


"""simple docstring"""


from typing import TYPE_CHECKING

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


_snake_case          =							{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							["BloomTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "BloomForCausalLM",
       "BloomModel",
       "BloomPreTrainedModel",
       "BloomForSequenceClassification",
       "BloomForTokenClassification",
       "BloomForQuestionAnswering",
   ]

if TYPE_CHECKING:
   from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bloom_fast import BloomTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bloom import (
          BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
          BloomForCausalLM,
          BloomForQuestionAnswering,
          BloomForSequenceClassification,
          BloomForTokenClassification,
          BloomModel,
          BloomPreTrainedModel,
      )

else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""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


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



def   snake_case						(				_a:     List[str]							)->		Optional[MinHash]:
      '''simple docstring'''


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



def   snake_case						(				_a:     str							)->		Set[str]:
      '''simple docstring'''


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



class   _a      :
    def __init__( self							:					List[Any]							,					*,
        SCREAMING_SNAKE_CASE__							:					float = 0.85							,					):
          lowerCamelCase__           =		duplication_jaccard_threshold
          lowerCamelCase__           =		NUM_PERM
          lowerCamelCase__           =		MinHashLSH(threshold=self._duplication_jaccard_threshold							,					num_perm=self._num_perm				)

          lowerCamelCase__           =		defaultdict(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					MinHash				):
          lowerCamelCase__           =		self._index.query(SCREAMING_SNAKE_CASE__				)
          if code_key in self._index.keys:
                print(F'Duplicate key {code_key}'				)
                return

          self._index.insert(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          if len(SCREAMING_SNAKE_CASE__				) > 0:
                for base_duplicate in close_duplicates:
                      if base_duplicate in self._duplicate_clusters:
                            self._duplicate_clusters[base_duplicate].add(SCREAMING_SNAKE_CASE__				)
                            break
                else:
                      self._duplicate_clusters[close_duplicates[0]].add(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[]
          for base, duplicates in self._duplicate_clusters.items():
                lowerCamelCase__           =		[base] + list(SCREAMING_SNAKE_CASE__				)
                # reformat the cluster to be a list of dict
                lowerCamelCase__           =		[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
                duplicate_clusters.append(SCREAMING_SNAKE_CASE__				)
          return duplicate_clusters


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.get_duplicate_clusters()
          with open(SCREAMING_SNAKE_CASE__							,					'w'				) as f:
                json.dump(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)





def   snake_case						(				_a:     Union[str, Any]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__    ,  lowerCamelCase__           =		element
      lowerCamelCase__           =		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						(				_a:     Type[Dataset]							)->		Tuple:
      '''simple docstring'''


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



def   snake_case						(				_a:     Type[Dataset]		,   _a:     float							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		DuplicationIndex(duplication_jaccard_threshold=_a							)

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

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



def   snake_case						(				_a:     str		,   _a:     str							)->		float:
      '''simple docstring'''


      lowerCamelCase__           =		get_tokens(_a							)
      lowerCamelCase__           =		get_tokens(_a							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_snake_case          =							None



def   snake_case						(				_a:     Dict		,   _a:     Union[str, Any]							)->		List[str]:
      '''simple docstring'''


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



def   snake_case						(				_a:     Any		,   _a:     Tuple		,   _a:     Dict							)->		Union[str, Any]:
      '''simple docstring'''


      global _shared_dataset
      lowerCamelCase__           =		dataset
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		partial(_find_cluster_extremes_shared		,   jaccard_threshold=_a							)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _a		,   _a		,   )		,   total=len(_a							)		,   ):
                  extremes_list.append(_a							)
      return extremes_list






def   snake_case						(				_a:     Type[Dataset]		,   _a:     float = 0.85							)->		Tuple[Type[Dataset], List[List[Dict]]]:
      '''simple docstring'''


      lowerCamelCase__           =		make_duplicate_clusters(_a		,   _a							)
      lowerCamelCase__           =		{x['base_index'] for cluster in duplicate_clusters for x in cluster}
      lowerCamelCase__           =		{}
      lowerCamelCase__           =		find_extremes(_a		,   _a		,   _a							)
      for extremes in extremes_clusters:
            for element in extremes:
                  lowerCamelCase__           =		element
      lowerCamelCase__           =		duplicate_indices - set(extreme_dict.keys()							)
      lowerCamelCase__           =		dataset.filter(lambda _a		,   _a							: idx not in remove_indices		,   with_indices=_a							)

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

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

      return ds_filter, duplicate_clusters



