



"""simple docstring"""
from collections import defaultdict


class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				List[Any] ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= total  # total no of tasks (N)

      # DP table will have a dimension of (2^M)*N
      # initially all values are set to -1
      lowercase_								= [
          [-1 for i in range(total + 1)] for j in range(2 ** len(lowerCAmelCase_))
      ]

      lowercase_								= defaultdict(lowerCAmelCase_)  # stores the list of persons for each task

      # final_mask is used to check if all persons are included by setting all bits
      # to 1
      lowercase_								= (1 << len(lowerCAmelCase_)) - 1

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      if mask == self.final_mask:
        return 1

      # if not everyone gets the task and no more tasks are available, return 0
      if task_no > self.total_tasks:
        return 0

      # if case already considered
      if self.dp[mask][task_no] != -1:
        return self.dp[mask][task_no]

      # Number of ways when we don't this task in the arrangement
      lowercase_								= self.count_ways_until(lowerCAmelCase_ ,  task_no + 1)

      # now assign the tasks one by one to all possible persons and recursively
      # assign for the remaining tasks.
      if task_no in self.task:
        for p in self.task[task_no]:
          # if p is already given a task
          if mask & (1 << p):
            continue

          # assign this task to p and change the mask value. And recursively
          # assign tasks with the new mask value.
          total_ways_util += self.count_ways_until(mask | (1 << p) ,  task_no + 1)

        # save the value.
      lowercase_								= total_ways_util

      return self.dp[mask][task_no]




    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      for i in range(len(lowerCAmelCase_)):
        for j in task_performed[i]:
          self.task[j].append(lowerCAmelCase_)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
      return self.count_ways_until(0 ,  1)


if __name__ == "__main__":
       UpperCAmelCase						:		Optional[Any]			= 5  # total no of tasks (the value of N)

       # the list of tasks that can be done by M persons.
       UpperCAmelCase						:		Tuple			= [[1, 3, 4], [1, 2, 5], [3, 4]]
       print(
           AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
               task_performed
           )
       )





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      raise NotImplementedError()

