

'''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 :      List[str]					 =     re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowerCamelCase :      Any					 =     1_0
lowerCamelCase :      Tuple					 =     2_5_6


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[MinHash]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			Set[str]:
     """simple docstring"""




     return {t for t in NON_ALPHA.split(_UpperCamelCase				) if len(t.strip()				) > 0}







class 							A__    :





  def __init__(		self      :	Dict							,					*,
      _a      :	float = 0.85							,					)	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =duplication_jaccard_threshold
       _SCREAMING_SNAKE_CASE							    =NUM_PERM
       _SCREAMING_SNAKE_CASE							    =MinHashLSH(threshold=self._duplication_jaccard_threshold							,					num_perm=self._num_perm )

       _SCREAMING_SNAKE_CASE							    =defaultdict(_a )





  def 						A  (		self      :	Tuple							,					_a      :	Tuple							,					_a      :	MinHash )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	int )	->    List[List[Dict]]:





       '''simple docstring'''






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





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Optional[Any] )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.get_duplicate_clusters()
       with open(_a							,					'w' ) as f:
            json.dump(_a							,					_a )


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =element
     _SCREAMING_SNAKE_CASE							    =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					(						_UpperCamelCase						:     Type[Dataset]				)      ->			List[Any]:
     """simple docstring"""




     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					(						_UpperCamelCase						:     Type[Dataset]							, _UpperCamelCase						:     float				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =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					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str				)      ->			float:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =get_tokens(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =get_tokens(_UpperCamelCase				)
     return len(tokensa & tokensa				) / len(tokensa | tokensa				)


lowerCamelCase :      Union[str, Any]					 =     None


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Tuple				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     for elementa in cluster:
          _SCREAMING_SNAKE_CASE							    =_shared_dataset[elementa['base_index']]['content']
          for elementa in extremes:
               _SCREAMING_SNAKE_CASE							    =_shared_dataset[elementa['base_index']]['content']
               if jaccard_similarity(_UpperCamelCase							, _UpperCamelCase				) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               _SCREAMING_SNAKE_CASE							    =1
               extremes.append(_UpperCamelCase				)
     return extremes


def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     str							, _UpperCamelCase						:     Any				)      ->			Optional[Any]:
     """simple docstring"""




     global _shared_dataset
     _SCREAMING_SNAKE_CASE							    =dataset
     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =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					(						_UpperCamelCase						:     Type[Dataset]							, _UpperCamelCase						:     float = 0.85				)      ->			Tuple[Type[Dataset], List[List[Dict]]]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =make_duplicate_clusters(_UpperCamelCase							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    ={x['base_index'] for cluster in duplicate_clusters for x in cluster}
     _SCREAMING_SNAKE_CASE							    ={}
     _SCREAMING_SNAKE_CASE							    =find_extremes(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)
     for extremes in extremes_clusters:
          for element in extremes:
               _SCREAMING_SNAKE_CASE							    =element
     _SCREAMING_SNAKE_CASE							    =duplicate_indices - set(extreme_dict.keys()				)
     _SCREAMING_SNAKE_CASE							    =dataset.filter(lambda _UpperCamelCase							, _UpperCamelCase				: idx not in remove_indices							, with_indices=_UpperCamelCase				)

     # update duplicate_clusters
     for cluster in duplicate_clusters:
          for element in cluster:
               _SCREAMING_SNAKE_CASE							    =element['base_index'] in extreme_dict
               if element["is_extreme"]:
                    _SCREAMING_SNAKE_CASE							    =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 datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case    						="""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

__snake_case    						="""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

__snake_case    						="""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""

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





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,      citation=_CITATION    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(
                {
                    'predictions': datasets.Sequence(datasets.Value('string'    ,      id='token'    )    ,      id='sequence'    ),
                    'references': datasets.Sequence(
                        datasets.Sequence(datasets.Value('string'    ,      id='token'    )    ,      id='sequence'    )    ,      id='references'    ),
                }    )    ,      codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py']    ,      reference_urls=[
                'https://en.wikipedia.org/wiki/BLEU',
                'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
            ]    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=4    ,      UpperCAmelCase__       :      Optional[int]=False    )				->  int:
        lowerCAmelCase     =			compute_bleu(
            reference_corpus=UpperCAmelCase__    ,      translation_corpus=UpperCAmelCase__    ,      max_order=UpperCAmelCase__    ,      smooth=UpperCAmelCase__    )
        ((lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase))     =			score
        return {
            "bleu": bleu,
            "precisions": precisions,
            "brevity_penalty": bp,
            "length_ratio": ratio,
            "translation_length": translation_length,
            "reference_length": reference_length,
        }





