


"""simple docstring"""







def _A						(						lowercase		):







    """simple docstring"""
    a					=len(lowercase		)
    a					=sum(lowercase		)

    a					=[[False for x in range(s + 1		)] for y in range(n + 1		)]

    for i in range(1	,   n + 1		):
        a					=True

    for i in range(1	,   s + 1		):
        a					=False

    for i in range(1	,   n + 1		):
        for j in range(1	,   s + 1		):
            a					=dp[i][j - 1]

            if arr[i - 1] <= j:
                a					=dp[i][j] or dp[i - 1][j - arr[i - 1]]

    for j in range(int(s / 2		)	,   -1	,   -1		):
        if dp[n][j] is True:
            a					=s - 2 * j
            break

    return diff



"""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    :






 """simple docstring"""




 __lowerCAmelCase			 =  42

 # setable values
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  None


 @classmethod
 def 				SCREAMING_SNAKE_CASE							(		cls			,   __A			,   __A			,   __A						) ->					List[str]:
     return cls(common=__A			,   init_noise_sigma=__A			,   timesteps=__A						)






@dataclass
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  42






class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCAmelCase			 =  42


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return True


 @register_to_config
 def __init__(		self			,   __A = 1000			,   __A = 0.0_001			,   __A = 0.02			,   __A = "linear"			,   __A = None			,   __A = "fixed_small"			,   __A = True			,   __A = "epsilon"			,   __A = jnp.floataa			,   ) ->					List[Any]:
     a					=dtype


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A = None						) ->					DDPMSchedulerState:
     if common is None:
         a					=CommonSchedulerState.create(self						)

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

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

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None						) ->					jnp.ndarray:
     return sample


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = ()						) ->					DDPMSchedulerState:
     a					=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
     a					=(jnp.arange(0			,   __A						) * step_ratio).round()[::-1]

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=None			,   __A=None						) ->					str:
     a					=state.common.alphas_cumprod[t]
     a					=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
     a					=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

     return variance


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A = None			,   __A = True			,   ) ->					Union[FlaxDDPMSchedulerOutput, Tuple]:
     a					=timestep

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

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

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

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

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

     a					=pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__A			,   state=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return add_noise_common(state.common			,   __A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return get_velocity_common(state.common			,   __A			,   __A			,   __A						)





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