



"""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       :      Any							=							"K-MEANS-CLUST/ "

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Any=None       )						->     Dict:





     '''simple docstring'''

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

     # Pick K indices from range [0, N).
     _UpperCAmelCase    :			int              =					np.random.randint(0		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_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.
     _UpperCAmelCase    :			Tuple              =					data[rand_indices, :]

     return centroids

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





     '''simple docstring'''

     return pairwise_distances(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						metric="euclidean"       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Tuple       )						->     str:





     '''simple docstring'''

     _UpperCAmelCase    :			Optional[Any]              =					centroid_pairwise_dist(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

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

     return cluster_assignment

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





     '''simple docstring'''

     _UpperCAmelCase    :			Optional[int]              =					[]
     for i in range(SCREAMING_SNAKE_CASE__       ):
          # Select all data points that belong to cluster i. Fill in the blank (RHS only)
          _UpperCAmelCase    :			Dict              =					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(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Optional[int]              =					np.array(SCREAMING_SNAKE_CASE__       )

     return new_centroids

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     str:





     '''simple docstring'''

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

     return heterogeneity

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:





     '''simple docstring'''

     plt.figure(figsize=(7, 4)       )
     plt.plot(SCREAMING_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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		List[str]=500		,						SCREAMING_SNAKE_CASE__			:		Dict=None		,						SCREAMING_SNAKE_CASE__			:		int=False       )						->     Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase    :			str              =					initial_centroids[:]
     _UpperCAmelCase    :			Tuple              =					None

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

          # 1. Make cluster assignments using nearest centroids
          _UpperCAmelCase    :			int              =					assign_clusters(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

          # 2. Compute a new centroid for each of the k clusters, averaging all data
          #    points assigned to that cluster.
          _UpperCAmelCase    :			Optional[Any]              =					revise_centroids(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_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:
               _UpperCAmelCase    :			Dict              =					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    :			Optional[Any]              =					compute_heterogeneity(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
               record_heterogeneity.append(SCREAMING_SNAKE_CASE__       )

          _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

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

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		pd.DataFrame		,						SCREAMING_SNAKE_CASE__			:		np.ndarray		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]=None       )						->     pd.DataFrame:





     '''simple docstring'''

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 _lowerCAmelCase       :      Tuple							=							None


_lowerCAmelCase       :      List[str]							=							logging.get_logger(__name__)

_lowerCAmelCase       :      str							=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase       :      Optional[Any]							=							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase       :      Union[str, Any]							=							{
    "facebook/xglm-564M": 20_48,
}






class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     str				  =				VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:     List[str]				  =				PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:     str				  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:     int				  =				['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:     str				  =				XGLMTokenizer



      def __init__(  self  :			List[Any]  ,			A  :			Union[str, Any]=None  ,			A  :			Optional[Any]=None  ,			A  :			Optional[Any]="<s>"  ,			A  :			Optional[int]="</s>"  ,			A  :			str="</s>"  ,			A  :			int="<s>"  ,			A  :			int="<unk>"  ,			A  :			List[str]="<pad>"  ,			**A  :			Tuple  ,			):
           # Compatibility with the original tokenizer
           _UpperCAmelCase    :			Union[str, Any]              =					7
           _UpperCAmelCase    :			Optional[int]              =					[f'<madeupword{i}>' for i in range(self.num_madeup_words		)]

           _UpperCAmelCase    :			Any              =					kwargs.get("additional_special_tokens"  ,			[]		)
           kwargs["additional_special_tokens"] += [
               word for word in madeup_words if word not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               A  ,			tokenizer_file=A  ,			bos_token=A  ,			eos_token=A  ,			sep_token=A  ,			cls_token=A  ,			unk_token=A  ,			pad_token=A  ,			**A  ,			)

           _UpperCAmelCase    :			Any              =					vocab_file
           _UpperCAmelCase    :			Union[str, Any]              =					False if not self.vocab_file else True



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):

           if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
           _UpperCAmelCase    :			Tuple              =					[self.sep_token_id]
           return sep + token_ids_a + sep + sep + token_ids_a



      def 		snake_case_      (  self  :			str  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
           _UpperCAmelCase    :			Optional[int]              =					[self.sep_token_id]

           if token_ids_a is None:
                return len(sep + token_ids_a		) * [0]
           return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]



      def 		snake_case_      (  self  :			Dict  ,			A  :			str  ,			A  :			Optional[str] = None		):
           if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."		)

           if not os.path.isdir(A		):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory.'		)
                return
           _UpperCAmelCase    :			str              =					os.path.join(
               A  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

           if os.path.abspath(self.vocab_file		) != os.path.abspath(A		):
                copyfile(self.vocab_file  ,			A		)

           return (out_vocab_file,)
