



"""simple docstring"""
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'''



     a__   							=42

     # setable values
     a__   							=42
     a__   							=42
     a__   							=None





     @classmethod
     def 					__lowerCAmelCase      (   cls			,      A			,      A			,      A						)				->   Union[str, Any]:
         return cls(common=A_			,      init_noise_sigma=A_			,      timesteps=A_						)



@dataclass
class 				_UpperCAmelCase				(					_lowercase ):





     '''simple docstring'''



     a__   							=42



class 				_UpperCAmelCase				(					_lowercase						,_lowercase ):





     '''simple docstring'''



     a__   							=[e.name for e in FlaxKarrasDiffusionSchedulers]

     a__   							=42





     @property
     def 					__lowerCAmelCase      (   self						)				->   Union[str, Any]:
         return True





     @register_to_config
     def __init__(   self			,      A = 1_0_0_0			,      A = 0.0_001			,      A = 0.02			,      A = "linear"			,      A = None			,      A = "fixed_small"			,      A = True			,      A = "epsilon"			,      A = jnp.floataa			,      )				->   Union[str, Any]:
         _UpperCAmelCase				:    Dict											=       dtype





     def 					__lowerCAmelCase      (   self			,      A = None						)				->   DDPMSchedulerState:
         if common is None:
             _UpperCAmelCase				:    List[str]											=       CommonSchedulerState.create(self						)

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

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

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





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A = None						)				->   jnp.ndarray:
         return sample





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A = ()						)				->   DDPMSchedulerState:
         _UpperCAmelCase				:    int											=       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
         _UpperCAmelCase				:    Any											=       (jnp.arange(0			,      A_						) * step_ratio).round()[::-1]

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





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A=None			,      A=None						)				->   Optional[int]:
         _UpperCAmelCase				:    List[str]											=       state.common.alphas_cumprod[t]
         _UpperCAmelCase				:    Optional[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
         _UpperCAmelCase				:    Tuple											=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

         if variance_type is None:
             _UpperCAmelCase				:    List[Any]											=       self.config.variance_type

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

         return variance





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A			,      A = None			,      A = True			,      )				->   Union[FlaxDDPMSchedulerOutput, Tuple]:
         _UpperCAmelCase				:    Optional[int]											=       timestep

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

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

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

         # 6. Add noise
         def random_variance():
             _UpperCAmelCase				:    List[Any]											=       jax.random.split(A_			,      num=1						)
             _UpperCAmelCase				:    Optional[int]											=       jax.random.normal(A_			,      shape=model_output.shape			,      dtype=self.dtype						)
             return (self._get_variance(A_			,      A_			,      predicted_variance=A_						) ** 0.5) * noise

         _UpperCAmelCase				:    Optional[int]											=       jnp.where(t > 0			,      random_variance()			,      jnp.zeros(model_output.shape			,      dtype=self.dtype						)						)

         _UpperCAmelCase				:    List[Any]											=       pred_prev_sample + variance

         if not return_dict:
             return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=A_			,      state=A_						)





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A			,      )				->   jnp.ndarray:
         return add_noise_common(state.common			,      A_			,      A_			,      A_						)





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A			,      )				->   jnp.ndarray:
         return get_velocity_common(state.common			,      A_			,      A_			,      A_						)





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




"""simple docstring"""


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Callable	,A_  :   Optional[Features] = None	,A_  :   str = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   Optional[dict] = None	,A_  :   Optional[int] = None	,**A_  :   int	,)  -> str:
							super().__init__(
							    features=A_	,cache_dir=A_	,keep_in_memory=A_	,streaming=A_	,num_proc=A_	,**A_	,)
							A     = Generator(
							    cache_dir=A_	,features=A_	,generator=A_	,gen_kwargs=A_	,**A_	,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset