


'''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 			lowerCamelCase_   :
      lowerCAmelCase__		     =     42

      # setable values
      lowerCAmelCase__		     =     42
      lowerCAmelCase__		     =     42
      lowerCAmelCase__		     =     None






      @classmethod
      def lowercase_							(					cls				:    str						,						_A				:    Dict						,						_A				:    Tuple						,						_A				:    Union[str, Any] ):


          '''simple docstring'''


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




@dataclass
class 			lowerCamelCase_   (	__lowercase      ):
      lowerCAmelCase__		     =     42




class 			lowerCamelCase_   (	__lowercase     ,       __lowercase      ):
      lowerCAmelCase__		     =     [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCAmelCase__		     =     42






      @property
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return True






      @register_to_config
      def __init__(					self				:    List[Any]						,						_A				:    List[Any] = 1_000						,						_A				:    List[Any] = 0.0_0_0_1						,						_A				:    str = 0.0_2						,						_A				:    Optional[int] = "linear"						,						_A				:    Optional[int] = None						,						_A				:    int = "fixed_small"						,						_A				:    str = True						,						_A				:    List[Any] = "epsilon"						,						_A				:    Any = jnp.floataa						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				dtype






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    Optional[int] = None ):


          '''simple docstring'''


          if common is None:
              UpperCAmelCase__			:		List[str]													=				CommonSchedulerState.create(self )

          # standard deviation of the initial noise distribution
          UpperCAmelCase__			:		Union[str, Any]													=				jnp.array(1.0						,						dtype=self.dtype )

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

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






      def lowercase_							(					self				:    int						,						_A				:    List[str]						,						_A				:    List[str]						,						_A				:    Dict = None ):


          '''simple docstring'''


          return sample






      def lowercase_							(					self				:    Dict						,						_A				:    str						,						_A				:    Optional[int]						,						_A				:    Dict = () ):


          '''simple docstring'''


          UpperCAmelCase__			:		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
          UpperCAmelCase__			:		Optional[Any]													=				(jnp.arange(0						,						_a ) * step_ratio).round()[::-1]

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






      def lowercase_							(					self				:    Optional[int]						,						_A				:    int						,						_A				:    int						,						_A				:    Optional[int]=None						,						_A				:    Optional[int]=None ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				state.common.alphas_cumprod[t]
          UpperCAmelCase__			:		List[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__			:		int													=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
              UpperCAmelCase__			:		str													=				self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small":
              UpperCAmelCase__			:		List[Any]													=				jnp.clip(_a						,						a_min=1e-20 )
          # for rl-diffuser https://arxiv.org/abs/2205.09991
          elif variance_type == "fixed_small_log":
              UpperCAmelCase__			:		int													=				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__			:		Tuple													=				variance
              UpperCAmelCase__			:		str													=				state.common.betas[t]
              UpperCAmelCase__			:		Optional[int]													=				(predicted_variance + 1) / 2
              UpperCAmelCase__			:		Union[str, Any]													=				frac * max_log + (1 - frac) * min_log

          return variance






      def lowercase_							(					self				:    Tuple						,						_A				:    Dict						,						_A				:    Optional[Any]						,						_A				:    Union[str, Any]						,						_A				:    str						,						_A				:    Tuple = None						,						_A				:    str = True						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				timestep

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

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

          # 1. compute alphas, betas
          UpperCAmelCase__			:		Optional[Any]													=				state.common.alphas_cumprod[t]
          UpperCAmelCase__			:		Any													=				jnp.where(t > 0						,						state.common.alphas_cumprod[t - 1]						,						jnp.array(1.0						,						dtype=self.dtype ) )
          UpperCAmelCase__			:		Dict													=				1 - alpha_prod_t
          UpperCAmelCase__			:		List[Any]													=				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__			:		int													=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
              UpperCAmelCase__			:		List[Any]													=				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__			:		Optional[int]													=				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__			:		Tuple													=				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__			:		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__			:		Union[str, Any]													=				jax.random.normal(_a						,						shape=model_output.shape						,						dtype=self.dtype )
              return (self._get_variance(_a						,						_a						,						predicted_variance=_a ) ** 0.5) * noise

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

          UpperCAmelCase__			:		List[str]													=				pred_prev_sample + variance

          if not return_dict:
              return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=_a						,						state=_a )






      def lowercase_							(					self				:    Tuple						,						_A				:    Dict						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    str						,						):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Optional[int]						,						_A				:    List[Any]						,						_A				:    int						,						_A				:    Union[str, Any]						,						_A				:    Any						,						):


          '''simple docstring'''


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






      def __len__(					self				:    str ):


          '''simple docstring'''


          return self.config.num_train_timesteps



def    __UpperCAmelCase (	__a      :			float	)			->     float:

					"""simple docstring"""

					return 10 - x * x



def    __UpperCAmelCase (	__a      :			float		,__a      :			float	)			->     float:

					"""simple docstring"""

					if equation(__a	) * equation(__a	) >= 0:
										raise ValueError('''Wrong space!'''	)

					_a	:    Dict         =		a
					while (b - a) >= 0.01:
										# Find middle point
										_a	:    Any         =		(a + b) / 2
										# Check if middle point is root
										if equation(__a	) == 0.0:
															break
										# Decide the side to repeat the steps
										if equation(__a	) * equation(__a	) < 0:
															_a	:    str         =		c
										else:
															_a	:    Union[str, Any]         =		c
					return c


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(bisection(-2, 5))
			print(bisection(0, 6))



