






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


snake_case     					=		re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
snake_case     					=		10
snake_case     					=		256


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






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


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Optional[Any]       ,			*,
         UpperCAmelCase_							:						float = 0.85       ,			):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     duplication_jaccard_threshold
       SCREAMING_SNAKE_CASE							:   Dict						    =     NUM_PERM
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     MinHashLSH(threshold=self._duplication_jaccard_threshold       ,			num_perm=self._num_perm   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     defaultdict(UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						MinHash   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self._index.query(UpperCAmelCase_   )
       if code_key in self._index.keys:
         print(f'''Duplicate key {code_key}'''   )
         return

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


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






     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_duplicate_clusters()
       with open(UpperCAmelCase_       ,			"w"   ) as f:
         json.dump(UpperCAmelCase_       ,			UpperCAmelCase_   )





def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     element
  SCREAMING_SNAKE_CASE							:   str						    =     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__      (  lowercase					):

  """simple docstring"""






  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash    ,  ThreadedIterator(lowercase    ,  max_queue_size=10000					)    ,  chunksize=100    ,  ):
      if data is not None:
        yield data


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[Any]						    =     DuplicationIndex(duplication_jaccard_threshold=lowercase					)

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

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


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     get_tokens(lowercase					)
  SCREAMING_SNAKE_CASE							:   int						    =     get_tokens(lowercase					)
  return len(tokensa & tokensa					) / len(tokensa | tokensa					)


snake_case     					=		None


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase    ,  lowercase = 0.85					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     make_duplicate_clusters(lowercase    ,  lowercase					)
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     {x["base_index"] for cluster in duplicate_clusters for x in cluster}
  SCREAMING_SNAKE_CASE							:   Dict						    =     {}
  SCREAMING_SNAKE_CASE							:   int						    =     find_extremes(lowercase    ,  lowercase    ,  lowercase					)
  for extremes in extremes_clusters:
    for element in extremes:
      SCREAMING_SNAKE_CASE							:   Any						    =     element
  SCREAMING_SNAKE_CASE							:   str						    =     duplicate_indices - set(extreme_dict.keys()					)
  SCREAMING_SNAKE_CASE							:   Tuple						    =     dataset.filter(lambda lowercase    ,  lowercase					: idx not in remove_indices    ,  with_indices=lowercase					)

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      SCREAMING_SNAKE_CASE							:   List[str]						    =     element["base_index"] in extreme_dict
      if element["is_extreme"]:
        SCREAMING_SNAKE_CASE							:   Tuple						    =     extreme_dict[element["base_index"]]["copies"]

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

  return ds_filter, duplicate_clusters











import logging
import os

from .state import PartialState
class    __UpperCamelCase			(       logging.LoggerAdapter				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= PartialState()
  return not main_process_only or (main_process_only and state.is_main_process)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if PartialState._shared_state == {}:
   raise RuntimeError(
       '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
  a__												= kwargs.pop('''main_process_only'''		,				SCREAMING_SNAKE_CASE      )
  a__												= kwargs.pop('''in_order'''		,				SCREAMING_SNAKE_CASE      )

  if self.isEnabledFor(SCREAMING_SNAKE_CASE      ):
   if self._should_log(SCREAMING_SNAKE_CASE      ):
    a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

   elif in_order:
    a__												= PartialState()
    for i in range(state.num_processes      ):
     if i == state.process_index:
      a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
     state.wait_for_everyone()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None       ):
 if log_level is None:
  a__												= os.environ.get('''ACCELERATE_LOG_LEVEL'''							,  __UpperCAmelCase       )
 a__												= logging.getLogger(__UpperCAmelCase       )
 if log_level is not None:
  logger.setLevel(log_level.upper()       )
  logger.root.setLevel(log_level.upper()       )
 return MultiProcessAdapter(__UpperCAmelCase							,  {}       )





