






'''simple docstring'''



# 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 UpperCAmelCase_					:




						"""simple docstring"""




						UpperCamelCase_								=			42

						# setable values
						UpperCamelCase_								=			42
						UpperCamelCase_								=			42
						UpperCamelCase_								=			None






						@classmethod
						def 				A__      ( cls			:		Optional[int]     ,      UpperCAmelCase			:		CommonSchedulerState     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		jnp.ndarray		)  ->  Optional[int]:


							'''simple docstring'''

							return cls(common=__lowerCamelCase     ,      init_noise_sigma=__lowerCamelCase     ,      timesteps=__lowerCamelCase		)





@dataclass
class UpperCAmelCase_					(    _A				):




						"""simple docstring"""




						UpperCamelCase_								=			42





class UpperCAmelCase_					(    _A				,       _A				):




						"""simple docstring"""




						UpperCamelCase_								=			[e.name for e in FlaxKarrasDiffusionSchedulers]

						UpperCamelCase_								=			42






						@property
						def 				A__      ( self			:		List[Any]		)  ->  str:


							'''simple docstring'''

							return True






						@register_to_config
						def __init__( self			:		Tuple     ,      UpperCAmelCase			:		int = 1000     ,      UpperCAmelCase			:		float = 0.0_0_0_1     ,      UpperCAmelCase			:		float = 0.0_2     ,      UpperCAmelCase			:		str = "linear"     ,      UpperCAmelCase			:		Optional[jnp.ndarray] = None     ,      UpperCAmelCase			:		str = "fixed_small"     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		str = "epsilon"     ,      UpperCAmelCase			:		jnp.dtype = jnp.floataa     ,      )  ->  List[str]:


							'''simple docstring'''

							lowercase					:   str				  =dtype






						def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Optional[CommonSchedulerState] = None		)  ->  DDPMSchedulerState:


							'''simple docstring'''

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

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

							lowercase					:   Union[str, Any]				  =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     ,      UpperCAmelCase			:		DDPMSchedulerState     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		Optional[int] = None		)  ->  jnp.ndarray:


							'''simple docstring'''

							return sample






						def 				A__      ( self			:		str     ,      UpperCAmelCase			:		DDPMSchedulerState     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Tuple = ()		)  ->  DDPMSchedulerState:


							'''simple docstring'''

							lowercase					:   List[str]				  =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
							lowercase					:   int				  =(jnp.arange(0     ,      __lowerCamelCase		) * step_ratio).round()[::-1]

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






						def 				A__      ( self			:		int     ,      UpperCAmelCase			:		DDPMSchedulerState     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]=None     ,      UpperCAmelCase			:		int=None		)  ->  Any:


							'''simple docstring'''

							lowercase					:   str				  =state.common.alphas_cumprod[t]
							lowercase					:   Union[str, Any]				  =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
							lowercase					:   str				  =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
								lowercase					:   Tuple				  =self.config.variance_type

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

							return variance






						def 				A__      ( self			:		int     ,      UpperCAmelCase			:		DDPMSchedulerState     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		Optional[jax.random.KeyArray] = None     ,      UpperCAmelCase			:		bool = True     ,      )  ->  Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''

							lowercase					:   Union[str, Any]				  =timestep

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

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

							# 1. compute alphas, betas
							lowercase					:   Dict				  =state.common.alphas_cumprod[t]
							lowercase					:   Tuple				  =jnp.where(t > 0     ,      state.common.alphas_cumprod[t - 1]     ,      jnp.array(1.0     ,      dtype=self.dtype		)		)
							lowercase					:   Any				  =1 - alpha_prod_t
							lowercase					:   str				  =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":
								lowercase					:   Optional[int]				  =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
								lowercase					:   Union[str, Any]				  =model_output
							elif self.config.prediction_type == "v_prediction":
								lowercase					:   Dict				  =(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:
								lowercase					:   Dict				  =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
							lowercase					:   Optional[int]				  =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							lowercase					:   Dict				  =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
							lowercase					:   List[str]				  =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

							lowercase					:   List[Any]				  =jnp.where(t > 0     ,      random_variance()     ,      jnp.zeros(model_output.shape     ,      dtype=self.dtype		)		)

							lowercase					:   List[Any]				  =pred_prev_sample + variance

							if not return_dict:
								return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase     ,      state=__lowerCamelCase		)






						def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		DDPMSchedulerState     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		jnp.ndarray     ,      )  ->  jnp.ndarray:


							'''simple docstring'''

							return add_noise_common(state.common     ,      __lowerCamelCase     ,      __lowerCamelCase     ,      __lowerCamelCase		)






						def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		DDPMSchedulerState     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		jnp.ndarray     ,      UpperCAmelCase			:		jnp.ndarray     ,      )  ->  jnp.ndarray:


							'''simple docstring'''

							return get_velocity_common(state.common     ,      __lowerCamelCase     ,      __lowerCamelCase     ,      __lowerCamelCase		)






						def __len__( self			:		str		)  ->  str:


							'''simple docstring'''

							return self.config.num_train_timesteps











'''simple docstring'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



