





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 			__A:
     __A							=							42
     __A							=							42

     # setable values
     __A							=							42
     __A							=							42
     __A							=							None

     @classmethod
     def 						_UpperCamelCase  (   cls,							A,							A,							A,							A,							):





            """simple docstring"""

            return cls(
                common=A__,							final_alpha_cumprod=A__,							init_noise_sigma=A__,							timesteps=A__,							)






@dataclass
class 			__A(    __lowercase			):
     __A							=							42






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

     __A							=							42

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return True

     @register_to_config
     def __init__(   self,							A = 1000,							A = 0.0_001,							A = 0.02,							A = "linear",							A = None,							A = True,							A = 0,							A = "epsilon",							A = jnp.floataa,							):





            """simple docstring"""

            _UpperCamelCase														=    dtype

     def 						_UpperCamelCase  (   self,							A = None   ):





            """simple docstring"""

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

            # At every step in ddim, we are looking into the previous alphas_cumprod
            # For the final step, there is no previous alphas_cumprod because we are already at 0
            # `set_alpha_to_one` decides whether we set this parameter simply to one or
            # whether we use the final alpha of the "non-previous" one.
            _UpperCamelCase														=    (
                jnp.array(1.0,							dtype=self.dtype   ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
            )

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

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

            return DDIMSchedulerState.create(
                common=A__,							final_alpha_cumprod=A__,							init_noise_sigma=A__,							timesteps=A__,							)

     def 						_UpperCamelCase  (   self,							A,							A,							A = None   ):





            """simple docstring"""

            return sample

     def 						_UpperCamelCase  (   self,							A,							A,							A = ()   ):





            """simple docstring"""

            _UpperCamelCase														=    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														=    (jnp.arange(0,							A__   ) * step_ratio).round()[::-1] + self.config.steps_offset

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

     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    state.common.alphas_cumprod[timestep]
            _UpperCamelCase														=    jnp.where(
                prev_timestep >= 0,							state.common.alphas_cumprod[prev_timestep],							state.final_alpha_cumprod   )
            _UpperCamelCase														=    1 - alpha_prod_t
            _UpperCamelCase														=    1 - alpha_prod_t_prev

            _UpperCamelCase														=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

            return variance

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							A = 0.0,							A = 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'''   )

            # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
            # Ideally, read DDIM paper in-detail understanding

            # Notation (<variable name> -> <name in paper>
            # - pred_noise_t -> e_theta(x_t, t)
            # - pred_original_sample -> f_theta(x_t, t) or x_0
            # - std_dev_t -> sigma_t
            # - eta -> η
            # - pred_sample_direction -> "direction pointing to x_t"
            # - pred_prev_sample -> "x_t-1"

            # 1. get previous step value (=t-1)
            _UpperCamelCase														=    timestep - self.config.num_train_timesteps // state.num_inference_steps

            _UpperCamelCase														=    state.common.alphas_cumprod
            _UpperCamelCase														=    state.final_alpha_cumprod

            # 2. compute alphas, betas
            _UpperCamelCase														=    alphas_cumprod[timestep]
            _UpperCamelCase														=    jnp.where(prev_timestep >= 0,							alphas_cumprod[prev_timestep],							A__   )

            _UpperCamelCase														=    1 - alpha_prod_t

            # 3. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            if self.config.prediction_type == "epsilon":
                   _UpperCamelCase														=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                   _UpperCamelCase														=    model_output
            elif self.config.prediction_type == "sample":
                   _UpperCamelCase														=    model_output
                   _UpperCamelCase														=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                   _UpperCamelCase														=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                   _UpperCamelCase														=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
            else:
                   raise ValueError(
                       F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                       ''' `v_prediction`'''   )

            # 4. compute variance: "sigma_t(η)" -> see formula (16)
            # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
            _UpperCamelCase														=    self._get_variance(A__,							A__,							A__   )
            _UpperCamelCase														=    eta * variance ** 0.5

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            _UpperCamelCase														=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

            # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            _UpperCamelCase														=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                   return (prev_sample, state)

            return FlaxDDIMSchedulerOutput(prev_sample=A__,							state=A__   )

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							):





            """simple docstring"""

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



     def __len__(   self   ):





            """simple docstring"""

            return self.config.num_train_timesteps









import functools
from math import sqrt
from time import time



def  snake_case_(_UpperCamelCase					,			*_UpperCamelCase					,			**_UpperCamelCase	)				->      str:



  """simple docstring"""






  _snake_case		=				time()
  _snake_case		=				func(*_UpperCamelCase					,			**_UpperCamelCase	)
  _snake_case		=				time()
  if int(end - start	) > 0:
    print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
  else:
    print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""	)
  return output



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n == 0:
    return [0]
  _snake_case		=				[0, 1]
  for _ in range(n - 1	):
    fib.append(fib[-1] + fib[-2]	)
  return fib



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  @functools.cache
  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  _snake_case		=				{0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_UpperCamelCase	) -> int:
    if num in cache:
      return cache[num]

    _snake_case		=				rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
    _snake_case		=				value
    return value

  return [rec_fn_memoized(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n >= 1_475:
    raise Exception('''n is too large'''	)
  _snake_case		=				sqrt(5	)
  _snake_case		=				(1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
 __A     						=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)

