



'''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__      :




					'''simple docstring'''



					A_						    =						42

					# setable values
					A_						    =						42
					A_						    =						42
					A_						    =						None




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





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






@dataclass
class 		lowerCamelCase__      (						__UpperCAmelCase     ):




					'''simple docstring'''



					A_						    =						42






class 		lowerCamelCase__      (						__UpperCAmelCase	,   __UpperCAmelCase     ):




					'''simple docstring'''



					A_						    =						[e.name for e in FlaxKarrasDiffusionSchedulers]

					A_						    =						42




					@property
					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						return True




					@register_to_config
					def __init__(  self				:	Any     ,  UpperCamelCase_				:	Any = 1000     ,  UpperCamelCase_				:	List[Any] = 0.00_01     ,  UpperCamelCase_				:	Tuple = 0.02     ,  UpperCamelCase_				:	Dict = "linear"     ,  UpperCamelCase_				:	int = None     ,  UpperCamelCase_				:	Tuple = "fixed_small"     ,  UpperCamelCase_				:	Dict = True     ,  UpperCamelCase_				:	Optional[int] = "epsilon"     ,  UpperCamelCase_				:	List[str] = jnp.floataa     ,  )  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		dtype




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	Optional[int] = None					)  ->       Tuple:



						'''simple docstring'''





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

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

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

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




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[int] = None					)  ->       Optional[Any]:



						'''simple docstring'''





						return sample




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Union[str, Any] = ()					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Dict       =		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
						_lowercase					:       List[Any]       =		(jnp.arange(0     ,  UpperCamelCase_					) * step_ratio).round()[::-1]

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




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Any=None     ,  UpperCamelCase_				:	Tuple=None					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Dict       =		state.common.alphas_cumprod[t]
						_lowercase					:       Dict       =		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
						_lowercase					:       Union[str, Any]       =		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

						if variance_type is None:
							_lowercase					:       List[Any]       =		self.config.variance_type

						# hacks - were probably added for training stability
						if variance_type == "fixed_small":
							_lowercase					:       Tuple       =		jnp.clip(UpperCamelCase_     ,  a_min=1E-20					)
						# for rl-diffuser https://arxiv.org/abs/2205.09991
						elif variance_type == "fixed_small_log":
							_lowercase					:       Union[str, Any]       =		jnp.log(jnp.clip(UpperCamelCase_     ,  a_min=1E-20					)					)
						elif variance_type == "fixed_large":
							_lowercase					:       Dict       =		state.common.betas[t]
						elif variance_type == "fixed_large_log":
							# Glide max_log
							_lowercase					:       Dict       =		jnp.log(state.common.betas[t]					)
						elif variance_type == "learned":
							return predicted_variance
						elif variance_type == "learned_range":
							_lowercase					:       Union[str, Any]       =		variance
							_lowercase					:       str       =		state.common.betas[t]
							_lowercase					:       List[Any]       =		(predicted_variance + 1) / 2
							_lowercase					:       Optional[Any]       =		frac * max_log + (1 - frac) * min_log

						return variance




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Union[str, Any] = True     ,  )  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		timestep

						if key is None:
							_lowercase					:       List[str]       =		jax.random.PRNGKey(0					)

						if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							_lowercase  ,    _lowercase					:       List[str]       =		jnp.split(UpperCamelCase_     ,  sample.shape[1]     ,  axis=1					)
						else:
							_lowercase					:       Optional[int]       =		None

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

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

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

						_lowercase					:       Dict       =		pred_prev_sample + variance

						if not return_dict:
							return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_     ,  state=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	str     ,  )  ->       int:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	int     ,  )  ->       int:



						'''simple docstring'''





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




					def __len__(  self				:	int					)  ->       Dict:



						'''simple docstring'''





						return self.config.num_train_timesteps





'''simple docstring'''

import unittest

from knapsack import greedy_knapsack as kp



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		[10, 20, 30, 40, 50, 60]
						_lowercase					:       Tuple       =		[2, 4, 6, 8, 10, 12]
						_lowercase					:       Optional[Any]       =		100
						self.assertEqual(kp.calc_profit(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)     ,  210					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'max_weight must greater than zero.'					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'Weight can not be negative.'					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'Profit can not be negative.'					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[str]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'max_weight must greater than zero.'					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(
						    UpperCamelCase_     ,  'The length of profit and weight must be same.'					)


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