
"""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""")

lowerCAmelCase								=						"""K-MEANS-CLUST/ """






def        lowerCAmelCase_       (		snake_case_		:      Optional[int]							,   snake_case_		:      Any							,   snake_case_		:      Optional[Any]=None      )       ->List[Any]:
			if seed is not None:  # useful for obtaining consistent results
						np.random.seed(snake_case_      )
			lowerCamelCase__     :					Optional[Any]		=data.shape[0]  # number of data points

			# Pick K indices from range [0, N).
			lowerCamelCase__     :					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.
			lowerCamelCase__     :					Optional[int]		=data[rand_indices, :]

			return centroids






def        lowerCAmelCase_       (		snake_case_		:      Tuple							,   snake_case_		:      Optional[Any]      )       ->List[Any]:
			return pairwise_distances(snake_case_							,   snake_case_							,   metric='euclidean'      )






def        lowerCAmelCase_       (		snake_case_		:      Optional[int]							,   snake_case_		:      Any      )       ->Union[str, Any]:
			# Compute distances between each data point and the set of centroids:
			# Fill in the blank (RHS only)
			lowerCamelCase__     :					Optional[Any]		=centroid_pairwise_dist(snake_case_							,   snake_case_      )

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

			return cluster_assignment






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Dict							,   snake_case_		:      int      )       ->Optional[Any]:
			lowerCamelCase__     :					int		=[]
			for i in range(snake_case_      ):
						# Select all data points that belong to cluster i. Fill in the blank (RHS only)
						lowerCamelCase__     :					str		=data[cluster_assignment == i]
						# Compute the mean of the data points. Fill in the blank (RHS only)
						lowerCamelCase__     :					Union[str, Any]		=member_data_points.mean(axis=0      )
						new_centroids.append(snake_case_      )
			lowerCamelCase__     :					List[Any]		=np.array(snake_case_      )

			return new_centroids






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Optional[int]							,   snake_case_		:      Dict							,   snake_case_		:      Any      )       ->Tuple:
			lowerCamelCase__     :					Union[str, Any]		=0.0
			for i in range(snake_case_      ):
						# Select all data points that belong to cluster i. Fill in the blank (RHS only)
						lowerCamelCase__     :					Optional[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)
									lowerCamelCase__     :					Any		=pairwise_distances(
									    snake_case_							,   [centroids[i]]							,   metric='euclidean'      )
									lowerCamelCase__     :					Dict		=distances**2
									heterogeneity += np.sum(snake_case_      )

			return heterogeneity






def        lowerCAmelCase_       (		snake_case_		:      Optional[int]							,   snake_case_		:      List[Any]      )       ->Optional[Any]:
			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': 1_6}      )
			plt.show()






def        lowerCAmelCase_       (		snake_case_		:      Optional[Any]							,   snake_case_		:      int							,   snake_case_		:      List[Any]							,   snake_case_		:      str=5_0_0							,   snake_case_		:      Union[str, Any]=None							,   snake_case_		:      Any=False      )       ->List[str]:
			lowerCamelCase__     :					Optional[int]		=initial_centroids[:]
			lowerCamelCase__     :					Optional[int]		=None

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

						# 1. Make cluster assignments using nearest centroids
						lowerCamelCase__     :					Tuple		=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.
						lowerCamelCase__     :					Optional[int]		=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:
									lowerCamelCase__     :					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
									lowerCamelCase__     :					Dict		=compute_heterogeneity(snake_case_							,   snake_case_							,   snake_case_							,   snake_case_      )
									record_heterogeneity.append(snake_case_      )

						lowerCamelCase__     :					List[Any]		=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

							lowerCAmelCase								=						ds.load_iris()
							lowerCAmelCase								=						3
							lowerCAmelCase								=						[]
							lowerCAmelCase								=						get_initial_centroids(dataset["""data"""], k, seed=0)
							lowerCAmelCase		,					lowerCAmelCase								=						kmeans(
							    dataset["""data"""],
							    k,
							    initial_centroids,
							    maxiter=4_00,
							    record_heterogeneity=heterogeneity,
							    verbose=True,
							)
							plot_heterogeneity(heterogeneity, k)






def        lowerCAmelCase_       (		snake_case_		:      pd.DataFrame							,   snake_case_		:      np.ndarray							,   snake_case_		:      List[str]=None      )       ->pd.DataFrame:
			# Fill missing values with given rules
			if fill_missing_report:
						lowerCamelCase__     :					Tuple		=df.fillna(value=snake_case_      )
			lowerCamelCase__     :					Any		=1
			lowerCamelCase__     :					Dict		=df.select_dtypes(np.number      ).columns
			lowerCamelCase__     :					Any		=(
			    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_      ) * 2_5 / 1_0_0      ) : round(len(snake_case_      ) * 7_5 / 1_0_0      )
			                        ]      )      ),
			            ),
			            ('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)
			lowerCamelCase__     :					Optional[Any]		=report[
			    (report['Features'] == 'dummy') & (report['Type'] == 'count')
			].copy()  # avoid SettingWithCopyWarning
			lowerCamelCase__     :					Optional[int]		=(
			    'ClusterSize'  # rename created cluster df to match report column names
			)
			lowerCamelCase__     :					Dict		='# of Customers'
			lowerCamelCase__     :					Optional[int]		=pd.DataFrame(
			    clustersize.iloc[:, 2:].values
			    / clustersize.iloc[:, 2:].values.sum()      )
			lowerCamelCase__     :					Optional[Any]		='% of Customers'  # rename created cluster df to match report column names
			lowerCamelCase__     :					Union[str, Any]		='ClusterProportion'
			lowerCamelCase__     :					Any		=clusterproportion.columns.tolist()
			lowerCamelCase__     :					Union[str, Any]		=cols[-2:] + cols[:-2]
			lowerCamelCase__     :					Optional[int]		=clusterproportion[cols]  # rearrange columns to match report
			lowerCamelCase__     :					Tuple		=report.columns
			lowerCamelCase__     :					Union[str, Any]		=pd.DataFrame(
			    abs(
			        report[report['Type'] == 'count'].iloc[:, 2:].values
			        - clustersize.iloc[:, 2:].values      )      )  # generating df with count of nan values
			lowerCamelCase__     :					Dict		=0
			lowerCamelCase__     :					Dict		='# of nan'
			lowerCamelCase__     :					Dict		=report[
			    report['Type'] == 'count'
			].Features.tolist()  # filling values in order to match report
			lowerCamelCase__     :					Optional[int]		=a.columns.tolist()
			lowerCamelCase__     :					Dict		=cols[-2:] + cols[:-2]
			lowerCamelCase__     :					Dict		=a[cols]  # rearrange columns to match report
			lowerCamelCase__     :					int		=report.columns  # rename columns to match report
			lowerCamelCase__     :					Optional[Any]		=report.drop(
			    report[report.Type == 'count'].index      )  # drop count values except cluster size
			lowerCamelCase__     :					int		=pd.concat(
			    [report, a, clustersize, clusterproportion]							,   axis=0      )  # concat report with clustert size and nan values
			lowerCamelCase__     :					List[str]		=report['Features'].isin(snake_case_      )
			lowerCamelCase__     :					Union[str, Any]		=report.columns.tolist()
			lowerCamelCase__     :					List[str]		=cols[0:2] + cols[-1:] + cols[2:-1]
			lowerCamelCase__     :					Tuple		=report[cols]
			lowerCamelCase__     :					Tuple		={
			    '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,
			}
			lowerCamelCase__     :					str		=(
			    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      )
			)
			lowerCamelCase__     :					str		=''
			lowerCamelCase__     :					Union[str, Any]		=report.reset_index()
			lowerCamelCase__     :					List[str]		=report.drop(columns=['index']      )
			return report


if __name__ == "__main__":
							import doctest

							doctest.testmod()

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}

class    A_     (					A__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """nystromformer"""





							def __init__(  self  :Optional[Any]					,							lowerCamelCase_  :Optional[Any]=30_000					,							lowerCamelCase_  :str=768					,							lowerCamelCase_  :str=12					,							lowerCamelCase_  :str=12					,							lowerCamelCase_  :Any=3_072					,							lowerCamelCase_  :Optional[int]="gelu_new"					,							lowerCamelCase_  :List[Any]=0.1					,							lowerCamelCase_  :List[str]=0.1					,							lowerCamelCase_  :Any=510					,							lowerCamelCase_  :Optional[int]=2					,							lowerCamelCase_  :List[Any]=64					,							lowerCamelCase_  :Union[str, Any]=64					,							lowerCamelCase_  :str=65					,							lowerCamelCase_  :Union[str, Any]=False					,							lowerCamelCase_  :str=0.02					,							lowerCamelCase_  :Any=1e-5					,							lowerCamelCase_  :int=1					,							lowerCamelCase_  :Optional[int]=0					,							lowerCamelCase_  :str=2					,							**lowerCamelCase_  :int					,							):





										"""simple docstring"""

										lowerCamelCase__     :					int		=vocab_size
										lowerCamelCase__     :					Optional[int]		=max_position_embeddings
										lowerCamelCase__     :					List[Any]		=hidden_size
										lowerCamelCase__     :					Tuple		=num_hidden_layers
										lowerCamelCase__     :					Optional[Any]		=num_attention_heads
										lowerCamelCase__     :					List[str]		=intermediate_size
										lowerCamelCase__     :					Union[str, Any]		=hidden_act
										lowerCamelCase__     :					int		=hidden_dropout_prob
										lowerCamelCase__     :					Tuple		=attention_probs_dropout_prob
										lowerCamelCase__     :					Dict		=initializer_range
										lowerCamelCase__     :					Tuple		=type_vocab_size
										lowerCamelCase__     :					Any		=segment_means_seq_len
										lowerCamelCase__     :					int		=num_landmarks
										lowerCamelCase__     :					Tuple		=conv_kernel_size
										lowerCamelCase__     :					Optional[int]		=inv_coeff_init_option
										lowerCamelCase__     :					List[str]		=layer_norm_eps
										super().__init__(pad_token_id=lowerCamelCase_					,							bos_token_id=lowerCamelCase_					,							eos_token_id=lowerCamelCase_					,							**lowerCamelCase_	)