



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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class    a		:
  UpperCamelCase		:			CommonSchedulerState

  # setable values
  UpperCamelCase		:			jnp.ndarray
  UpperCamelCase		:			jnp.ndarray
  UpperCamelCase		:			jnp.ndarray
  UpperCamelCase		:			Optional[int]         =   None

  # running values
  UpperCamelCase		:			Optional[jnp.ndarray]         =   None



  @classmethod
  def 		lowerCamelCase__    (  cls	:   List[Any]    ,						lowerCAmelCase	:   CommonSchedulerState    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   jnp.ndarray						)       ->   Union[str, Any]:


     '''simple docstring'''




     return cls(common=lowerCAmelCase    ,						init_noise_sigma=lowerCAmelCase    ,						timesteps=lowerCAmelCase    ,						sigmas=lowerCAmelCase						)







@dataclass
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			LMSDiscreteSchedulerState







class    a		(					UpperCAmelCase__		, UpperCAmelCase__    ):
  UpperCamelCase		:			List[Any]         =   [e.name for e in FlaxKarrasDiffusionSchedulers]

  UpperCamelCase		:			jnp.dtype



  @property
  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Any:


     '''simple docstring'''




     return True



  @register_to_config
  def __init__(  self	:   Dict    ,						lowerCAmelCase	:   int = 1000    ,						lowerCAmelCase	:   float = 0.0_0_0_1    ,						lowerCAmelCase	:   float = 0.0_2    ,						lowerCAmelCase	:   str = "linear"    ,						lowerCAmelCase	:   Optional[jnp.ndarray] = None    ,						lowerCAmelCase	:   str = "epsilon"    ,						lowerCAmelCase	:   jnp.dtype = jnp.floataa    ,						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =dtype



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   Optional[CommonSchedulerState] = None						)       ->   LMSDiscreteSchedulerState:


     '''simple docstring'''




     if common is None:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =CommonSchedulerState.create(self						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =jnp.arange(0    ,						self.config.num_train_timesteps						).round()[::-1]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     SCREAMING_SNAKE_CASE_:	List[Any]            =sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=lowerCAmelCase    ,						init_noise_sigma=lowerCAmelCase    ,						timesteps=lowerCAmelCase    ,						sigmas=lowerCAmelCase    ,						)



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   LMSDiscreteSchedulerState    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   int						)       ->   jnp.ndarray:


     '''simple docstring'''




     (SCREAMING_SNAKE_CASE_						):	Optional[Any]            =jnp.where(state.timesteps == timestep    ,						size=1						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =step_index[0]

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =state.sigmas[step_index]
     SCREAMING_SNAKE_CASE_:	int            =sample / ((sigma**2 + 1) ** 0.5)
     return sample



  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   LMSDiscreteSchedulerState    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Dict						)       ->   int:


     '''simple docstring'''




     def lms_derivative(lowerCAmelCase	:   str						):
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =1.0
        for k in range(lowerCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =integrate.quad(lowerCAmelCase    ,						state.sigmas[t]    ,						state.sigmas[t + 1]    ,						epsrel=1E-4						)[0]

     return integrated_coeff



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   LMSDiscreteSchedulerState    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Tuple = ()						)       ->   LMSDiscreteSchedulerState:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =jnp.linspace(self.config.num_train_timesteps - 1    ,						0    ,						lowerCAmelCase    ,						dtype=self.dtype						)

     SCREAMING_SNAKE_CASE_:	List[str]            =jnp.floor(lowerCAmelCase						).astype(jnp.intaa						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =jnp.ceil(lowerCAmelCase						).astype(jnp.intaa						)

     SCREAMING_SNAKE_CASE_:	int            =jnp.mod(lowerCAmelCase    ,						1.0						)

     SCREAMING_SNAKE_CASE_:	str            =((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     SCREAMING_SNAKE_CASE_:	Optional[int]            =(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     SCREAMING_SNAKE_CASE_:	List[str]            =jnp.concatenate([sigmas, jnp.array([0.0]    ,						dtype=self.dtype						)]						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =timesteps.astype(jnp.intaa						)

     # initial running values
     SCREAMING_SNAKE_CASE_:	Tuple            =jnp.zeros((0,) + shape    ,						dtype=self.dtype						)

     return state.replace(
         timesteps=lowerCAmelCase    ,						sigmas=lowerCAmelCase    ,						num_inference_steps=lowerCAmelCase    ,						derivatives=lowerCAmelCase    ,						)



  def 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   LMSDiscreteSchedulerState    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   int = 4    ,						lowerCAmelCase	:   bool = True    ,						)       ->   Union[FlaxLMSSchedulerOutput, Tuple]:


     '''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"""						)

     SCREAMING_SNAKE_CASE_:	Tuple            =state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
        raise ValueError(
            f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

     # 2. Convert to an ODE derivative
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =(sample - pred_original_sample) / sigma
     SCREAMING_SNAKE_CASE_:	Tuple            =state.replace(derivatives=jnp.append(state.derivatives    ,						lowerCAmelCase						)						)
     if len(state.derivatives						) > order:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =state.replace(derivatives=jnp.delete(state.derivatives    ,						0						)						)

     # 3. Compute linear multistep coefficients
     SCREAMING_SNAKE_CASE_:	List[Any]            =min(timestep + 1    ,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =[self.get_lms_coefficient(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						) for curr_order in range(lowerCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     SCREAMING_SNAKE_CASE_:	int            =sample + sum(
         coeff * derivative for coeff, derivative in zip(lowerCAmelCase    ,						reversed(state.derivatives						)						)						)

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=lowerCAmelCase    ,						state=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   List[Any]    ,						lowerCAmelCase	:   LMSDiscreteSchedulerState    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   jnp.ndarray    ,						lowerCAmelCase	:   jnp.ndarray    ,						)       ->   jnp.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =state.sigmas[timesteps].flatten()
     SCREAMING_SNAKE_CASE_:	List[Any]            =broadcast_to_shape_from_left(lowerCAmelCase    ,						noise.shape						)

     SCREAMING_SNAKE_CASE_:	str            =original_samples + noise * sigma

     return noisy_samples





  def __len__(  self	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     return self.config.num_train_timesteps









"""simple docstring"""
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   seed(1	)
   return [gauss(lowercase							,     lowercase	) for _ in range(lowercase	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return sum(lowercase	) / instance_count





def        __magic_name__			(							lowercase							,     lowercase	):
   return instance_count / total_count





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store all squared differences
   # iterate over number of elements in items
   for i in range(len(lowercase	)	):
      # for loop iterates over number of elements in inner layer of items
      for j in range(len(items[i]	)	):
         # appending squared differences to 'squared_diff' list
         squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =len(lowercase	)  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
   # for loop iterates over number of elements in list
   for i in range(len(lowercase	)	):
      # for loop iterates over number of inner items of each element
      for j in range(len(x_items[i]	)	):
         SCREAMING_SNAKE_CASE_:	Optional[int]            =[]  # to store all discriminant values of each item as a list
         # for loop iterates over number of classes we have in our dataset
         for k in range(len(lowercase	)	):
            # appending values of discriminants for each class to 'temp' list
            temp.append(
                x_items[i][j] * (means[k] / variance)
                - (means[k] ** 2 / (2 * variance))
                + log(probabilities[k]	)	)
         # appending discriminant values of each item to 'results' list
         results.append(lowercase	)

   return [result.index(max(lowercase	)	) for result in results]





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =sum(1 for i, j in zip(lowercase							,     lowercase	) if i == j	)
   # percentage of accuracy equals to number of correct predictions divided by number
   # of all data and multiplied by 100
   return (correct / len(lowercase	)) * 100


_UpperCAmelCase       =     TypeVar("""num""")





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase = lambda lowercase	: True							,     lowercase = None							,     ):
   while True:
      try:
         SCREAMING_SNAKE_CASE_:	List[Any]            =input_type(input(lowercase	).strip() or default	)
         if condition(lowercase	):
            return user_input
         else:
            print(f'''{user_input}: {err_msg}'''	)
            continue
      except ValueError:
         print(
             f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''	)





def        __magic_name__			(							):
   while True:
      print(""" Linear Discriminant Analysis """.center(50							,     """*"""	)	)
      print("""*""" * 50							,     """\n"""	)
      print("""First of all we should specify the number of classes that"""	)
      print("""we want to generate as training dataset"""	)
      # Trying to get number of classes
      SCREAMING_SNAKE_CASE_:	List[Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg="""Enter the number of classes (Data Groupings): """							,     err_msg="""Number of classes should be positive!"""							,     )

      print("""-""" * 100	)

      # Trying to get the value of standard deviation
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x >= 0							,     input_msg=(
              """Enter the value of standard deviation"""
              """(Default value is 1.0 for all classes): """
          )							,     err_msg="""Standard deviation should not be negative!"""							,     default="""1.0"""							,     )

      print("""-""" * 100	)

      # Trying to get number of instances in classes and theirs means to generate
      # dataset
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store instance counts of classes in dataset
      for i in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	int            =valid_input(
             input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg=(f'''Enter The number of instances for class_{i+1}: ''')							,     err_msg="""Number of instances should be positive!"""							,     )
         counts.append(lowercase	)
      print("""-""" * 100	)

      # An empty list to store values of user-entered means of classes
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
      for a in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	Tuple            =valid_input(
             input_type=lowercase							,     input_msg=(f'''Enter the value of mean for class_{a+1}: ''')							,     err_msg="""This is an invalid value."""							,     )
         user_means.append(lowercase	)
      print("""-""" * 100	)

      print("""Standard deviation: """							,     lowercase	)
      # print out the number of instances in classes in separated line
      for i, count in enumerate(lowercase							,     1	):
         print(f'''Number of instances in class_{i} is: {count}'''	)
      print("""-""" * 100	)

      # print out mean values of classes separated line
      for i, user_mean in enumerate(lowercase							,     1	):
         print(f'''Mean of class_{i} is: {user_mean}'''	)
      print("""-""" * 100	)

      # Generating training dataset drawn from gaussian distribution
      SCREAMING_SNAKE_CASE_:	Tuple            =[
          gaussian_distribution(user_means[j]							,     lowercase							,     counts[j]	)
          for j in range(lowercase	)
      ]
      print("""Generated Normal Distribution: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Generating Ys to detecting corresponding classes
      SCREAMING_SNAKE_CASE_:	int            =y_generator(lowercase							,     lowercase	)
      print("""Generated Corresponding Ys: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Calculating the value of actual mean for each class
      SCREAMING_SNAKE_CASE_:	str            =[calculate_mean(counts[k]							,     x[k]	) for k in range(lowercase	)]
      # for loop iterates over number of elements in 'actual_means' list and print
      # out them in separated line
      for i, actual_mean in enumerate(lowercase							,     1	):
         print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''	)
      print("""-""" * 100	)

      # Calculating the value of probabilities for each class
      SCREAMING_SNAKE_CASE_:	List[Any]            =[
          calculate_probabilities(counts[i]							,     sum(lowercase	)	) for i in range(lowercase	)
      ]

      # for loop iterates over number of elements in 'probabilities' list and print
      # out them in separated line
      for i, probability in enumerate(lowercase							,     1	):
         print(f'''Probability of class_{i} is: {probability}'''	)
      print("""-""" * 100	)

      # Calculating the values of variance for each class
      SCREAMING_SNAKE_CASE_:	str            =calculate_variance(lowercase							,     lowercase							,     sum(lowercase	)	)
      print("""Variance: """							,     lowercase	)
      print("""-""" * 100	)

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      SCREAMING_SNAKE_CASE_:	Dict            =predict_y_values(lowercase							,     lowercase							,     lowercase							,     lowercase	)
      print("""-""" * 100	)

      # Calculating Accuracy of the model
      print(f'''Accuracy: {accuracy(lowercase							,     lowercase	)}'''	)
      print("""-""" * 100	)
      print(""" DONE """.center(100							,     """+"""	)	)

      if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
         print("""\n""" + """GoodBye!""".center(100							,     """-"""	) + """\n"""	)
         break
      system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
   main()



