


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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class  UpperCamelCase__ :
      a__   :     Any								=						42

      # setable values
      a__   :     List[str]								=						42
      a__   :     Union[str, Any]								=						42
      a__   :     Tuple								=						42
      a__   :     Any								=						None

      # running values
      a__   :     Tuple								=						None






      @classmethod
      def 	__lowercase(     cls					:   Any,		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Optional[Any]   )  -> str:
           return cls(common=__UpperCamelCase,		init_noise_sigma=__UpperCamelCase,		timesteps=__UpperCamelCase,		sigmas=__UpperCamelCase   )

@dataclass
class  UpperCamelCase__ ( UpperCAmelCase_			):
      a__   :     int								=						42

class  UpperCamelCase__ ( UpperCAmelCase_							,						UpperCAmelCase_			):
      a__   :     str								=						[e.name for e in FlaxKarrasDiffusionSchedulers]

      a__   :     Optional[Any]								=						42






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






      @register_to_config
      def __init__(     self					:   Optional[Any],		__lowerCamelCase					:   Optional[Any] = 10_00,		__lowerCamelCase					:   List[Any] = 0.0001,		__lowerCamelCase					:   Union[str, Any] = 0.02,		__lowerCamelCase					:   Tuple = "linear",		__lowerCamelCase					:   str = None,		__lowerCamelCase					:   int = "epsilon",		__lowerCamelCase					:   List[str] = jnp.floataa,		)  -> Tuple:
           UpperCamelCase__							:  Union[str, Any]								=     dtype






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   Tuple = None   )  -> List[str]:
           if common is None:
                UpperCamelCase__							:  Tuple								=     CommonSchedulerState.create(self   )

           UpperCamelCase__							:  Union[str, Any]								=     jnp.arange(0,		self.config.num_train_timesteps   ).round()[::-1]
           UpperCamelCase__							:  Dict								=     ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

           # standard deviation of the initial noise distribution
           UpperCamelCase__							:  int								=     sigmas.max()

           return LMSDiscreteSchedulerState.create(
               common=__UpperCamelCase,		init_noise_sigma=__UpperCamelCase,		timesteps=__UpperCamelCase,		sigmas=__UpperCamelCase,		)






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   List[str],		__lowerCamelCase					:   int   )  -> Optional[Any]:
           ((UpperCamelCase__					)	,					)							:  Dict								=     jnp.where(state.timesteps == timestep,		size=1   )
           UpperCamelCase__							:  Tuple								=     step_index[0]

           UpperCamelCase__							:  str								=     state.sigmas[step_index]
           UpperCamelCase__							:  Optional[int]								=     sample / ((sigma**2 + 1) ** 0.5)
           return sample






      def 	__lowercase(     self					:   Optional[int],		__lowerCamelCase					:   Any,		__lowerCamelCase					:   Any,		__lowerCamelCase					:   str,		__lowerCamelCase					:   Tuple   )  -> List[Any]:

           def lms_derivative(__lowerCamelCase					:   Any   ):
                UpperCamelCase__							:  List[Any]								=     1.0
                for k in range(__UpperCamelCase   ):
                     if current_order == k:
                          continue
                     prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                return prod

           UpperCamelCase__							:  Union[str, Any]								=     integrate.quad(__UpperCamelCase,		state.sigmas[t],		state.sigmas[t + 1],		epsrel=1e-4   )[0]

           return integrated_coeff






      def 	__lowercase(     self					:   Tuple,		__lowerCamelCase					:   int,		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   Tuple = ()   )  -> Tuple:
           UpperCamelCase__							:  Dict								=     jnp.linspace(self.config.num_train_timesteps - 1,		0,		__UpperCamelCase,		dtype=self.dtype   )

           UpperCamelCase__							:  Union[str, Any]								=     jnp.floor(__UpperCamelCase   ).astype(jnp.intaa   )
           UpperCamelCase__							:  List[Any]								=     jnp.ceil(__UpperCamelCase   ).astype(jnp.intaa   )

           UpperCamelCase__							:  List[Any]								=     jnp.mod(__UpperCamelCase,		1.0   )

           UpperCamelCase__							:  List[str]								=     ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
           UpperCamelCase__							:  List[Any]								=     (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
           UpperCamelCase__							:  Optional[Any]								=     jnp.concatenate([sigmas, jnp.array([0.0],		dtype=self.dtype   )]   )

           UpperCamelCase__							:  Dict								=     timesteps.astype(jnp.intaa   )

           # initial running values
           UpperCamelCase__							:  Any								=     jnp.zeros((0,) + shape,		dtype=self.dtype   )

           return state.replace(
               timesteps=__UpperCamelCase,		sigmas=__UpperCamelCase,		num_inference_steps=__UpperCamelCase,		derivatives=__UpperCamelCase,		)






      def 	__lowercase(     self					:   Optional[Any],		__lowerCamelCase					:   Optional[int],		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   str,		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   Any = 4,		__lowerCamelCase					:   List[Any] = True,		)  -> Optional[int]:
           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'''   )

           UpperCamelCase__							:  int								=     state.sigmas[timestep]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                UpperCamelCase__							:  int								=     sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                UpperCamelCase__							:  Optional[int]								=     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           else:
                raise ValueError(
                    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'   )

           # 2. Convert to an ODE derivative
           UpperCamelCase__							:  str								=     (sample - pred_original_sample) / sigma
           UpperCamelCase__							:  Union[str, Any]								=     state.replace(derivatives=jnp.append(state.derivatives,		__UpperCamelCase   )   )
           if len(state.derivatives   ) > order:
                UpperCamelCase__							:  Tuple								=     state.replace(derivatives=jnp.delete(state.derivatives,		0   )   )

           # 3. Compute linear multistep coefficients
           UpperCamelCase__							:  Union[str, Any]								=     min(timestep + 1,		__UpperCamelCase   )
           UpperCamelCase__							:  Optional[Any]								=     [self.get_lms_coefficient(__UpperCamelCase,		__UpperCamelCase,		__UpperCamelCase,		__UpperCamelCase   ) for curr_order in range(__UpperCamelCase   )]

           # 4. Compute previous sample based on the derivatives path
           UpperCamelCase__							:  Union[str, Any]								=     sample + sum(
               coeff * derivative for coeff, derivative in zip(__UpperCamelCase,		reversed(state.derivatives   )   )   )

           if not return_dict:
                return (prev_sample, state)

           return FlaxLMSSchedulerOutput(prev_sample=__UpperCamelCase,		state=__UpperCamelCase   )






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   Optional[Any],		)  -> int:
           UpperCamelCase__							:  Optional[Any]								=     state.sigmas[timesteps].flatten()
           UpperCamelCase__							:  Any								=     broadcast_to_shape_from_left(__UpperCamelCase,		noise.shape   )

           UpperCamelCase__							:  str								=     original_samples + noise * sigma

           return noisy_samples






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








import math







def 						_lowercase       (     __lowerCamelCase			:					int			)  ->   str:





     '''simple docstring'''





     if number == 0:
          UpperCamelCase__							:  List[str]								=     '''Zero'''
          return words
     else:
          UpperCamelCase__							:  str								=     math.logaa(__lowerCamelCase			)
          UpperCamelCase__							:  Tuple								=     digits + 1
          UpperCamelCase__							:  List[str]								=     {}
          UpperCamelCase__							:  Optional[int]								=     ''''''
          UpperCamelCase__							:  str								=     '''One'''
          UpperCamelCase__							:  int								=     '''Two'''
          UpperCamelCase__							:  str								=     '''Three'''
          UpperCamelCase__							:  Dict								=     '''Four'''
          UpperCamelCase__							:  List[str]								=     '''Five'''
          UpperCamelCase__							:  int								=     '''Six'''
          UpperCamelCase__							:  str								=     '''Seven'''
          UpperCamelCase__							:  List[str]								=     '''Eight'''
          UpperCamelCase__							:  Tuple								=     '''Nine'''

          UpperCamelCase__							:  int								=     {}
          UpperCamelCase__							:  Any								=     ''''''
          UpperCamelCase__							:  Union[str, Any]								=     '''Twenty'''
          UpperCamelCase__							:  Optional[int]								=     '''Thirty'''
          UpperCamelCase__							:  Optional[int]								=     '''Forty'''
          UpperCamelCase__							:  Any								=     '''Fifty'''
          UpperCamelCase__							:  Optional[Any]								=     '''Sixty'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Seventy'''
          UpperCamelCase__							:  Tuple								=     '''Eighty'''
          UpperCamelCase__							:  Dict								=     '''Ninety'''

          UpperCamelCase__							:  Any								=     {}
          UpperCamelCase__							:  str								=     '''Ten'''
          UpperCamelCase__							:  List[Any]								=     '''Eleven'''
          UpperCamelCase__							:  List[str]								=     '''Twelve'''
          UpperCamelCase__							:  List[str]								=     '''Thirteen'''
          UpperCamelCase__							:  List[Any]								=     '''Fourteen'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Fifteen'''
          UpperCamelCase__							:  str								=     '''Sixteen'''
          UpperCamelCase__							:  int								=     '''Seventeen'''
          UpperCamelCase__							:  Tuple								=     '''Eighteen'''
          UpperCamelCase__							:  int								=     '''Nineteen'''

          UpperCamelCase__							:  Union[str, Any]								=     {}
          UpperCamelCase__							:  Dict								=     '''Hundred,'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Thousand,'''
          UpperCamelCase__							:  List[Any]								=     '''Lakh,'''
          UpperCamelCase__							:  str								=     '''Crore,'''

          UpperCamelCase__							:  int								=     number
          UpperCamelCase__							:  int								=     ''''''
          UpperCamelCase__							:  int								=     0
          UpperCamelCase__							:  int								=     int(__lowerCamelCase			)
          while counter < digits:
               UpperCamelCase__							:  str								=     temp_num % 10
               if counter % 2 == 0:
                    UpperCamelCase__							:  List[Any]								=     ''''''
                    if counter in placevalue and current != 0:
                         UpperCamelCase__							:  Optional[Any]								=     placevalue[counter]
                    if counter == 2:
                         UpperCamelCase__							:  Tuple								=     singles[current] + addition + words
                    elif counter == 0:
                         if ((temp_num % 100) // 10) == 1:
                              UpperCamelCase__							:  Optional[Any]								=     teens[current] + addition + words
                              UpperCamelCase__							:  int								=     temp_num // 10
                              counter += 1
                         else:
                              UpperCamelCase__							:  Optional[Any]								=     singles[current] + addition + words

                    else:
                         UpperCamelCase__							:  Tuple								=     doubles[current] + addition + words

               else:
                    if counter == 1:
                         if current == 1:
                              UpperCamelCase__							:  Optional[Any]								=     teens[number % 10] + words
                         else:
                              UpperCamelCase__							:  Union[str, Any]								=     ''''''
                              if counter in placevalue:
                                   UpperCamelCase__							:  Optional[int]								=     placevalue[counter]
                              UpperCamelCase__							:  Optional[Any]								=     doubles[current] + addition + words
                    else:
                         UpperCamelCase__							:  List[Any]								=     ''''''
                         if counter in placevalue:
                              if current == 0 and ((temp_num % 100) // 10) == 0:
                                   UpperCamelCase__							:  Dict								=     ''''''
                              else:
                                   UpperCamelCase__							:  List[Any]								=     placevalue[counter]
                         if ((temp_num % 100) // 10) == 1:
                              UpperCamelCase__							:  Optional[int]								=     teens[current] + addition + words
                              UpperCamelCase__							:  Dict								=     temp_num // 10
                              counter += 1
                         else:
                              UpperCamelCase__							:  Optional[int]								=     singles[current] + addition + words
               counter += 1
               UpperCamelCase__							:  List[str]								=     temp_num // 10
     return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()




