






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""")

SCREAMING_SNAKE_CASE__		:       str   							=							"""K-MEANS-CLUST/ """





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None					)   -> List[str]:

					'''simple docstring'''
					if seed is not None:  # useful for obtaining consistent results
										np.random.seed(__lowerCamelCase					)
					UpperCAmelCase__			:     int										= data.shape[0]  # number of data points

					# Pick K indices from range [0, N).
					UpperCAmelCase__			:     str										= np.random.randint(0   ,       __lowerCamelCase   ,       __lowerCamelCase					)

					# 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__			:     Optional[int]										= data[rand_indices, :]

					return centroids





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:

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





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> str:

					'''simple docstring'''
					UpperCAmelCase__			:     Optional[Any]										= centroid_pairwise_dist(__lowerCamelCase   ,       __lowerCamelCase					)

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

					return cluster_assignment





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

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

					return new_centroids





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> List[str]:

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

					return heterogeneity





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

					'''simple docstring'''
					plt.figure(figsize=(7, 4)					)
					plt.plot(__lowerCamelCase   ,       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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=500   ,       __lowerCamelCase=None   ,       __lowerCamelCase=False					)   -> Union[str, Any]:

					'''simple docstring'''
					UpperCAmelCase__			:     Tuple										= initial_centroids[:]
					UpperCAmelCase__			:     Union[str, Any]										= None

					for itr in range(__lowerCamelCase					):
										if verbose:
															print(__lowerCamelCase   ,       end=""""""					)

										# 1. Make cluster assignments using nearest centroids
										UpperCAmelCase__			:     Optional[int]										= assign_clusters(__lowerCamelCase   ,       __lowerCamelCase					)

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

										# 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__			:     Tuple										= 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__			:     str										= compute_heterogeneity(__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)
															record_heterogeneity.append(__lowerCamelCase					)

										UpperCAmelCase__			:     str										= 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

						SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							ds.load_iris()
						SCREAMING_SNAKE_CASE__		:       Tuple   							=							3
						SCREAMING_SNAKE_CASE__		:       List[str]   							=							[]
						SCREAMING_SNAKE_CASE__		:       List[Any]   							=							get_initial_centroids(dataset["""data"""], k, seed=0)
						SCREAMING_SNAKE_CASE__		:       Dict   							=							kmeans(
						    dataset["""data"""],
						    k,
						    initial_centroids,
						    maxiter=4_00,
						    record_heterogeneity=heterogeneity,
						    verbose=True,
						)
						plot_heterogeneity(heterogeneity, k)





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None					)   -> pd.DataFrame:

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       str   							=							[
          """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DinatForImageClassification""",
          """DinatModel""",
          """DinatPreTrainedModel""",
          """DinatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






