperCamelCase__								=				42
	UpperCamelCase__								=				None

	# running values
	UpperCamelCase__								=				None

	@classmethod
	def 	UpperCamelCase   (							cls	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							return cls(common=UpperCAmelCase	,   init_noise_sigma=UpperCAmelCase	,   timesteps=UpperCAmelCase	,   sigmas=UpperCAmelCase   )





@dataclass
class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				snake_case__					,	snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				[e.name for e in FlaxKarrasDiffusionSchedulers]

	UpperCamelCase__								=				42

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							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	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dtype

	def 	UpperCamelCase   (							self	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							if common is None:
													_UpperCAmelCase     =      CommonSchedulerState.create(self   )

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

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

							return LMSDiscreteSchedulerState.create(
							    common=UpperCAmelCase	,   init_noise_sigma=UpperCAmelCase	,   timesteps=UpperCAmelCase	,   sigmas=UpperCAmelCase	,   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							((_UpperCAmelCase) ,			)     =      jnp.where(state.timesteps == timestep	,   size=1   )
							_UpperCAmelCase     =      step_index[0]

							_UpperCAmelCase     =      state.sigmas[step_index]
							_UpperCAmelCase     =      sample / ((sigma**2 + 1) ** 0.5)
							return sample

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							def lms_derivative(UpperCAmelCase   ):
													_UpperCAmelCase     =      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

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

							return integrated_coeff

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = ()   ):
							"""simple docstring"""







							_UpperCAmelCase     =      jnp.linspace(self.config.num_train_timesteps - 1	,   0	,   UpperCAmelCase	,   dtype=self.dtype   )

							_UpperCAmelCase     =      jnp.floor(UpperCAmelCase   ).astype(jnp.intaa   )
							_UpperCAmelCase     =      jnp.ceil(UpperCAmelCase   ).astype(jnp.intaa   )

							_UpperCAmelCase     =      jnp.mod(UpperCAmelCase	,   1.0   )

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

							_UpperCAmelCase     =      timesteps.astype(jnp.intaa   )

							# initial running values
							_UpperCAmelCase     =      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	,   ):
							"""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'   )

							_UpperCAmelCase     =      state.sigmas[timestep]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													_UpperCAmelCase     =      sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													_UpperCAmelCase     =      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
							_UpperCAmelCase     =      (sample - pred_original_sample) / sigma
							_UpperCAmelCase     =      state.replace(derivatives=jnp.append(state.derivatives	,   UpperCAmelCase   )   )
							if len(state.derivatives   ) > order:
													_UpperCAmelCase     =      state.replace(derivatives=jnp.delete(state.derivatives	,   0   )   )

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

							# 4. Compute previous sample based on the derivatives path
							_UpperCAmelCase     =      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	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      state.sigmas[timesteps].flatten()
							_UpperCAmelCase     =      broadcast_to_shape_from_left(UpperCAmelCase	,   noise.shape   )

							_UpperCAmelCase     =      original_samples + noise * sigma

							return noisy_samples

	def __len__(							self   ):
							"""simple docstring"""







							return self.config.num_train_timesteps








from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						if not inplace:
												_UpperCAmelCase     =      ma + ma
						else:
												ma += ma

						return ma


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      -1 * len(__lowerCAmelCase     )
						_UpperCAmelCase     =      list(range(len(tensor.shape[:zero_index]     )     )     )
						return tensor.permute(first_inds + [zero_index + i for i in inds]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						return t.reshape(t.shape[:-no_dims] + (-1,)     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 1E-4     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      mask.expand(*value.shape     )
						return torch.sum(mask * value			,		dim=__lowerCAmelCase     ) / (eps + torch.sum(__lowerCAmelCase			,		dim=__lowerCAmelCase     ))


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase = 2.31_25			,		__lowerCAmelCase = 21.68_75			,		__lowerCAmelCase = 64     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.linspace(__lowerCAmelCase			,		__lowerCAmelCase			,		no_bins - 1			,		device=pts.device     )
						_UpperCAmelCase     =      torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2			,		dim=-1     )     )
						return torch.bucketize(__lowerCAmelCase			,		__lowerCAmelCase     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						_UpperCAmelCase     =      dicts[0]
						_UpperCAmelCase     =      {}
						for k, v in first.items():
												_UpperCAmelCase     =      [d[k] for d in dicts]
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_multimap(__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      fn(__lowerCAmelCase     )

						return new_dict


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      v_bins.view(((1,) * len(x.shape     )) + (len(__lowerCAmelCase     ),)     )
						_UpperCAmelCase     =      x[..., None] - reshaped_bins
						_UpperCAmelCase     =      torch.argmin(torch.abs(__lowerCAmelCase     )			,		dim=-1     )
						return nn.functional.one_hot(__lowerCAmelCase			,		num_classes=len(__lowerCAmelCase     )     ).float()


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 0			,		__lowerCAmelCase = 0     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      []
						for i, s in enumerate(data.shape[:no_batch_dims]     ):
												_UpperCAmelCase     =      torch.arange(__lowerCAmelCase     )
												_UpperCAmelCase     =      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
												ranges.append(__lowerCAmelCase     )

						_UpperCAmelCase     =      [slice(__lowerCAmelCase     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
						_UpperCAmelCase     =      inds
						ranges.extend(__lowerCAmelCase     )
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(__lowerCAmelCase     )]


_a				     =  TypeVar('''T''')


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Dict[Any, Union[dict, list, tuple, Any]]:





						"""simple docstring"""



						_UpperCAmelCase     =      {}
						for k, v in dic.items():
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						return new_dict


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  list:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  tuple:





						"""simple docstring"""



						...


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return [tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree]
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return tuple(tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return fn(__lowerCAmelCase     )
						else:
												print(type(__lowerCAmelCase     )     )
												raise ValueError('Not supported'     )

_a				     =  partial(tree_map, leaf_type=torch.Tensor)
