




'''simple docstring'''







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase			:      List[str]			=					"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

lowercase			:      Tuple			=					"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"


lowercase			:      int			=					"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase							):
     snake_case					   = [rgram for rgrams in rgramslist for rgram in rgrams]
     snake_case					   = Counter(snake_case_							)

     snake_case					   = Counter(snake_case_							)
     snake_case					   = Counter()
     for sgram, scount in sgramcounter.items():
          snake_case					   = scount * numref

     snake_case					   = Counter(snake_case_							)
     snake_case					   = Counter()
     for cgram, ccount in cgramcounter.items():
          snake_case					   = ccount * numref

     # KEEP
     snake_case					   = sgramcounter_rep & cgramcounter_rep
     snake_case					   = keepgramcounter_rep & rgramcounter
     snake_case					   = sgramcounter_rep & rgramcounter

     snake_case					   = 0
     snake_case					   = 0
     for keepgram in keepgramcountergood_rep:
          keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
          # Fix an alleged bug [2] in the keep score computation.
          # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
          keeptmpscorea += keepgramcountergood_rep[keepgram]
     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     #      a target exactly.
     snake_case					   = 1
     snake_case					   = 1
     if len(snake_case_							) > 0:
          snake_case					   = keeptmpscorea / len(snake_case_							)
     if len(snake_case_							) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          snake_case					   = keeptmpscorea / sum(keepgramcounterall_rep.values()							)
     snake_case					   = 0
     if keepscore_precision > 0 or keepscore_recall > 0:
          snake_case					   = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     snake_case					   = sgramcounter_rep - cgramcounter_rep
     snake_case					   = delgramcounter_rep - rgramcounter
     snake_case					   = sgramcounter_rep - rgramcounter
     snake_case					   = 0
     snake_case					   = 0
     for delgram in delgramcountergood_rep:
          deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
          deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     # a target exactly.
     snake_case					   = 1
     if len(snake_case_							) > 0:
          snake_case					   = deltmpscorea / len(snake_case_							)

     # ADDITION
     snake_case					   = set(snake_case_							) - set(snake_case_							)
     snake_case					   = set(snake_case_							) & set(snake_case_							)
     snake_case					   = set(snake_case_							) - set(snake_case_							)

     snake_case					   = 0
     for addgram in addgramcountergood:
          addtmpscore += 1

     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     # a target exactly.
     snake_case					   = 1
     snake_case					   = 1
     if len(snake_case_							) > 0:
          snake_case					   = addtmpscore / len(snake_case_							)
     if len(snake_case_							) > 0:
          snake_case					   = addtmpscore / len(snake_case_							)
     snake_case					   = 0
     if addscore_precision > 0 or addscore_recall > 0:
          snake_case					   = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase							):
     snake_case					   = len(snake_case_							)

     snake_case					   = ssent.split(""" """							)
     snake_case					   = csent.split(""" """							)
     snake_case					   = []
     snake_case					   = []
     snake_case					   = []
     snake_case					   = []
     snake_case					   = []
     snake_case					   = []

     snake_case					   = []
     snake_case					   = []
     snake_case					   = []
     snake_case					   = []
     for rsent in rsents:
          snake_case					   = rsent.split(""" """							)
          snake_case					   = []
          snake_case					   = []
          snake_case					   = []
          ragramslist.append(snake_case_							)
          for i in range(0    ,      len(snake_case_							) - 1							):
               if i < len(snake_case_							) - 1:
                    snake_case					   = ragrams[i] + ''' ''' + ragrams[i + 1]
                    ragrams.append(snake_case_							)
               if i < len(snake_case_							) - 2:
                    snake_case					   = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                    ragrams.append(snake_case_							)
               if i < len(snake_case_							) - 3:
                    snake_case					   = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                    ragrams.append(snake_case_							)
          ragramslist.append(snake_case_							)
          ragramslist.append(snake_case_							)
          ragramslist.append(snake_case_							)

     for i in range(0    ,      len(snake_case_							) - 1							):
          if i < len(snake_case_							) - 1:
               snake_case					   = sagrams[i] + ''' ''' + sagrams[i + 1]
               sagrams.append(snake_case_							)
          if i < len(snake_case_							) - 2:
               snake_case					   = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
               sagrams.append(snake_case_							)
          if i < len(snake_case_							) - 3:
               snake_case					   = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
               sagrams.append(snake_case_							)

     for i in range(0    ,      len(snake_case_							) - 1							):
          if i < len(snake_case_							) - 1:
               snake_case					   = cagrams[i] + ''' ''' + cagrams[i + 1]
               cagrams.append(snake_case_							)
          if i < len(snake_case_							) - 2:
               snake_case					   = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
               cagrams.append(snake_case_							)
          if i < len(snake_case_							) - 3:
               snake_case					   = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
               cagrams.append(snake_case_							)

     (snake_case)					   = SARIngram(snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_							)
     (snake_case)					   = SARIngram(snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_							)
     (snake_case)					   = SARIngram(snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_							)
     (snake_case)					   = SARIngram(snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_							)
     snake_case					   = sum([keepascore, keepascore, keepascore, keepascore]							) / 4
     snake_case					   = sum([delascore, delascore, delascore, delascore]							) / 4
     snake_case					   = sum([addascore, addascore, addascore, addascore]							) / 4
     snake_case					   = (avgkeepscore + avgdelscore + avgaddscore) / 3
     return finalscore



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = True    ,      __lowercase = "13a"    ,      __lowercase = True							):
     # Normalization is requried for the ASSET dataset (one of the primary
     # datasets in sentence simplification) to allow using space
     # to split the sentence. Even though Wiki-Auto and TURK datasets,
     # do not require normalization, we do it for consistency.
     # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
     # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

     if lowercase:
          snake_case					   = sentence.lower()

     if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__							).major >= 2:
               snake_case					   = sacrebleu.metrics.bleu._get_tokenizer(snake_case_							)()(snake_case_							)
          else:
               snake_case					   = sacrebleu.TOKENIZERS[tokenizer]()(snake_case_							)
     elif tokenizer == "moses":
          snake_case					   = sacremoses.MosesTokenizer().tokenize(snake_case_    ,      return_str=snake_case_    ,      escape=snake_case_							)
     elif tokenizer == "penn":
          snake_case					   = sacremoses.MosesTokenizer().penn_tokenize(snake_case_    ,      return_str=snake_case_							)
     else:
          snake_case					   = sentence

     if not return_str:
          snake_case					   = normalized_sent.split()

     return normalized_sent







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,		_KWARGS_DESCRIPTION  )
class 				_a   (datasets.Metric  ):





     '''simple docstring'''



     def  snake_case_			(							self       )			->							List[Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
                  {
                      """sources""": datasets.Value("""string"""       ,id="""sequence"""       ),
                      """predictions""": datasets.Value("""string"""       ,id="""sequence"""       ),
                      """references""": datasets.Sequence(datasets.Value("""string"""       ,id="""sequence"""       )       ,id="""references"""       ),
                  }       )       ,codebase_urls=[
                  """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
                  """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
              ]       ,reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]       ,)







     def  snake_case_			(							self       ,__a       ,__a       ,__a       )			->							Optional[Any]:


          if not (len(__UpperCamelCase       ) == len(__UpperCamelCase       ) == len(__UpperCamelCase       )):
               raise ValueError("""Sources length must match predictions and references lengths."""       )
          snake_case					   = 0
          for src, pred, refs in zip(__UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ):
               sari_score += SARIsent(normalize(__UpperCamelCase       )       ,normalize(__UpperCamelCase       )       ,[normalize(__UpperCamelCase       ) for sent in refs]       )
          snake_case					   = sari_score / len(__UpperCamelCase       )
          return {"sari": 100 * sari_score}









'''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""")

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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=None							):
     if seed is not None:  # useful for obtaining consistent results
          np.random.seed(__lowercase							)
     snake_case :			Optional[int]					   = data.shape[0]  # number of data points

     # Pick K indices from range [0, N).
     snake_case :			Tuple					   = 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.
     snake_case :			List[Any]					   = data[rand_indices, :]

     return centroids



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     return pairwise_distances(__lowercase    ,      __lowercase    ,      metric="""euclidean"""							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     # Compute distances between each data point and the set of centroids:
     # Fill in the blank (RHS only)
     snake_case :			Union[str, Any]					   = centroid_pairwise_dist(__lowercase    ,      __lowercase							)

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

     return cluster_assignment



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

     return new_centroids



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

     return heterogeneity



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     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 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=500    ,      __lowercase=None    ,      __lowercase=False							):
     snake_case :			Tuple					   = initial_centroids[:]
     snake_case :			Dict					   = None

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

          # 1. Make cluster assignments using nearest centroids
          snake_case :			List[Any]					   = assign_clusters(__lowercase    ,      __lowercase							)

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

          snake_case :			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

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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=None							):
     # Fill missing values with given rules
     if fill_missing_report:
          snake_case :			List[str]					   = df.fillna(value=__lowercase							)
     snake_case :			List[Any]					   = 1
     snake_case :			Optional[int]					   = df.select_dtypes(np.number							).columns
     snake_case :			int					   = (
         df.groupby(["""Cluster"""]							)[  # construct report dataframe
             numeric_cols
         ]  # group by cluster number
         .agg(
             [
                 ("""sum""", np.sum),
                 ("""mean_with_zeros""", lambda __lowercase							: np.mean(np.nan_to_num(__lowercase							)							)),
                 ("""mean_without_zeros""", lambda __lowercase							: x.replace(0    ,      np.NaN							).mean()),
                 (
                     """mean_25-75""",
                     lambda __lowercase							: 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 __lowercase							: x.min()),
                 ("""5%""", lambda __lowercase							: x.quantile(0.05							)),
                 ("""25%""", lambda __lowercase							: x.quantile(0.25							)),
                 ("""50%""", lambda __lowercase							: x.quantile(0.50							)),
                 ("""75%""", lambda __lowercase							: x.quantile(0.75							)),
                 ("""95%""", lambda __lowercase							: x.quantile(0.95							)),
                 ("""max""", lambda __lowercase							: x.max()),
                 ("""count""", lambda __lowercase							: x.count()),
                 ("""stdev""", lambda __lowercase							: x.std()),
                 ("""mode""", lambda __lowercase							: x.mode()[0]),
                 ("""median""", lambda __lowercase							: x.median()),
                 ("""# > 0""", lambda __lowercase							: (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)
     snake_case :			List[str]					   = report[
         (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
     ].copy()  # avoid SettingWithCopyWarning
     snake_case :			Optional[Any]					   = (
         """ClusterSize"""  # rename created cluster df to match report column names
     )
     snake_case :			Optional[int]					   = """# of Customers"""
     snake_case :			List[str]					   = pd.DataFrame(
         clustersize.iloc[:, 2:].values
         / clustersize.iloc[:, 2:].values.sum()							)
     snake_case :			List[str]					   = """% of Customers"""  # rename created cluster df to match report column names
     snake_case :			List[Any]					   = """ClusterProportion"""
     snake_case :			Tuple					   = clusterproportion.columns.tolist()
     snake_case :			Optional[int]					   = cols[-2:] + cols[:-2]
     snake_case :			Tuple					   = clusterproportion[cols]  # rearrange columns to match report
     snake_case :			Tuple					   = report.columns
     snake_case :			List[str]					   = pd.DataFrame(
         abs(
             report[report["""Type"""] == """count"""].iloc[:, 2:].values
             - clustersize.iloc[:, 2:].values							)							)  # generating df with count of nan values
     snake_case :			Tuple					   = 0
     snake_case :			int					   = """# of nan"""
     snake_case :			int					   = report[
         report["""Type"""] == """count"""
     ].Features.tolist()  # filling values in order to match report
     snake_case :			Optional[Any]					   = a.columns.tolist()
     snake_case :			Dict					   = cols[-2:] + cols[:-2]
     snake_case :			Optional[Any]					   = a[cols]  # rearrange columns to match report
     snake_case :			Tuple					   = report.columns  # rename columns to match report
     snake_case :			Optional[Any]					   = report.drop(
         report[report.Type == """count"""].index							)  # drop count values except cluster size
     snake_case :			List[Any]					   = pd.concat(
         [report, a, clustersize, clusterproportion]    ,      axis=0							)  # concat report with clustert size and nan values
     snake_case :			Optional[int]					   = report["""Features"""].isin(__lowercase							)
     snake_case :			Any					   = report.columns.tolist()
     snake_case :			Union[str, Any]					   = cols[0:2] + cols[-1:] + cols[2:-1]
     snake_case :			List[Any]					   = report[cols]
     snake_case :			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,
     }
     snake_case :			List[Any]					   = (
         report.assign(
             Sortera=lambda __lowercase							: x.Type.map(__lowercase							)    ,      Sortera=lambda __lowercase							: list(reversed(range(len(__lowercase							)							)							)							)    ,      )
         .sort_values(["""Sorter1""", """Mark""", """Sorter2"""]    ,      ascending=__lowercase							)
         .drop(["""Sorter1""", """Sorter2"""]    ,      axis=1							)
     )
     snake_case :			str					   = """"""
     snake_case :			Dict					   = report.reset_index()
     snake_case :			Optional[int]					   = report.drop(columns=["""index"""]							)
     return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()



