




'''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""")

a_   :	Optional[Any]							   = """K-MEANS-CLUST/ """





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    str     , __snake_case							:    int=None     )							->		Dict:



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

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

       return centroids





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



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





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    Optional[Any]     )							->		Tuple:



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

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

       return cluster_assignment





def   a_							(   __snake_case							:    str     , __snake_case							:    Tuple     , __snake_case							:    str     )							->		str:



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

       return new_centroids





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    Any     , __snake_case							:    Optional[Any]     , __snake_case							:    Optional[int]     )							->		List[Any]:



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

       return heterogeneity





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    Optional[int]     )							->		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   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    Tuple     , __snake_case							:    Dict     , __snake_case							:    List[Any]=500     , __snake_case							:    Any=None     , __snake_case							:    Optional[Any]=False     )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=initial_centroids[:]
       lowerCamelCase_							=None

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

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

              # 2. Compute a new centroid for each of the k clusters, averaging all data
              #    points assigned to that cluster.
              lowerCamelCase_							=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:
                     lowerCamelCase_							=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_							=compute_heterogeneity(lowercase_     , lowercase_     , lowercase_     , lowercase_     )
                     record_heterogeneity.append(lowercase_     )

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





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    Optional[int]     , __snake_case							:    Dict=None     )							->		pd.DataFrame:



       """simple docstring"""
       # Fill missing values with given rules
       if fill_missing_report:
              lowerCamelCase_							=df.fillna(value=lowercase_     )
       lowerCamelCase_							=1
       lowerCamelCase_							=df.select_dtypes(np.number     ).columns
       lowerCamelCase_							=(
           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(lowercase_     )     )),
                   ('''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(lowercase_     )[
                                   round(len(lowercase_     ) * 25 / 100     ) : round(len(lowercase_     ) * 75 / 100     )
                               ]     )     ),
                   ),
                   ('''mean_with_na''', np.mean),
                   ('''min''', lambda __snake_case     : x.min()),
                   ('''5%''', lambda __snake_case     : x.quantile(0.0_5     )),
                   ('''25%''', lambda __snake_case     : x.quantile(0.2_5     )),
                   ('''50%''', lambda __snake_case     : x.quantile(0.5_0     )),
                   ('''75%''', lambda __snake_case     : x.quantile(0.7_5     )),
                   ('''95%''', lambda __snake_case     : x.quantile(0.9_5     )),
                   ('''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=lowercase_     , columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}     )
       )  # rename columns
       # calculate the size of cluster(count of clientID's)
       lowerCamelCase_							=report[
           (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
       ].copy()  # avoid SettingWithCopyWarning
       lowerCamelCase_							=(
           '''ClusterSize'''  # rename created cluster df to match report column names
       )
       lowerCamelCase_							='''# of Customers'''
       lowerCamelCase_							=pd.DataFrame(
           clustersize.iloc[:, 2:].values
           / clustersize.iloc[:, 2:].values.sum()     )
       lowerCamelCase_							='''% of Customers'''  # rename created cluster df to match report column names
       lowerCamelCase_							='''ClusterProportion'''
       lowerCamelCase_							=clusterproportion.columns.tolist()
       lowerCamelCase_							=cols[-2:] + cols[:-2]
       lowerCamelCase_							=clusterproportion[cols]  # rearrange columns to match report
       lowerCamelCase_							=report.columns
       lowerCamelCase_							=pd.DataFrame(
           abs(
               report[report['''Type'''] == '''count'''].iloc[:, 2:].values
               - clustersize.iloc[:, 2:].values     )     )  # generating df with count of nan values
       lowerCamelCase_							=0
       lowerCamelCase_							='''# of nan'''
       lowerCamelCase_							=report[
           report['''Type'''] == '''count'''
       ].Features.tolist()  # filling values in order to match report
       lowerCamelCase_							=a.columns.tolist()
       lowerCamelCase_							=cols[-2:] + cols[:-2]
       lowerCamelCase_							=a[cols]  # rearrange columns to match report
       lowerCamelCase_							=report.columns  # rename columns to match report
       lowerCamelCase_							=report.drop(
           report[report.Type == '''count'''].index     )  # drop count values except cluster size
       lowerCamelCase_							=pd.concat(
           [report, a, clustersize, clusterproportion]     , axis=0     )  # concat report with clustert size and nan values
       lowerCamelCase_							=report['''Features'''].isin(lowercase_     )
       lowerCamelCase_							=report.columns.tolist()
       lowerCamelCase_							=cols[0:2] + cols[-1:] + cols[2:-1]
       lowerCamelCase_							=report[cols]
       lowerCamelCase_							={
           '''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_							=(
           report.assign(
               Sortera=lambda __snake_case     : x.Type.map(lowercase_     )     , Sortera=lambda __snake_case     : list(reversed(range(len(lowercase_     )     )     )     )     , )
           .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']     , ascending=lowercase_     )
           .drop(['''Sorter1''', '''Sorter2''']     , axis=1     )
       )
       lowerCamelCase_							=''''''
       lowerCamelCase_							=report.reset_index()
       lowerCamelCase_							=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 tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbartaa import MBartaaTokenizer
else:
     a_   :	Tuple							   = None


a_   :	List[Any]							   = logging.get_logger(__name__)

a_   :	Any							   = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Any							   = {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

a_   :	Dict							   = {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
a_   :	List[str]							   = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Any   		=VOCAB_FILES_NAMES
       lowercase			:					str   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					Dict   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=MBartaaTokenizer

       lowercase			:					List[int]   		=[]
       lowercase			:					List[int]   		=[]






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="</s>",	lowerCAmelCase="</s>",	lowerCAmelCase="<s>",	lowerCAmelCase="<unk>",	lowerCAmelCase="<pad>",	lowerCAmelCase="<mask>",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=AddedToken(lowerCAmelCase,	lstrip=lowerCAmelCase,	rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase,	lowerCAmelCase						) else mask_token

              lowerCamelCase_							=kwargs.get('''additional_special_tokens''',	[]						)
              kwargs["additional_special_tokens"] += [
                  code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
              ]

              super().__init__(
                  lowerCAmelCase,	src_lang=lowerCAmelCase,	tgt_lang=lowerCAmelCase,	tokenizer_file=lowerCAmelCase,	eos_token=lowerCAmelCase,	sep_token=lowerCAmelCase,	cls_token=lowerCAmelCase,	unk_token=lowerCAmelCase,	pad_token=lowerCAmelCase,	mask_token=lowerCAmelCase,	**lowerCAmelCase,	)

              lowerCamelCase_							=vocab_file
              lowerCamelCase_							=False if not self.vocab_file else True

              lowerCamelCase_							={
                  lang_code: self.convert_tokens_to_ids(lowerCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }

              lowerCamelCase_							=src_lang if src_lang is not None else '''en_XX'''
              lowerCamelCase_							=tgt_lang
              lowerCamelCase_							=self.lang_code_to_id[self._src_lang]
              self.set_src_lang_special_tokens(self._src_lang						)






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self._src_lang






       @src_lang.setter
       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=new_src_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + self.suffix_tokens
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = "en_XX",	lowerCAmelCase = None,	lowerCAmelCase = "ro_RO",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=src_lang
              lowerCamelCase_							=tgt_lang
              return super().prepare_seqaseq_batch(lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_src_lang_special_tokens(self.src_lang						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_tgt_lang_special_tokens(self.tgt_lang						)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

              lowerCamelCase_							=self.convert_ids_to_tokens(self.prefix_tokens						)
              lowerCamelCase_							=self.convert_ids_to_tokens(self.suffix_tokens						)

              lowerCamelCase_							=processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,	self.prefix_tokens + self.suffix_tokens						)						),	)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

              lowerCamelCase_							=self.convert_ids_to_tokens(self.prefix_tokens						)
              lowerCamelCase_							=self.convert_ids_to_tokens(self.suffix_tokens						)

              lowerCamelCase_							=processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,	self.prefix_tokens + self.suffix_tokens						)						),	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              if src_lang is None or tgt_lang is None:
                     raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''						)
              lowerCamelCase_							=src_lang
              lowerCamelCase_							=self(lowerCAmelCase,	add_special_tokens=lowerCAmelCase,	return_tensors=lowerCAmelCase,	**lowerCAmelCase						)
              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=tgt_lang_id
              return inputs






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              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(lowerCAmelCase						):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                     return
              lowerCamelCase_							=os.path.join(
                  lowerCAmelCase,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

              return (out_vocab_file,)




