"""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        a				:
    _snake_case     :  Optional[int]     							=							42

    # setable values
    _snake_case     :  Tuple     							=							42
    _snake_case     :  str     							=							42
    _snake_case     :  Optional[Any]     							=							None


    @classmethod
    def      lowerCAmelCase_  (					cls	:  int   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int					):
      return cls(common=__lowerCAmelCase   ,				init_noise_sigma=__lowerCAmelCase   ,				timesteps=__lowerCAmelCase					)



@dataclass
class        a				(		__UpperCAmelCase   ):
    _snake_case     :  Tuple     							=							42



class        a				(		__UpperCAmelCase	,							__UpperCAmelCase   ):
    _snake_case     :  Tuple     							=							[e.name for e in FlaxKarrasDiffusionSchedulers]

    _snake_case     :  Dict     							=							42


    @property
    def      lowerCAmelCase_  (					self	:  Dict					):
      return True


    @register_to_config
    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  List[Any] = 1000   ,				__lowerCAmelCase	:  List[Any] = 0.0_001   ,				__lowerCAmelCase	:  Optional[int] = 0.02   ,				__lowerCAmelCase	:  Dict = "linear"   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  List[Any] = "fixed_small"   ,				__lowerCAmelCase	:  Optional[int] = True   ,				__lowerCAmelCase	:  int = "epsilon"   ,				__lowerCAmelCase	:  Dict = jnp.floataa   ,				):
      _UpperCAmelCase						 =      dtype


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Dict = None					):

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

      # 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 DDPMSchedulerState.create(
          common=__lowerCAmelCase   ,				init_noise_sigma=__lowerCAmelCase   ,				timesteps=__lowerCAmelCase   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  List[Any] = None					):
      return sample


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any] = ()					):
      _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   ,				__lowerCAmelCase					) * step_ratio).round()[::-1]

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


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  Tuple=None					):
      _UpperCAmelCase						 =      state.common.alphas_cumprod[t]
      _UpperCAmelCase						 =      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						 =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
        _UpperCAmelCase						 =      self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
        _UpperCAmelCase						 =      jnp.clip(__lowerCAmelCase   ,				a_min=1e-2_0					)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
        _UpperCAmelCase						 =      jnp.log(jnp.clip(__lowerCAmelCase   ,				a_min=1e-2_0					)					)
      elif variance_type == "fixed_large":
        _UpperCAmelCase						 =      state.common.betas[t]
      elif variance_type == "fixed_large_log":
        # Glide max_log
        _UpperCAmelCase						 =      jnp.log(state.common.betas[t]					)
      elif variance_type == "learned":
        return predicted_variance
      elif variance_type == "learned_range":
        _UpperCAmelCase						 =      variance
        _UpperCAmelCase						 =      state.common.betas[t]
        _UpperCAmelCase						 =      (predicted_variance + 1) / 2
        _UpperCAmelCase						 =      frac * max_log + (1 - frac) * min_log

      return variance


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str = None   ,				__lowerCAmelCase	:  Optional[int] = True   ,				):
      _UpperCAmelCase						 =      timestep

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

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

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

      # 6. Add noise
      def random_variance():
        _UpperCAmelCase						 =      jax.random.split(__lowerCAmelCase   ,				num=1					)
        _UpperCAmelCase						 =      jax.random.normal(__lowerCAmelCase   ,				shape=model_output.shape   ,				dtype=self.dtype					)
        return (self._get_variance(__lowerCAmelCase   ,				__lowerCAmelCase   ,				predicted_variance=__lowerCAmelCase					) ** 0.5) * noise

      _UpperCAmelCase						 =      jnp.where(t > 0   ,				random_variance()   ,				jnp.zeros(model_output.shape   ,				dtype=self.dtype					)					)

      _UpperCAmelCase						 =      pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase   ,				state=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  str   ,				):
      return add_noise_common(state.common   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[str]   ,				):
      return get_velocity_common(state.common   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)


    def __len__(					self	:  Any					):
      return self.config.num_train_timesteps

"""simple docstring"""


import unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


if __name__ == "__main__":
    unittest.main()
