




import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("ignore")

a_      						=   "K-MEANS-CLUST/ "




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_=None					)				->		Tuple:

    """simple docstring"""


    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(lowercase_					)
    __UpperCamelCase     						=  data.shape[0]  # number of data points

    # Pick K indices from range [0, N).
    __UpperCamelCase     						=  np.random.randint(0    ,   lowercase_    ,   lowercase_					)

    # Keep centroids as dense format, as many entries will be nonzero due to averaging.
    # As long as at least one document in a cluster contains a word,
    # it will carry a nonzero weight in the TF-IDF vector of the centroid.
    __UpperCamelCase     						=  data[rand_indices, :]

    return centroids




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		str:

    """simple docstring"""


    return pairwise_distances(lowercase_    ,   lowercase_    ,   metric='''euclidean'''					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		str:

    """simple docstring"""


    __UpperCamelCase     						=  centroid_pairwise_dist(lowercase_    ,   lowercase_					)

    # Compute cluster assignments for each data point:
    # Fill in the blank (RHS only)
    __UpperCamelCase     						=  np.argmin(lowercase_    ,   axis=1					)

    return cluster_assignment




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(lowercase_					):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        __UpperCamelCase     						=  data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        __UpperCamelCase     						=  member_data_points.mean(axis=0					)
        new_centroids.append(lowercase_					)
    __UpperCamelCase     						=  np.array(lowercase_					)

    return new_centroids




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  0.0
    for i in range(lowercase_					):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        __UpperCamelCase     						=  data[cluster_assignment == i, :]

        if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
            # Compute distances from centroid to data points (RHS only)
            __UpperCamelCase     						=  pairwise_distances(
                lowercase_    ,   [centroids[i]]    ,   metric='''euclidean'''					)
            __UpperCamelCase     						=  distances**2
            heterogeneity += np.sum(lowercase_					)

    return heterogeneity




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		List[Any]:

    """simple docstring"""


    plt.figure(figsize=(7, 4)					)
    plt.plot(lowercase_    ,   linewidth=4					)
    plt.xlabel('''# Iterations'''					)
    plt.ylabel('''Heterogeneity'''					)
    plt.title(F"Heterogeneity of clustering over time, K={k:d}"					)
    plt.rcParams.update({'''font.size''': 16}					)
    plt.show()




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_=5_00    ,   lowercase_=None    ,   lowercase_=False					)				->		Optional[int]:

    """simple docstring"""


    __UpperCamelCase     						=  initial_centroids[:]
    __UpperCamelCase     						=  None

    for itr in range(lowercase_					):
        if verbose:
            print(lowercase_    ,   end=''''''					)

        # 1. Make cluster assignments using nearest centroids
        __UpperCamelCase     						=  assign_clusters(lowercase_    ,   lowercase_					)

        # 2. Compute a new centroid for each of the k clusters, averaging all data
        #    points assigned to that cluster.
        __UpperCamelCase     						=  revise_centroids(lowercase_    ,   lowercase_    ,   lowercase_					)

        # Check for convergence: if none of the assignments changed, stop
        if (
            prev_cluster_assignment is not None
            and (prev_cluster_assignment == cluster_assignment).all()
        ):
            break

        # Print number of new assignments
        if prev_cluster_assignment is not None:
            __UpperCamelCase     						=  np.sum(prev_cluster_assignment != cluster_assignment					)
            if verbose:
                print(
                    F"    {num_changed:5d} elements changed their cluster assignment."					)

        # Record heterogeneity convergence metric
        if record_heterogeneity is not None:
            # YOUR CODE HERE
            __UpperCamelCase     						=  compute_heterogeneity(lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)
            record_heterogeneity.append(lowercase_					)

        __UpperCamelCase     						=  cluster_assignment[:]

    return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
 from sklearn import datasets as ds

 a_      						=   ds.load_iris()
 a_      						=   3
 a_      						=   []
 a_      						=   get_initial_centroids(dataset["data"], k, seed=0)
 a_						,			a_      						=   kmeans(
     dataset["data"],
     k,
     initial_centroids,
     maxiter=400,
     record_heterogeneity=heterogeneity,
     verbose=True,
 )
 plot_heterogeneity(heterogeneity, k)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_=None					)				->		pd.DataFrame:

    """simple docstring"""


    if fill_missing_report:
        __UpperCamelCase     						=  df.fillna(value=lowercase_					)
    __UpperCamelCase     						=  1
    __UpperCamelCase     						=  df.select_dtypes(np.number					).columns
    __UpperCamelCase     						=  (
        df.groupby(['''Cluster''']					)[  # construct report dataframe
            numeric_cols
        ]  # group by cluster number
        .agg(
            [
                ('''sum''', np.sum),
                ('''mean_with_zeros''', lambda lowercase_					: np.mean(np.nan_to_num(lowercase_					)					)),
                ('''mean_without_zeros''', lambda lowercase_					: x.replace(0    ,   np.NaN					).mean()),
                (
                    '''mean_25-75''',
                    lambda lowercase_					: np.mean(
                        np.nan_to_num(
                            sorted(lowercase_					)[
                                round(len(lowercase_					) * 25 / 1_00					) : round(len(lowercase_					) * 75 / 1_00					)
                            ]					)					),
                ),
                ('''mean_with_na''', np.mean),
                ('''min''', lambda lowercase_					: x.min()),
                ('''5%''', lambda lowercase_					: x.quantile(0.05					)),
                ('''25%''', lambda lowercase_					: x.quantile(0.25					)),
                ('''50%''', lambda lowercase_					: x.quantile(0.50					)),
                ('''75%''', lambda lowercase_					: x.quantile(0.75					)),
                ('''95%''', lambda lowercase_					: x.quantile(0.95					)),
                ('''max''', lambda lowercase_					: x.max()),
                ('''count''', lambda lowercase_					: x.count()),
                ('''stdev''', lambda lowercase_					: x.std()),
                ('''mode''', lambda lowercase_					: x.mode()[0]),
                ('''median''', lambda lowercase_					: x.median()),
                ('''# > 0''', lambda lowercase_					: (x > 0).sum()),
            ]					)
        .T.reset_index()
        .rename(index=lowercase_    ,   columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}					)
    )  # rename columns
    # calculate the size of cluster(count of clientID's)
    __UpperCamelCase     						=  report[
        (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
    ].copy()  # avoid SettingWithCopyWarning
    __UpperCamelCase     						=  (
        '''ClusterSize'''  # rename created cluster df to match report column names
    )
    __UpperCamelCase     						=  '''# of Customers'''
    __UpperCamelCase     						=  pd.DataFrame(
        clustersize.iloc[:, 2:].values
        / clustersize.iloc[:, 2:].values.sum()					)
    __UpperCamelCase     						=  '''% of Customers'''  # rename created cluster df to match report column names
    __UpperCamelCase     						=  '''ClusterProportion'''
    __UpperCamelCase     						=  clusterproportion.columns.tolist()
    __UpperCamelCase     						=  cols[-2:] + cols[:-2]
    __UpperCamelCase     						=  clusterproportion[cols]  # rearrange columns to match report
    __UpperCamelCase     						=  report.columns
    __UpperCamelCase     						=  pd.DataFrame(
        abs(
            report[report['''Type'''] == '''count'''].iloc[:, 2:].values
            - clustersize.iloc[:, 2:].values					)					)  # generating df with count of nan values
    __UpperCamelCase     						=  0
    __UpperCamelCase     						=  '''# of nan'''
    __UpperCamelCase     						=  report[
        report['''Type'''] == '''count'''
    ].Features.tolist()  # filling values in order to match report
    __UpperCamelCase     						=  a.columns.tolist()
    __UpperCamelCase     						=  cols[-2:] + cols[:-2]
    __UpperCamelCase     						=  a[cols]  # rearrange columns to match report
    __UpperCamelCase     						=  report.columns  # rename columns to match report
    __UpperCamelCase     						=  report.drop(
        report[report.Type == '''count'''].index					)  # drop count values except cluster size
    __UpperCamelCase     						=  pd.concat(
        [report, a, clustersize, clusterproportion]    ,   axis=0					)  # concat report with clustert size and nan values
    __UpperCamelCase     						=  report['''Features'''].isin(lowercase_					)
    __UpperCamelCase     						=  report.columns.tolist()
    __UpperCamelCase     						=  cols[0:2] + cols[-1:] + cols[2:-1]
    __UpperCamelCase     						=  report[cols]
    __UpperCamelCase     						=  {
        '''ClusterSize''': 9,
        '''ClusterProportion''': 8,
        '''mean_with_zeros''': 7,
        '''mean_with_na''': 6,
        '''max''': 5,
        '''50%''': 4,
        '''min''': 3,
        '''25%''': 2,
        '''75%''': 1,
        '''# of nan''': 0,
        '''# > 0''': -1,
        '''sum_with_na''': -2,
    }
    __UpperCamelCase     						=  (
        report.assign(
            Sortera=lambda lowercase_					: x.Type.map(lowercase_					)    ,   Sortera=lambda lowercase_					: list(reversed(range(len(lowercase_					)					)					)					)    ,   )
        .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']    ,   ascending=lowercase_					)
        .drop(['''Sorter1''', '''Sorter2''']    ,   axis=1					)
    )
    __UpperCamelCase     						=  ''''''
    __UpperCamelCase     						=  report.reset_index()
    __UpperCamelCase     						=  report.drop(columns=['''index''']					)
    return report


if __name__ == "__main__":
 import doctest

 doctest.testmod()












# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
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"""

     _snake_case:		CommonSchedulerState
     _snake_case:		jnp.ndarray

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




     @classmethod
     def        __snake_case      (			cls     :      Optional[int]		,     A__     :      CommonSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				Any:
           return cls(
               common=A__		,     final_alpha_cumprod=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     )






@dataclass
class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		DDIMSchedulerState






class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		List[str]   =[e.name for e in FlaxKarrasDiffusionSchedulers]

     _snake_case:		jnp.dtype




     @property
     def        __snake_case      (			self     :      Optional[int]	) ->				List[str]:
           return True




     @register_to_config
     def __init__(			self     :      Optional[Any]		,     A__     :      int = 1_0_0_0		,     A__     :      float = 0.0001		,     A__     :      float = 0.02		,     A__     :      str = "linear"		,     A__     :      Optional[jnp.ndarray] = None		,     A__     :      bool = True		,     A__     :      int = 0		,     A__     :      str = "epsilon"		,     A__     :      jnp.dtype = jnp.floataa		,     ) ->				Optional[int]:
           lowerCamelCase__					     = dtype




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Optional[CommonSchedulerState] = None	) ->				DDIMSchedulerState:
           if common is None:
                 lowerCamelCase__					     = CommonSchedulerState.create(self	)

           # At every step in ddim, we are looking into the previous alphas_cumprod
           # For the final step, there is no previous alphas_cumprod because we are already at 0
           # `set_alpha_to_one` decides whether we set this parameter simply to one or
           # whether we use the final alpha of the "non-previous" one.
           lowerCamelCase__					     = (
               jnp.array(1.0		,     dtype=self.dtype	) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
           )

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

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

           return DDIMSchedulerState.create(
               common=A__		,     final_alpha_cumprod=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     )




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      Optional[int] = None	) ->				jnp.ndarray:
           return sample




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      DDIMSchedulerState		,     A__     :      int		,     A__     :      Tuple = ()	) ->				DDIMSchedulerState:
           lowerCamelCase__					     = 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
           lowerCamelCase__					     = (jnp.arange(0		,     A__	) * step_ratio).round()[::-1] + self.config.steps_offset

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




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      DDIMSchedulerState		,     A__     :      List[str]		,     A__     :      Optional[Any]	) ->				int:
           lowerCamelCase__					     = state.common.alphas_cumprod[timestep]
           lowerCamelCase__					     = jnp.where(
               prev_timestep >= 0		,     state.common.alphas_cumprod[prev_timestep]		,     state.final_alpha_cumprod	)
           lowerCamelCase__					     = 1 - alpha_prod_t
           lowerCamelCase__					     = 1 - alpha_prod_t_prev

           lowerCamelCase__					     = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance




     def        __snake_case      (			self     :      List[str]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      int		,     A__     :      jnp.ndarray		,     A__     :      float = 0.0		,     A__     :      bool = True		,     ) ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
           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'	)

           # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
           # Ideally, read DDIM paper in-detail understanding

           # Notation (<variable name> -> <name in paper>
           # - pred_noise_t -> e_theta(x_t, t)
           # - pred_original_sample -> f_theta(x_t, t) or x_0
           # - std_dev_t -> sigma_t
           # - eta -> η
           # - pred_sample_direction -> "direction pointing to x_t"
           # - pred_prev_sample -> "x_t-1"

           # 1. get previous step value (=t-1)
           lowerCamelCase__					     = timestep - self.config.num_train_timesteps // state.num_inference_steps

           lowerCamelCase__					     = state.common.alphas_cumprod
           lowerCamelCase__					     = state.final_alpha_cumprod

           # 2. compute alphas, betas
           lowerCamelCase__					     = alphas_cumprod[timestep]
           lowerCamelCase__					     = jnp.where(prev_timestep >= 0		,     alphas_cumprod[prev_timestep]		,     A__	)

           lowerCamelCase__					     = 1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                 lowerCamelCase__					     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 lowerCamelCase__					     = model_output
           elif self.config.prediction_type == "sample":
                 lowerCamelCase__					     = model_output
                 lowerCamelCase__					     = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                 lowerCamelCase__					     = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 lowerCamelCase__					     = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                 raise ValueError(
                     F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                     ' `v_prediction`'	)

           # 4. compute variance: "sigma_t(η)" -> see formula (16)
           # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
           lowerCamelCase__					     = self._get_variance(A__		,     A__		,     A__	)
           lowerCamelCase__					     = eta * variance ** 0.5

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__					     = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__					     = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                 return (prev_sample, state)

           return FlaxDDIMSchedulerOutput(prev_sample=A__		,     state=A__	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           return add_noise_common(state.common		,     A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           return get_velocity_common(state.common		,     A__		,     A__		,     A__	)




     def __len__(			self     :      Any	) ->				Union[str, Any]:
           return self.config.num_train_timesteps