'''simple docstring'''





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""")

UpperCamelCase       :     List[str]			=    """K-MEANS-CLUST/ """






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Union[str, Any]				,	snake_case       :				Dict=None  )   ->				Optional[Any]:







     """simple docstring"""
     if seed is not None:  # useful for obtaining consistent results
          np.random.seed(snake_case  )
     a					:							Optional[Any]							= data.shape[0]  # number of data points

     # Pick K indices from range [0, N).
     a					:							Union[str, Any]							= np.random.randint(0				,	snake_case				,	snake_case  )

     # 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.
     a					:							int							= data[rand_indices, :]

     return centroids






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[str]				,	snake_case       :				Dict  )   ->				List[Any]:







     """simple docstring"""
     return pairwise_distances(snake_case				,	snake_case				,	metric='euclidean'  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[Any]				,	snake_case       :				Optional[int]  )   ->				str:







     """simple docstring"""
     # Compute distances between each data point and the set of centroids:
     # Fill in the blank (RHS only)
     a					:							str							= centroid_pairwise_dist(snake_case				,	snake_case  )

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

     return cluster_assignment






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Union[str, Any]				,	snake_case       :				Any  )   ->				Union[str, Any]:







     """simple docstring"""
     a					:							Tuple							= []
     for i in range(snake_case  ):
          # Select all data points that belong to cluster i. Fill in the blank (RHS only)
          a					:							List[Any]							= data[cluster_assignment == i]
          # Compute the mean of the data points. Fill in the blank (RHS only)
          a					:							List[str]							= member_data_points.mean(axis=0  )
          new_centroids.append(snake_case  )
     a					:							Tuple							= np.array(snake_case  )

     return new_centroids






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Dict				,	snake_case       :				List[Any]				,	snake_case       :				List[Any]  )   ->				Optional[Any]:







     """simple docstring"""
     a					:							Tuple							= 0.0
     for i in range(snake_case  ):
          # Select all data points that belong to cluster i. Fill in the blank (RHS only)
          a					:							List[str]							= 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)
               a					:							List[Any]							= pairwise_distances(
                   snake_case				,	[centroids[i]]				,	metric='euclidean'  )
               a					:							int							= distances**2
               heterogeneity += np.sum(snake_case  )

     return heterogeneity






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				Dict  )   ->				Tuple:







     """simple docstring"""
     plt.figure(figsize=(7, 4)  )
     plt.plot(snake_case				,	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__  (				snake_case       :				List[Any]				,	snake_case       :				Optional[int]				,	snake_case       :				str				,	snake_case       :				Dict=500				,	snake_case       :				Union[str, Any]=None				,	snake_case       :				Union[str, Any]=False  )   ->				str:







     """simple docstring"""
     a					:							List[str]							= initial_centroids[:]
     a					:							List[str]							= None

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

          # 1. Make cluster assignments using nearest centroids
          a					:							Union[str, Any]							= assign_clusters(snake_case				,	snake_case  )

          # 2. Compute a new centroid for each of the k clusters, averaging all data
          #    points assigned to that cluster.
          a					:							Union[str, Any]							= revise_centroids(snake_case				,	snake_case				,	snake_case  )

          # 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:
               a					:							Union[str, Any]							= 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
               a					:							List[str]							= compute_heterogeneity(snake_case				,	snake_case				,	snake_case				,	snake_case  )
               record_heterogeneity.append(snake_case  )

          a					:							int							= 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

      UpperCamelCase       :     List[str]			=    ds.load_iris()
      UpperCamelCase       :     str			=    3
      UpperCamelCase       :     Any			=    []
      UpperCamelCase       :     Dict			=    get_initial_centroids(dataset["""data"""], k, seed=0)
      UpperCamelCase	,				UpperCamelCase       :     Optional[Any]			=    kmeans(
          dataset["""data"""],
          k,
          initial_centroids,
          maxiter=400,
          record_heterogeneity=heterogeneity,
          verbose=True,
      )
      plot_heterogeneity(heterogeneity, k)






def SCREAMING_SNAKE_CASE__  (				snake_case       :				pd.DataFrame				,	snake_case       :				np.ndarray				,	snake_case       :				Tuple=None  )   ->				pd.DataFrame:







     """simple docstring"""
     # Fill missing values with given rules
     if fill_missing_report:
          a					:							Dict							= df.fillna(value=snake_case  )
     a					:							List[str]							= 1
     a					:							str							= df.select_dtypes(np.number  ).columns
     a					:							List[str]							= (
         df.groupby(['Cluster']  )[  # construct report dataframe
             numeric_cols
         ]  # group by cluster number
         .agg(
             [
                 ('sum', np.sum),
                 ('mean_with_zeros', lambda snake_case  : np.mean(np.nan_to_num(snake_case  )  )),
                 ('mean_without_zeros', lambda snake_case  : x.replace(0				,	np.NaN  ).mean()),
                 (
                     'mean_25-75',
                     lambda snake_case  : np.mean(
                         np.nan_to_num(
                             sorted(snake_case  )[
                                 round(len(snake_case  ) * 25 / 100  ) : round(len(snake_case  ) * 75 / 100  )
                             ]  )  ),
                 ),
                 ('mean_with_na', np.mean),
                 ('min', lambda snake_case  : x.min()),
                 ('5%', lambda snake_case  : x.quantile(0.05  )),
                 ('25%', lambda snake_case  : x.quantile(0.25  )),
                 ('50%', lambda snake_case  : x.quantile(0.50  )),
                 ('75%', lambda snake_case  : x.quantile(0.75  )),
                 ('95%', lambda snake_case  : x.quantile(0.95  )),
                 ('max', lambda snake_case  : x.max()),
                 ('count', lambda snake_case  : x.count()),
                 ('stdev', lambda snake_case  : x.std()),
                 ('mode', lambda snake_case  : x.mode()[0]),
                 ('median', lambda snake_case  : x.median()),
                 ('# > 0', lambda snake_case  : (x > 0).sum()),
             ]  )
         .T.reset_index()
         .rename(index=snake_case				,	columns={'level_0': 'Features', 'level_1': 'Type'}  )
     )  # rename columns
     # calculate the size of cluster(count of clientID's)
     a					:							Tuple							= report[
         (report['Features'] == 'dummy') & (report['Type'] == 'count')
     ].copy()  # avoid SettingWithCopyWarning
     a					:							Union[str, Any]							= (
         'ClusterSize'  # rename created cluster df to match report column names
     )
     a					:							List[Any]							= '# of Customers'
     a					:							List[Any]							= pd.DataFrame(
         clustersize.iloc[:, 2:].values
         / clustersize.iloc[:, 2:].values.sum()  )
     a					:							Any							= '% of Customers'  # rename created cluster df to match report column names
     a					:							Any							= 'ClusterProportion'
     a					:							List[Any]							= clusterproportion.columns.tolist()
     a					:							List[str]							= cols[-2:] + cols[:-2]
     a					:							int							= clusterproportion[cols]  # rearrange columns to match report
     a					:							List[str]							= report.columns
     a					:							Union[str, Any]							= pd.DataFrame(
         abs(
             report[report['Type'] == 'count'].iloc[:, 2:].values
             - clustersize.iloc[:, 2:].values  )  )  # generating df with count of nan values
     a					:							int							= 0
     a					:							Optional[int]							= '# of nan'
     a					:							Any							= report[
         report['Type'] == 'count'
     ].Features.tolist()  # filling values in order to match report
     a					:							str							= a.columns.tolist()
     a					:							Union[str, Any]							= cols[-2:] + cols[:-2]
     a					:							Dict							= a[cols]  # rearrange columns to match report
     a					:							str							= report.columns  # rename columns to match report
     a					:							int							= report.drop(
         report[report.Type == 'count'].index  )  # drop count values except cluster size
     a					:							List[str]							= pd.concat(
         [report, a, clustersize, clusterproportion]				,	axis=0  )  # concat report with clustert size and nan values
     a					:							int							= report['Features'].isin(snake_case  )
     a					:							Optional[Any]							= report.columns.tolist()
     a					:							Optional[Any]							= cols[0:2] + cols[-1:] + cols[2:-1]
     a					:							Union[str, Any]							= report[cols]
     a					:							List[Any]							= {
         '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,
     }
     a					:							Optional[int]							= (
         report.assign(
             Sortera=lambda snake_case  : x.Type.map(snake_case  )				,	Sortera=lambda snake_case  : list(reversed(range(len(snake_case  )  )  )  )				,	)
         .sort_values(['Sorter1', 'Mark', 'Sorter2']				,	ascending=snake_case  )
         .drop(['Sorter1', 'Sorter2']				,	axis=1  )
     )
     a					:							int							= ''
     a					:							Union[str, Any]							= report.reset_index()
     a					:							Union[str, Any]							= report.drop(columns=['index']  )
     return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()


'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase       :     Tuple			=    {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

