





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

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



@flax.struct.dataclass
class  _a  :






							"""simple docstring"""

							snake_case_							=    42
							snake_case_							=    42

							# setable values
							snake_case_							=    42
							snake_case_							=    42
							snake_case_							=    None

							@classmethod
							def   A_	(   cls					:  Optional[Any]				,						a					:  CommonSchedulerState				,						a					:  jnp.ndarray				,						a					:  jnp.ndarray				,						a					:  jnp.ndarray				,						)						->int:
													return cls(
													    common=a				,						final_alpha_cumprod=a				,						init_noise_sigma=a				,						timesteps=a				,						)







@dataclass
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    42







class  _a  (					lowercase__       ,				lowercase__				):






							"""simple docstring"""

							snake_case_							=    [e.name for e in FlaxKarrasDiffusionSchedulers]

							snake_case_							=    42

							@property
							def   A_	(   self					:  List[str]     )						->List[Any]:
													return True

							@register_to_config
							def __init__(   self					:  str				,						a					:  int = 10_00				,						a					:  float = 0.0001				,						a					:  float = 0.02				,						a					:  str = "linear"				,						a					:  Optional[jnp.ndarray] = None				,						a					:  bool = True				,						a					:  int = 0				,						a					:  str = "epsilon"				,						a					:  jnp.dtype = jnp.floataa				,						)						->Any:
													SCREAMING_SNAKE_CASE__  :					Tuple =       dtype

							def   A_	(   self					:  Union[str, Any]				,						a					:  Optional[CommonSchedulerState] = None     )						->DDIMSchedulerState:
													if common is None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       CommonSchedulerState.create(self     )

													# At every step in ddim, we are looking into the previous alphas_cumprod
													# For the final step, there is no previous alphas_cumprod because we are already at 0
													# `set_alpha_to_one` decides whether we set this parameter simply to one or
													# whether we use the final alpha of the "non-previous" one.
													SCREAMING_SNAKE_CASE__  :					Any =       (
													    jnp.array(1.0				,						dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
													)

													# standard deviation of the initial noise distribution
													SCREAMING_SNAKE_CASE__  :					Dict =       jnp.array(1.0				,						dtype=self.dtype     )

													SCREAMING_SNAKE_CASE__  :					Dict =       jnp.arange(0				,						self.config.num_train_timesteps     ).round()[::-1]

													return DDIMSchedulerState.create(
													    common=a				,						final_alpha_cumprod=a				,						init_noise_sigma=a				,						timesteps=a				,						)

							def   A_	(   self					:  str				,						a					:  DDIMSchedulerState				,						a					:  jnp.ndarray				,						a					:  Optional[int] = None     )						->jnp.ndarray:
													return sample

							def   A_	(   self					:  Any				,						a					:  DDIMSchedulerState				,						a					:  int				,						a					:  Tuple = ()     )						->DDIMSchedulerState:
													SCREAMING_SNAKE_CASE__  :					Dict =       self.config.num_train_timesteps // num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# rounding to avoid issues when num_inference_step is power of 3
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (jnp.arange(0				,						a     ) * step_ratio).round()[::-1] + self.config.steps_offset

													return state.replace(
													    num_inference_steps=a				,						timesteps=a				,						)

							def   A_	(   self					:  List[str]				,						a					:  DDIMSchedulerState				,						a					:  List[Any]				,						a					:  Any     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					str =       state.common.alphas_cumprod[timestep]
													SCREAMING_SNAKE_CASE__  :					Tuple =       jnp.where(
													    prev_timestep >= 0				,						state.common.alphas_cumprod[prev_timestep]				,						state.final_alpha_cumprod     )
													SCREAMING_SNAKE_CASE__  :					str =       1 - alpha_prod_t
													SCREAMING_SNAKE_CASE__  :					List[Any] =       1 - alpha_prod_t_prev

													SCREAMING_SNAKE_CASE__  :					str =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance

							def   A_	(   self					:  str				,						a					:  DDIMSchedulerState				,						a					:  jnp.ndarray				,						a					:  int				,						a					:  jnp.ndarray				,						a					:  float = 0.0				,						a					:  bool = True				,						)						->Union[FlaxDDIMSchedulerOutput, 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"     )

													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
													# Ideally, read DDIM paper in-detail understanding

													# Notation (<variable name> -> <name in paper>
													# - pred_noise_t -> e_theta(x_t, t)
													# - pred_original_sample -> f_theta(x_t, t) or x_0
													# - std_dev_t -> sigma_t
													# - eta -> η
													# - pred_sample_direction -> "direction pointing to x_t"
													# - pred_prev_sample -> "x_t-1"

													# 1. get previous step value (=t-1)
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       timestep - self.config.num_train_timesteps // state.num_inference_steps

													SCREAMING_SNAKE_CASE__  :					str =       state.common.alphas_cumprod
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       state.final_alpha_cumprod

													# 2. compute alphas, betas
													SCREAMING_SNAKE_CASE__  :					str =       alphas_cumprod[timestep]
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       jnp.where(prev_timestep >= 0				,						alphas_cumprod[prev_timestep]				,						a     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       1 - alpha_prod_t

													# 3. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													if self.config.prediction_type == "epsilon":
																			SCREAMING_SNAKE_CASE__  :					int =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model_output
													elif self.config.prediction_type == "sample":
																			SCREAMING_SNAKE_CASE__  :					str =       model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
													elif self.config.prediction_type == "v_prediction":
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			SCREAMING_SNAKE_CASE__  :					int =       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																			    " `v_prediction`"     )

													# 4. compute variance: "sigma_t(η)" -> see formula (16)
													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
													SCREAMING_SNAKE_CASE__  :					int =       self._get_variance(a				,						a				,						a     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       eta * variance ** 0.5

													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													SCREAMING_SNAKE_CASE__  :					Tuple =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

													# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													SCREAMING_SNAKE_CASE__  :					Any =       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

													if not return_dict:
																			return (prev_sample, state)

													return FlaxDDIMSchedulerOutput(prev_sample=a				,						state=a     )

							def   A_	(   self					:  Dict				,						a					:  DDIMSchedulerState				,						a					:  jnp.ndarray				,						a					:  jnp.ndarray				,						a					:  jnp.ndarray				,						)						->jnp.ndarray:
													return add_noise_common(state.common				,						a				,						a				,						a     )

							def   A_	(   self					:  Dict				,						a					:  DDIMSchedulerState				,						a					:  jnp.ndarray				,						a					:  jnp.ndarray				,						a					:  jnp.ndarray				,						)						->jnp.ndarray:
													return get_velocity_common(state.common				,						a				,						a				,						a     )






							def __len__(   self					:  Optional[int]     )						->str:
													return self.config.num_train_timesteps






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class  _a  :






							"""simple docstring"""

							snake_case_							=    ("_front", "_back", "_len")



							@dataclass
							class  _a  :






														"""simple docstring"""

														snake_case_							=    None
														snake_case_							=    None
														snake_case_							=    None







							class  _a  :






														"""simple docstring"""

														snake_case_							=    ("_cur",)

														def __init__(   self					:  Tuple				,						a					:  Deque._Node | None     )						->None:
																				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       cur

														def __iter__(   self					:  Dict     )						->Deque._Iterator:
																				return self






														def   A_	(   self					:  int     )						->Any:
																				if self._cur is None:
																										# finished iterating
																										raise StopIteration
																				SCREAMING_SNAKE_CASE__  :					Tuple =       self._cur.val
																				SCREAMING_SNAKE_CASE__  :					int =       self._cur.next_node

																				return val







							def __init__(   self					:  str				,						a					:  Iterable[Any] | None = None     )						->None:
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					int =       0

													if iterable is not None:
																			# append every value to the deque
																			for val in iterable:
																									self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					int =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       node
																			SCREAMING_SNAKE_CASE__  :					Any =       self._back
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node  # assign new back to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  List[str]				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					str =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Any =       self._front
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node
																			SCREAMING_SNAKE_CASE__  :					int =       node  # assign new front to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  Dict				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.appendleft(a     )

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._back
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._back.prev_node  # set new back
													# drop the last node - python will deallocate memory automatically
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					Any =       self._front
													SCREAMING_SNAKE_CASE__  :					Dict =       self._front.next_node  # set new front and drop the first node
													SCREAMING_SNAKE_CASE__  :					Dict =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  str     )						->bool:
													return self._front is None

							def __len__(   self					:  List[str]     )						->int:
													return self._len

							def __eq__(   self					:  List[Any]				,						a					:  object     )						->bool:

													if not isinstance(a				,						a     ):
																			return NotImplemented

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._front
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       other._front

													# if the length of the dequeues are not the same, they are not equal
													if len(self     ) != len(a     ):
																			return False

													while me is not None and oth is not None:
																			# compare every value
																			if me.val != oth.val:
																									return False
																			SCREAMING_SNAKE_CASE__  :					Any =       me.next_node
																			SCREAMING_SNAKE_CASE__  :					Any =       oth.next_node

													return True

							def __iter__(   self					:  Any     )						->Deque._Iterator:
													return Deque._Iterator(self._front     )






							def __repr__(   self					:  Any     )						->str:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
													SCREAMING_SNAKE_CASE__  :					int =       self._front
													while aux is not None:
																			# append the values in a list to display
																			values_list.append(aux.val     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       aux.next_node

													return f"""[{', '.join(repr(a     ) for val in values_list     )}]"""


if __name__ == "__main__":
				import doctest

				doctest.testmod()