





'''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


__magic_name__   :						int    =			re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__magic_name__   :						Optional[int]    =			10
__magic_name__   :						Any    =			256




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   *,
         lowerCamelCase = 0.85						,   ):
       _snake_case     		=				duplication_jaccard_threshold
       _snake_case     		=				NUM_PERM
       _snake_case     		=				MinHashLSH(threshold=self._duplication_jaccard_threshold						,   num_perm=self._num_perm	)

       _snake_case     		=				defaultdict(lowerCamelCase	)

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






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.get_duplicate_clusters()
       with open(lowerCamelCase						,   "w"	) as f:
         json.dump(lowerCamelCase						,   lowerCamelCase	)







def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				element
  _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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				get_tokens(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				get_tokens(SCREAMING_SNAKE_CASE__       )
  return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


__magic_name__   :						Union[str, Any]    =			None




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  global _shared_dataset
  _snake_case     		=				dataset
  _snake_case     		=				[]
  _snake_case     		=				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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 0.85       ):







  '''simple docstring'''



  _snake_case     		=				make_duplicate_clusters(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				{x["base_index"] for cluster in duplicate_clusters for x in cluster}
  _snake_case     		=				{}
  _snake_case     		=				find_extremes(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  for extremes in extremes_clusters:
    for element in extremes:
      _snake_case     		=				element
  _snake_case     		=				duplicate_indices - set(extreme_dict.keys()       )
  _snake_case     		=				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:
      _snake_case     		=				element["base_index"] in extreme_dict
      if element["is_extreme"]:
        _snake_case     		=				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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						Union[str, Any]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


