
"""simple docstring"""
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  _snake_case	:
							SCREAMING_SNAKE_CASE		  =    42

							# setable values
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    None

							# running values
							SCREAMING_SNAKE_CASE		  =    None






							@classmethod
							def 				__lowercase	(       cls    :      Optional[int]    ,			snake_case__    :      CommonSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray     ):






												"""simple docstring"""



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






@dataclass
class  _snake_case	(    _a							):
							SCREAMING_SNAKE_CASE		  =    42






class  _snake_case	(    _a			,					_a							):
							SCREAMING_SNAKE_CASE		  =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							SCREAMING_SNAKE_CASE		  =    42






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return True






							@register_to_config
							def __init__(       self    :      Optional[Any]    ,			snake_case__    :      int = 1_0_0_0    ,			snake_case__    :      float = 0.0_001    ,			snake_case__    :      float = 0.02    ,			snake_case__    :      str = "linear"    ,			snake_case__    :      Optional[jnp.ndarray] = None    ,			snake_case__    :      str = "epsilon"    ,			snake_case__    :      jnp.dtype = jnp.floataa    ,			):






												"""simple docstring"""



												__a:    Optional[Any]		     =       dtype






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      Optional[CommonSchedulerState] = None     ):






												"""simple docstring"""



												if common is None:
																	__a:    Optional[Any]		     =       CommonSchedulerState.create(self     )

												__a:    Dict		     =       jnp.arange(0    ,			self.config.num_train_timesteps     ).round()[::-1]
												__a:    Optional[int]		     =       ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

												# standard deviation of the initial noise distribution
												__a:    Union[str, Any]		     =       sigmas.max()

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






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      LMSDiscreteSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      int     ):






												"""simple docstring"""



												(__a ):    Union[str, Any]		     =       jnp.where(state.timesteps == timestep    ,			size=1     )
												__a:    Dict		     =       step_index[0]

												__a:    List[Any]		     =       state.sigmas[step_index]
												__a:    Tuple		     =       sample / ((sigma**2 + 1) ** 0.5)
												return sample






							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      LMSDiscreteSchedulerState    ,			snake_case__    :      str    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



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

												__a:    Tuple		     =       integrate.quad(__lowerCamelCase    ,			state.sigmas[t]    ,			state.sigmas[t + 1]    ,			epsrel=1e-4     )[0]

												return integrated_coeff






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      LMSDiscreteSchedulerState    ,			snake_case__    :      int    ,			snake_case__    :      Tuple = ()     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       jnp.linspace(self.config.num_train_timesteps - 1    ,			0    ,			__lowerCamelCase    ,			dtype=self.dtype     )

												__a:    Tuple		     =       jnp.floor(__lowerCamelCase     ).astype(jnp.intaa     )
												__a:    Optional[Any]		     =       jnp.ceil(__lowerCamelCase     ).astype(jnp.intaa     )

												__a:    Optional[Any]		     =       jnp.mod(__lowerCamelCase    ,			1.0     )

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

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

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

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






							def 				__lowercase	(       self    :      Any    ,			snake_case__    :      LMSDiscreteSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      int    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      int = 4    ,			snake_case__    :      bool = True    ,			):






												"""simple docstring"""



												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"""     )

												__a:    Optional[int]		     =       state.sigmas[timestep]

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																	__a:    Union[str, Any]		     =       sample - sigma * model_output
												elif self.config.prediction_type == "v_prediction":
																	# * c_out + input * c_skip
																	__a:    Any		     =       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
												__a:    Dict		     =       (sample - pred_original_sample) / sigma
												__a:    Union[str, Any]		     =       state.replace(derivatives=jnp.append(state.derivatives    ,			__lowerCamelCase     )     )
												if len(state.derivatives     ) > order:
																	__a:    List[Any]		     =       state.replace(derivatives=jnp.delete(state.derivatives    ,			0     )     )

												# 3. Compute linear multistep coefficients
												__a:    List[str]		     =       min(timestep + 1    ,			__lowerCamelCase     )
												__a:    int		     =       [self.get_lms_coefficient(__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase     ) for curr_order in range(__lowerCamelCase     )]

												# 4. Compute previous sample based on the derivatives path
												__a:    Dict		     =       sample + sum(
												    coeff * derivative for coeff, derivative in zip(__lowerCamelCase    ,			reversed(state.derivatives     )     )     )

												if not return_dict:
																	return (prev_sample, state)

												return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase    ,			state=__lowerCamelCase     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      LMSDiscreteSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			):






												"""simple docstring"""



												__a:    Optional[Any]		     =       state.sigmas[timesteps].flatten()
												__a:    Union[str, Any]		     =       broadcast_to_shape_from_left(__lowerCamelCase    ,			noise.shape     )

												__a:    Any		     =       original_samples + noise * sigma

												return noisy_samples






							def __len__(       self    :      Optional[int]     ):






												"""simple docstring"""



												return self.config.num_train_timesteps








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   int      ):
		if (difference := index - (len(self.sequence      ) - 2)) >= 1:
			for _ in range(__lowerCamelCase      ):
				self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
		return self.sequence[:index]

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""simple docstring"""



	print(
	    """Fibonacci Series Using Dynamic Programming\n"""      ,     """Enter the index of the Fibonacci number you want to calculate """      ,     """in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,     sep=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

	while True:
		UpperCamelCase	:str		=      input(""">> """   )
		if prompt in {"exit", "quit"}:
			break

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
