





"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


lowerCamelCase_								=		re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowerCamelCase_								=		1_0
lowerCamelCase_								=		2_5_6



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Optional[MinHash]:




      if len(_UpperCamelCase							) < MIN_NUM_TOKENS:
            return None
      __SCREAMING_SNAKE_CASE   :Tuple						=  MinHash(num_perm=_UpperCamelCase							)
      for token in set(_UpperCamelCase							):
            min_hash.update(token.encode()							)
      return min_hash



def      __lowerCamelCase   (							a_     :		str							)			->      Set[str]:
      return {t for t in NON_ALPHA.split(_UpperCamelCase							) if len(t.strip()							) > 0}






class 					_SCREAMING_SNAKE_CASE:




       def __init__(						self		,*,
           SCREAMING_SNAKE_CASE__ = 0.8_5		,)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  duplication_jaccard_threshold
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  NUM_PERM
             __SCREAMING_SNAKE_CASE   :List[Any]						=  MinHashLSH(threshold=self._duplication_jaccard_threshold		,num_perm=self._num_perm							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  defaultdict(_a							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  self._index.query(_a							)
             if code_key in self._index.keys:
                   print(f'''Duplicate key {code_key}'''							)
                   return

             self._index.insert(_a		,_a							)
             if len(_a							) > 0:
                   for base_duplicate in close_duplicates:
                         if base_duplicate in self._duplicate_clusters:
                               self._duplicate_clusters[base_duplicate].add(_a							)
                               break
                   else:
                         self._duplicate_clusters[close_duplicates[0]].add(_a							)




       def     _UpperCamelCase     (						self							)			->      List[List[Dict]]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  []
             for base, duplicates in self._duplicate_clusters.items():
                   __SCREAMING_SNAKE_CASE   :Tuple						=  [base] + list(_a							)
                   # reformat the cluster to be a list of dict
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
                   duplicate_clusters.append(_a							)
             return duplicate_clusters




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  self.get_duplicate_clusters()
             with open(_a		,'''w'''							) as f:
                   json.dump(_a		,_a							)




def      __lowerCamelCase   (							a_     :		Tuple							)			->      Tuple:
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :str						=  element
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  get_min_hash([t for t in NON_ALPHA.split(data['''content''']							) if len(t.strip()							) > 0]							)
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash



def      __lowerCamelCase   (							a_     :		Type[Dataset]							)			->      List[Any]:




      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash ,   ThreadedIterator(_UpperCamelCase ,   max_queue_size=1_00_00							) ,   chunksize=1_00 ,   ):
                  if data is not None:
                        yield data



def      __lowerCamelCase   (							a_     :		Type[Dataset] ,   a_     :		float							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  DuplicationIndex(duplication_jaccard_threshold=_UpperCamelCase							)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_UpperCamelCase							)							) ,   max_queue_size=1_00							)							):
            di.add(_UpperCamelCase ,   _UpperCamelCase							)

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()



def      __lowerCamelCase   (							a_     :		str ,   a_     :		str							)			->      float:
      __SCREAMING_SNAKE_CASE   :Dict						=  get_tokens(_UpperCamelCase							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  get_tokens(_UpperCamelCase							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


lowerCamelCase_								=		None



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Tuple							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :Any						=  []
      for elementa in cluster:
            __SCREAMING_SNAKE_CASE   :Dict						=  _shared_dataset[elementa['''base_index''']]['''content''']
            for elementa in extremes:
                  __SCREAMING_SNAKE_CASE   :Tuple						=  _shared_dataset[elementa['''base_index''']]['''content''']
                  if jaccard_similarity(_UpperCamelCase ,   _UpperCamelCase							) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  __SCREAMING_SNAKE_CASE   :str						=  1
                  extremes.append(_UpperCamelCase							)
      return extremes



def      __lowerCamelCase   (							a_     :		int ,   a_     :		str ,   a_     :		Any							)			->      Optional[Any]:
      global _shared_dataset
      __SCREAMING_SNAKE_CASE   :int						=  dataset
      __SCREAMING_SNAKE_CASE   :List[Any]						=  []
      __SCREAMING_SNAKE_CASE   :Any						=  partial(_find_cluster_extremes_shared ,   jaccard_threshold=_UpperCamelCase							)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _UpperCamelCase ,   _UpperCamelCase ,   ) ,   total=len(_UpperCamelCase							) ,   ):
                  extremes_list.append(_UpperCamelCase							)
      return extremes_list







def      __lowerCamelCase   (							a_     :		Type[Dataset] ,   a_     :		float = 0.85							)			->      Tuple[Type[Dataset], List[List[Dict]]]:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  make_duplicate_clusters(_UpperCamelCase ,   _UpperCamelCase							)
      __SCREAMING_SNAKE_CASE   :List[str]						=  {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  {}
      __SCREAMING_SNAKE_CASE   :Dict						=  find_extremes(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase							)
      for extremes in extremes_clusters:
            for element in extremes:
                  __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  element
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  duplicate_indices - set(extreme_dict.keys()							)
      __SCREAMING_SNAKE_CASE   :Any						=  dataset.filter(lambda a_ ,   a_							: idx not in remove_indices ,   with_indices=_UpperCamelCase							)

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  element['''base_index'''] in extreme_dict
                  if element["is_extreme"]:
                        __SCREAMING_SNAKE_CASE   :Dict						=  extreme_dict[element['''base_index''']]['''copies''']

      print(f'''Original dataset size: {len(_UpperCamelCase							)}'''							)
      print(f'''Number of duplicate clusters: {len(_UpperCamelCase							)}'''							)
      print(f'''Files in duplicate cluster: {len(_UpperCamelCase							)}'''							)
      print(f'''Unique files in duplicate cluster: {len(_UpperCamelCase							)}'''							)
      print(f'''Filtered dataset size: {len(_UpperCamelCase							)}'''							)

      return ds_filter, duplicate_clusters


'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase :      List[Any]					 =     version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
     from nltk import word_tokenize


lowerCamelCase :      Any					 =     "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCamelCase :      Optional[Any]					 =     "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCamelCase :      Optional[Any]					 =     "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"







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





  def 						A  (		self      :	Tuple )	->    str:





       '''simple docstring'''






       return datasets.MetricInfo(
           description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
               {
                   'predictions': datasets.Value('string'							,					id='sequence' ),
                   'references': datasets.Value('string'							,					id='sequence' ),
               } )							,					codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,					reference_urls=[
               'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
               'https://en.wikipedia.org/wiki/METEOR',
           ]							,					)





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Union[str, Any] )	->    Optional[int]:





       '''simple docstring'''






       import nltk

       nltk.download('wordnet' )
       if NLTK_VERSION >= version.Version('3.6.5' ):
            nltk.download('punkt' )
       if NLTK_VERSION >= version.Version('3.6.6' ):
            nltk.download('omw-1.4' )





  def 						A  (		self      :	int							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	List[str]=0.9							,					_a      :	Dict=3							,					_a      :	Optional[int]=0.5 )	->    Optional[int]:





       '''simple docstring'''






       if NLTK_VERSION >= version.Version('3.6.5' ):
            _SCREAMING_SNAKE_CASE							    =[
                meteor_score.single_meteor_score(
                    word_tokenize(_a )							,					word_tokenize(_a )							,					alpha=_a							,					beta=_a							,					gamma=_a )
                for ref, pred in zip(_a							,					_a )
            ]
       else:
            _SCREAMING_SNAKE_CASE							    =[
                meteor_score.single_meteor_score(_a							,					_a							,					alpha=_a							,					beta=_a							,					gamma=_a )
                for ref, pred in zip(_a							,					_a )
            ]

       return {"meteor": np.mean(_a )}
