




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					42

				# setable values
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					42
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					42
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					None


				@classmethod
				def     _A							(							cls:   Optional[int]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Union[str, Any]		):
											return cls(common=_lowerCamelCase		,					init_noise_sigma=_lowerCamelCase		,					timesteps=_lowerCamelCase		)






@dataclass
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					42






class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					[e.name for e in FlaxKarrasDiffusionSchedulers]

				SCREAMING_SNAKE_CASE__					:							Tuple	      =					42


				@property
				def     _A							(							self:   Optional[Any]		):
											return True


				@register_to_config
				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Optional[Any] = 10_00		,					_lowerCamelCase:   List[Any] = 0.00_01		,					_lowerCamelCase:   Union[str, Any] = 0.02		,					_lowerCamelCase:   Any = "linear"		,					_lowerCamelCase:   int = None		,					_lowerCamelCase:   int = "fixed_small"		,					_lowerCamelCase:   Optional[Any] = True		,					_lowerCamelCase:   str = "epsilon"		,					_lowerCamelCase:   Tuple = jnp.floataa		,					):
											SCREAMING_SNAKE_CASE_   	=    dtype


				def     _A							(							self:   str		,					_lowerCamelCase:   Optional[Any] = None		):

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

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

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

											return DDPMSchedulerState.create(
											    common=_lowerCamelCase		,					init_noise_sigma=_lowerCamelCase		,					timesteps=_lowerCamelCase		,					)


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   str = None		):
											return sample


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Optional[int] = ()		):
											SCREAMING_SNAKE_CASE_   	=    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_   	=    (jnp.arange(0		,					_lowerCamelCase		) * step_ratio).round()[::-1]

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


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   int=None		,					_lowerCamelCase:   Dict=None		):
											SCREAMING_SNAKE_CASE_   	=    state.common.alphas_cumprod[t]
											SCREAMING_SNAKE_CASE_   	=    jnp.where(t > 0		,					state.common.alphas_cumprod[t - 1]		,					jnp.array(1.0		,					dtype=self.dtype		)		)

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											SCREAMING_SNAKE_CASE_   	=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																		SCREAMING_SNAKE_CASE_   	=    self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																		SCREAMING_SNAKE_CASE_   	=    jnp.clip(_lowerCamelCase		,					a_min=1E-20		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																		SCREAMING_SNAKE_CASE_   	=    jnp.log(jnp.clip(_lowerCamelCase		,					a_min=1E-20		)		)
											elif variance_type == "fixed_large":
																		SCREAMING_SNAKE_CASE_   	=    state.common.betas[t]
											elif variance_type == "fixed_large_log":
																		# Glide max_log
																		SCREAMING_SNAKE_CASE_   	=    jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																		return predicted_variance
											elif variance_type == "learned_range":
																		SCREAMING_SNAKE_CASE_   	=    variance
																		SCREAMING_SNAKE_CASE_   	=    state.common.betas[t]
																		SCREAMING_SNAKE_CASE_   	=    (predicted_variance + 1) / 2
																		SCREAMING_SNAKE_CASE_   	=    frac * max_log + (1 - frac) * min_log

											return variance


				def     _A							(							self:   str		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Any = None		,					_lowerCamelCase:   Optional[int] = True		,					):
											SCREAMING_SNAKE_CASE_   	=    timestep

											if key is None:
																		SCREAMING_SNAKE_CASE_   	=    jax.random.PRNGKey(0		)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																		SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    jnp.split(_lowerCamelCase		,					sample.shape[1]		,					axis=1		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    None

											# 1. compute alphas, betas
											SCREAMING_SNAKE_CASE_   	=    state.common.alphas_cumprod[t]
											SCREAMING_SNAKE_CASE_   	=    jnp.where(t > 0		,					state.common.alphas_cumprod[t - 1]		,					jnp.array(1.0		,					dtype=self.dtype		)		)
											SCREAMING_SNAKE_CASE_   	=    1 - alpha_prod_t
											SCREAMING_SNAKE_CASE_   	=    1 - alpha_prod_t_prev

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																		SCREAMING_SNAKE_CASE_   	=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																		SCREAMING_SNAKE_CASE_   	=    model_output
											elif self.config.prediction_type == "v_prediction":
																		SCREAMING_SNAKE_CASE_   	=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											else:
																		raise ValueError(
																		    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
																		    ''' for the FlaxDDPMScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		SCREAMING_SNAKE_CASE_   	=    jnp.clip(_lowerCamelCase		,					-1		,					1		)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											SCREAMING_SNAKE_CASE_   	=    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											SCREAMING_SNAKE_CASE_   	=    state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											SCREAMING_SNAKE_CASE_   	=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																		SCREAMING_SNAKE_CASE_   	=    jax.random.split(_lowerCamelCase		,					num=1		)
																		SCREAMING_SNAKE_CASE_   	=    jax.random.normal(_lowerCamelCase		,					shape=model_output.shape		,					dtype=self.dtype		)
																		return (self._get_variance(_lowerCamelCase		,					_lowerCamelCase		,					predicted_variance=_lowerCamelCase		) ** 0.5) * noise

											SCREAMING_SNAKE_CASE_   	=    jnp.where(t > 0		,					random_variance()		,					jnp.zeros(model_output.shape		,					dtype=self.dtype		)		)

											SCREAMING_SNAKE_CASE_   	=    pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=_lowerCamelCase		,					state=_lowerCamelCase		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Any		,					):
											return add_noise_common(state.common		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   int		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Union[str, Any]		,					):
											return get_velocity_common(state.common		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)





				def __len__(							self:   List[Any]		):
											return self.config.num_train_timesteps






def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


