


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				(			SCREAMING_SNAKE_CASE__			:					List[str]				):
     if len(SCREAMING_SNAKE_CASE__				) < MIN_NUM_TOKENS:
          return None
     __UpperCamelCase    							=MinHash(num_perm=SCREAMING_SNAKE_CASE__				)
     for token in set(SCREAMING_SNAKE_CASE__				):
          min_hash.update(token.encode()				)
     return min_hash

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str				):
     return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE__				) if len(t.strip()				) > 0}






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   *,
        A_ = 0.85					,   )  ->       str:
         __UpperCamelCase    							=duplication_jaccard_threshold
         __UpperCamelCase    							=NUM_PERM
         __UpperCamelCase    							=MinHashLSH(threshold=self._duplication_jaccard_threshold					,   num_perm=self._num_perm    )

         __UpperCamelCase    							=defaultdict(A_    )






    def _a					(			self					,   A_					,   A_    )  ->       None:
         __UpperCamelCase    							=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    )  ->       List[List[Dict]]:
         __UpperCamelCase    							=[]
         for base, duplicates in self._duplicate_clusters.items():
              __UpperCamelCase    							=[base] + list(A_    )
              # 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(A_    )
         return duplicate_clusters






    def _a					(			self					,   A_    )  ->       None:
         __UpperCamelCase    							=self.get_duplicate_clusters()
         with open(A_					,   'w'    ) as f:
              json.dump(A_					,   A_    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     __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				(			SCREAMING_SNAKE_CASE__			:					Type[Dataset]				):
     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash			,     ThreadedIterator(SCREAMING_SNAKE_CASE__			,     max_queue_size=1_00_00				)			,     chunksize=1_00			,     ):
               if data is not None:
                    yield data

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Type[Dataset]			,     SCREAMING_SNAKE_CASE__			:					float				):
     __UpperCamelCase    							=DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__				)

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

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					str				):
     __UpperCamelCase    							=get_tokens(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=get_tokens(SCREAMING_SNAKE_CASE__				)
     return len(tokensa & tokensa				) / len(tokensa | tokensa				)


_A		       =						None

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					int				):
     __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(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               __UpperCamelCase    							=1
               extremes.append(SCREAMING_SNAKE_CASE__				)
     return extremes

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					Any				):
     global _shared_dataset
     __UpperCamelCase    							=dataset
     __UpperCamelCase    							=[]
     __UpperCamelCase    							=partial(_find_cluster_extremes_shared			,     jaccard_threshold=SCREAMING_SNAKE_CASE__				)
     with mp.Pool() as pool:
          for extremes in tqdm(
              pool.imap_unordered(
                  SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     )			,     total=len(SCREAMING_SNAKE_CASE__				)			,     ):
               extremes_list.append(SCREAMING_SNAKE_CASE__				)
     return extremes_list





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Type[Dataset]			,     SCREAMING_SNAKE_CASE__			:					float = 0.85				):
     __UpperCamelCase    							=make_duplicate_clusters(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							={x['base_index'] for cluster in duplicate_clusters for x in cluster}
     __UpperCamelCase    							={}
     __UpperCamelCase    							=find_extremes(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     for extremes in extremes_clusters:
          for element in extremes:
               __UpperCamelCase    							=element
     __UpperCamelCase    							=duplicate_indices - set(extreme_dict.keys()				)
     __UpperCamelCase    							=dataset.filter(lambda SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				: idx not in remove_indices			,     with_indices=SCREAMING_SNAKE_CASE__				)

     # 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(SCREAMING_SNAKE_CASE__				)}'				)
     print(F'Number of duplicate clusters: {len(SCREAMING_SNAKE_CASE__				)}'				)
     print(F'Files in duplicate cluster: {len(SCREAMING_SNAKE_CASE__				)}'				)
     print(F'Unique files in duplicate cluster: {len(SCREAMING_SNAKE_CASE__				)}'				)
     print(F'Filtered dataset size: {len(SCREAMING_SNAKE_CASE__				)}'				)

     return ds_filter, duplicate_clusters









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  TransfoXLTokenizer
    UpperCAmelCase__						:		str      =  False
    UpperCAmelCase__						:		Tuple      =  False






    def _a					(			self    )  ->       Union[str, Any]:
         super().setUp()

         __UpperCamelCase    							=[
             '<unk>',
             '[CLS]',
             '[SEP]',
             'want',
             'unwanted',
             'wa',
             'un',
             'running',
             ',',
             'low',
             'l',
         ]
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file					,   'w'					,   encoding='utf-8'    ) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )






    def _a					(			self					,   **A_    )  ->       Optional[int]:
         __UpperCamelCase    							=True
         return TransfoXLTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='<unk> UNwanted , running'
         __UpperCamelCase    							='<unk> unwanted, running'
         return input_text, output_text






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=TransfoXLTokenizer(vocab_file=self.vocab_file					,   lower_case=A_    )

         __UpperCamelCase    							=tokenizer.tokenize('<unk> UNwanted , running'    )
         self.assertListEqual(A_					,   ['<unk>', 'unwanted', ',', 'running']    )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   [0, 4, 8, 7]    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['hello', '!', 'how', 'are', 'you', '?']    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )
         __UpperCamelCase    							='Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
         __UpperCamelCase    							=[
             'Hello',
             '(',
             'bracket',
             ')',
             'and',
             'side',
             '@-@',
             'scrolled',
             '[',
             'and',
             ']',
             'Henry',
             '\'s',
             '$',
             '5',
             '@,@',
             '000',
             'with',
             '3',
             '@.@',
             '34',
             'm',
             '.',
             'What',
             '\'s',
             'up',
             '!',
             '?',
         ]

         self.assertListEqual(tokenizer.tokenize(A_    )					,   A_    )

         self.assertEqual(tokenizer.convert_tokens_to_string(A_    )					,   A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=len(A_    )

         tokenizer.add_tokens(['new1', 'new2']    )
         tokenizer.move_added_token('new1'					,   1    )

         # Check that moved token is not copied (duplicate)
         self.assertEqual(len(A_    )					,   original_len + 2    )
         # Check that token is moved to specified id
         self.assertEqual(tokenizer.encode('new1'    )					,   [1]    )
         self.assertEqual(tokenizer.decode([1]    )					,   'new1'    )





