





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_		(							_UpperCamelCase					)						->		Optional[MinHash]:






				"""simple docstring"""


				if len(_UpperCamelCase					) < MIN_NUM_TOKENS:
								return None
				snake_case_		:       Optional[int]           =    MinHash(num_perm=_UpperCamelCase					)
				for token in set(_UpperCamelCase					):
								min_hash.update(token.encode()					)
				return min_hash






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Set[str]:






				"""simple docstring"""


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



class    __lowerCAmelCase			:






			def __init__(self   ,				*,
			    __magic_name__ = 0.85   ,				)	->  int:


							'''simple docstring'''



							snake_case_		:       int           =    duplication_jaccard_threshold
							snake_case_		:       Tuple           =    NUM_PERM
							snake_case_		:       Dict           =    MinHashLSH(threshold=self._duplication_jaccard_threshold   ,				num_perm=self._num_perm       )

							snake_case_		:       Dict           =    defaultdict(__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       str           =    self._index.query(__magic_name__       )
							if code_key in self._index.keys:
											print(F'''Duplicate key {code_key}'''       )
											return

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






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


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.get_duplicate_clusters()
							with open(__magic_name__   ,				'''w'''       ) as f:
											json.dump(__magic_name__   ,				__magic_name__       )






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				snake_case_     ,				snake_case_		:       Any           =    element
				snake_case_		:       Tuple           =    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					)						->		Union[str, Any]:






				"""simple docstring"""


				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash					,							ThreadedIterator(_UpperCamelCase					,							max_queue_size=10_000					)					,							chunksize=100					,							):
												if data is not None:
																yield data






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Union[str, Any]:






				"""simple docstring"""


				snake_case_		:       Union[str, Any]           =    DuplicationIndex(duplication_jaccard_threshold=_UpperCamelCase					)

				for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_UpperCamelCase					)					)					,							max_queue_size=100					)					):
								di.add(_UpperCamelCase					,							_UpperCamelCase					)

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






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		float:






				"""simple docstring"""


				snake_case_		:       str           =    get_tokens(_UpperCamelCase					)
				snake_case_		:       Optional[int]           =    get_tokens(_UpperCamelCase					)
				return len(tokensa & tokensa					) / len(tokensa | tokensa					)


lowerCAmelCase_        =							None






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


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






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				global _shared_dataset
				snake_case_		:       Optional[Any]           =    dataset
				snake_case_		:       Union[str, Any]           =    []
				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_		(							_UpperCamelCase					,							_UpperCamelCase = 0.85					)						->		Tuple[Type[Dataset], List[List[Dict]]]:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    make_duplicate_clusters(_UpperCamelCase					,							_UpperCamelCase					)
				snake_case_		:       Union[str, Any]           =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
				snake_case_		:       Optional[Any]           =    {}
				snake_case_		:       str           =    find_extremes(_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)
				for extremes in extremes_clusters:
								for element in extremes:
												snake_case_		:       Dict           =    element
				snake_case_		:       List[Any]           =    duplicate_indices - set(extreme_dict.keys()					)
				snake_case_		:       str           =    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:
												snake_case_		:       str           =    element['''base_index'''] in extreme_dict
												if element["is_extreme"]:
																snake_case_		:       Union[str, Any]           =    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'''






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCAmelCase    :     Any         =       '''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

_UpperCAmelCase    :     str         =       '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

_UpperCAmelCase    :     Union[str, Any]         =       '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			MetricInfo:
        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'     ),
                }     ), )

   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			List[List[List[str]]], UpperCamelCase__      :			List[List[str]], UpperCamelCase__      :			int = 1, UpperCamelCase__      :			int = 4, )  ->			Dict[str, float]:
        return {
            "google_bleu": gleu_score.corpus_gleu(
                list_of_references=UpperCamelCase__, hypotheses=UpperCamelCase__, min_len=UpperCamelCase__, max_len=UpperCamelCase__     )
        }



