






'''simple docstring'''







from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


__lowerCAmelCase									=   [
    'Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

__lowerCAmelCase									=   [
    'Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .'
    ' Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]





def 		_UpperCAmelCase (				):
				a_		:	List[Any]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		bootstrap_aggregation=lowerCAmelCase_    ,		rouge_keys=['''rouge2''', '''rougeL''']   )
				assert isinstance(lowerCAmelCase_    ,		lowerCAmelCase_   )
				a_		:	Optional[int]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		bootstrap_aggregation=lowerCAmelCase_    ,		rouge_keys=['''rouge2''']   )
				assert (
				    pd.DataFrame(no_aggregation['''rouge2''']   ).fmeasure.mean()
				    == pd.DataFrame(no_aggregation_just_ra['''rouge2''']   ).fmeasure.mean()
				)





def 		_UpperCAmelCase (				):
				a_		:	Optional[Any]									=   '''rougeLsum'''
				a_		:	Optional[Any]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		newline_sep=lowerCAmelCase_    ,		rouge_keys=[k]   )[k]
				a_		:	Optional[Any]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		newline_sep=lowerCAmelCase_    ,		rouge_keys=[k]   )[k]
				assert score > score_no_sep





def 		_UpperCAmelCase (				):
				a_		:	int									=   ['''rouge1''', '''rouge2''', '''rougeL''']
				a_		:	List[str]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		newline_sep=lowerCAmelCase_    ,		rouge_keys=lowerCAmelCase_   )
				a_		:	Union[str, Any]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		newline_sep=lowerCAmelCase_    ,		rouge_keys=lowerCAmelCase_   )
				assert score_sep == score_no_sep





def 		_UpperCAmelCase (				):
				a_		:	int									=   [
				    '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
				    '''Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .''',
				]
				a_		:	Dict									=   [
				    '''Margot Frank, died in 1945, a month earlier than previously thought.''',
				    '''Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of'''
				    ''' the final seconds on board Flight 9525.''',
				]
				assert calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		newline_sep=lowerCAmelCase_   ) == calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		newline_sep=lowerCAmelCase_   )





def 		_UpperCAmelCase (				):
				a_		:	Dict									=   [
				    '''\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" '''
				]
				a_		:	str									=   [
				    ''' Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
				]

				a_		:	List[Any]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		rouge_keys=['''rougeLsum''']    ,		newline_sep=lowerCAmelCase_   )['''rougeLsum''']
				a_		:	List[str]									=   calculate_rouge(lowerCAmelCase_    ,		lowerCAmelCase_    ,		rouge_keys=['''rougeLsum''']   )['''rougeLsum''']
				assert new_score > prev_score





def 		_UpperCAmelCase (				):
				a_		:	Any									=   Path('''examples/seq2seq/test_data/wmt_en_ro'''   )
				a_		:	List[Any]									=   calculate_rouge_path(data_dir.joinpath('''test.source'''   )    ,		data_dir.joinpath('''test.target'''   )   )
				assert isinstance(lowerCAmelCase_    ,		lowerCAmelCase_   )
				a_		:	str									=   calculate_rouge_path(
				    data_dir.joinpath('''test.source'''   )    ,		data_dir.joinpath('''test.target'''   )    ,		bootstrap_aggregation=lowerCAmelCase_   )
				assert isinstance(lowerCAmelCase_    ,		lowerCAmelCase_   )








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


__a	 =      re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__a	 =      10
__a	 =      256
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[MinHash]:
   if len(lowerCAmelCase_ ) < MIN_NUM_TOKENS:
      return None
   UpperCAmelCase      					=  MinHash(num_perm=lowerCAmelCase_ )
   for token in set(lowerCAmelCase_ ):
      min_hash.update(token.encode() )
   return min_hash
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Set[str]:
   return {t for t in NON_ALPHA.split(lowerCAmelCase_ ) if len(t.strip() ) > 0}






class 	__lowercase					:





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






         """simple docstring"""







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

         UpperCAmelCase      					=  defaultdict(__lowerCamelCase							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       MinHash							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  self._index.query(__lowerCamelCase							)
         if code_key in self._index.keys:
            print(F"""Duplicate key {code_key}"""							)
            return

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





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[List[Dict]]:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_duplicate_clusters()
         with open(__lowerCamelCase       ,							"""w"""							) as f:
            json.dump(__lowerCamelCase       ,							__lowerCamelCase							)

def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Tuple:
   UpperCAmelCase					,	UpperCAmelCase      					=  element
   UpperCAmelCase      					=  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 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   with mp.Pool() as pool:
      for data in pool.imap_unordered(
          _compute_min_hash	,    ThreadedIterator(lowerCAmelCase_	,    max_queue_size=1_0_0_0_0 )	,    chunksize=1_0_0	,    ):
         if data is not None:
            yield data
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Any:
   UpperCAmelCase      					=  DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_ )

   for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_ ) )	,    max_queue_size=1_0_0 ) ):
      di.add(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Returns a List[Cluster] where Cluster is List[str] with the filenames.
   return di.get_duplicate_clusters()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->float:
   UpperCAmelCase      					=  get_tokens(lowerCAmelCase_ )
   UpperCAmelCase      					=  get_tokens(lowerCAmelCase_ )
   return len(tokensa & tokensa ) / len(tokensa | tokensa )


__a	 =      None
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   UpperCAmelCase      					=  []
   for elementa in cluster:
      UpperCAmelCase      					=  _shared_dataset[elementa["""base_index"""]]["""content"""]
      for elementa in extremes:
         UpperCAmelCase      					=  _shared_dataset[elementa["""base_index"""]]["""content"""]
         if jaccard_similarity(lowerCAmelCase_	,    lowerCAmelCase_ ) >= jaccard_threshold:
            elementa["copies"] += 1
            break
      else:
         UpperCAmelCase      					=  1
         extremes.append(lowerCAmelCase_ )
   return extremes
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   global _shared_dataset
   UpperCAmelCase      					=  dataset
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  partial(_find_cluster_extremes_shared	,    jaccard_threshold=lowerCAmelCase_ )
   with mp.Pool() as pool:
      for extremes in tqdm(
          pool.imap_unordered(
              lowerCAmelCase_	,    lowerCAmelCase_	,    )	,    total=len(lowerCAmelCase_ )	,    ):
         extremes_list.append(lowerCAmelCase_ )
   return extremes_list







def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ = 0.85 )    ->Tuple[Type[Dataset], List[List[Dict]]]:
   UpperCAmelCase      					=  make_duplicate_clusters(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  find_extremes(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
   for extremes in extremes_clusters:
      for element in extremes:
         UpperCAmelCase      					=  element
   UpperCAmelCase      					=  duplicate_indices - set(extreme_dict.keys() )
   UpperCAmelCase      					=  dataset.filter(lambda lowerCAmelCase_	,    lowerCAmelCase_ : idx not in remove_indices	,    with_indices=lowerCAmelCase_ )

   # update duplicate_clusters
   for cluster in duplicate_clusters:
      for element in cluster:
         UpperCAmelCase      					=  element["""base_index"""] in extreme_dict
         if element["is_extreme"]:
            UpperCAmelCase      					=  extreme_dict[element["""base_index"""]]["""copies"""]

   print(F"""Original dataset size: {len(lowerCAmelCase_ )}""" )
   print(F"""Number of duplicate clusters: {len(lowerCAmelCase_ )}""" )
   print(F"""Files in duplicate cluster: {len(lowerCAmelCase_ )}""" )
   print(F"""Unique files in duplicate cluster: {len(lowerCAmelCase_ )}""" )
   print(F"""Filtered dataset size: {len(lowerCAmelCase_ )}""" )

   return ds_filter, duplicate_clusters



