






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:     Optional[int]   					=					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
__a:     List[str]   					=					10
__a:     List[str]   					=					256




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[MinHash]:
       if len(__snake_case ) < MIN_NUM_TOKENS:
              return None
       _UpperCAmelCase							=    MinHash(num_perm=__snake_case )
       for token in set(__snake_case ):
              min_hash.update(token.encode() )
       return min_hash




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Set[str]:
       return {t for t in NON_ALPHA.split(__snake_case ) if len(t.strip() ) > 0}
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Any       ,				*,
      lowerCamelCase		:			float = 0.85       ,				)						->	Optional[int]:


         """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  lowerCamelCase       (      self		:			List[Any]       ,				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  lowerCamelCase       (      self		:			List[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  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			int							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_duplicate_clusters()
         with open(lowerCamelCase       ,				"""w"""							) as f:
                json.dump(lowerCamelCase       ,				lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       _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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash   ,      ThreadedIterator(__snake_case   ,      max_queue_size=1_0_0_0_0 )   ,      chunksize=1_0_0   ,      ):
                     if data is not None:
                            yield data




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    DuplicationIndex(duplication_jaccard_threshold=__snake_case )

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

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




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						float:
       _UpperCAmelCase							=    get_tokens(__snake_case )
       _UpperCAmelCase							=    get_tokens(__snake_case )
       return len(tokensa & tokensa ) / len(tokensa | tokensa )


__a:     List[str]   					=					None




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Union[str, Any]:
       _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(__snake_case   ,      __snake_case ) >= jaccard_threshold:
                            elementa["copies"] += 1
                            break
              else:
                     _UpperCAmelCase							=    1
                     extremes.append(__snake_case )
       return extremes




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       global _shared_dataset
       _UpperCAmelCase							=    dataset
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    partial(_find_cluster_extremes_shared   ,      jaccard_threshold=__snake_case )
       with mp.Pool() as pool:
              for extremes in tqdm(
                  pool.imap_unordered(
                      __snake_case   ,      __snake_case   ,      )   ,      total=len(__snake_case )   ,      ):
                     extremes_list.append(__snake_case )
       return extremes_list





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = 0.85 )				->						Tuple[Type[Dataset], List[List[Dict]]]:
       _UpperCAmelCase							=    make_duplicate_clusters(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    find_extremes(__snake_case   ,      __snake_case   ,      __snake_case )
       for extremes in extremes_clusters:
              for element in extremes:
                     _UpperCAmelCase							=    element
       _UpperCAmelCase							=    duplicate_indices - set(extreme_dict.keys() )
       _UpperCAmelCase							=    dataset.filter(lambda __snake_case   ,      __snake_case : idx not in remove_indices   ,      with_indices=__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(__snake_case )}""" )
       print(f"""Number of duplicate clusters: {len(__snake_case )}""" )
       print(f"""Files in duplicate cluster: {len(__snake_case )}""" )
       print(f"""Unique files in duplicate cluster: {len(__snake_case )}""" )
       print(f"""Filtered dataset size: {len(__snake_case )}""" )

       return ds_filter, duplicate_clusters







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			str							)						->	Optional[int]:


         """simple docstring"""
         raise NotImplementedError()





  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         raise NotImplementedError()



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			str       ,				lowerCamelCase		:			"AutoTokenizer"       ,				lowerCamelCase		:			bool = False       ,				**lowerCamelCase		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    tokenizer
         _UpperCAmelCase							=    skip_prompt
         _UpperCAmelCase							=    decode_kwargs

         # variables used in the streaming process
         _UpperCAmelCase							=    []
         _UpperCAmelCase							=    0
         _UpperCAmelCase							=    True


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         if len(value.shape							) > 1 and value.shape[0] > 1:
                raise ValueError("""TextStreamer only supports batch size 1"""							)
         elif len(value.shape							) > 1:
                _UpperCAmelCase							=    value[0]

         if self.skip_prompt and self.next_tokens_are_prompt:
                _UpperCAmelCase							=    False
                return

         # Add the new token to the cache and decodes the entire thing.
         self.token_cache.extend(value.tolist()							)
         _UpperCAmelCase							=    self.tokenizer.decode(self.token_cache       ,				**self.decode_kwargs							)

         # After the symbol for a new line, we flush the cache.
         if text.endswith("""\n"""							):
                _UpperCAmelCase							=    text[self.print_len :]
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    0
         # If the last token is a CJK character, we print the characters.
         elif len(lowerCamelCase							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
                _UpperCAmelCase							=    text[self.print_len :]
                self.print_len += len(lowerCamelCase							)
         # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
         # which may change with the subsequent token -- there are probably smarter ways to do this!)
         else:
                _UpperCAmelCase							=    text[self.print_len : text.rfind(""" """							) + 1]
                self.print_len += len(lowerCamelCase							)

         self.on_finalized_text(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         # Flush the cache, if it exists
         if len(self.token_cache							) > 0:
                _UpperCAmelCase							=    self.tokenizer.decode(self.token_cache       ,				**self.decode_kwargs							)
                _UpperCAmelCase							=    text[self.print_len :]
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    0
         else:
                _UpperCAmelCase							=    """"""

         _UpperCAmelCase							=    True
         self.on_finalized_text(lowerCamelCase       ,				stream_end=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool = False							)						->	Any:


         """simple docstring"""
         print(lowerCamelCase       ,				flush=lowerCamelCase       ,				end="""""" if not stream_end else None							)





  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Any							)						->	int:


         """simple docstring"""
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0X4E00 and cp <= 0X9FFF)
             or (cp >= 0X3400 and cp <= 0X4DBF)  #
             or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
             or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
             or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
             or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
             or (cp >= 0XF900 and cp <= 0XFAFF)
             or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
         ):  #
                return True

         return False



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			"AutoTokenizer"       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			Optional[float] = None       ,				**lowerCamelCase		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         super().__init__(lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							)
         _UpperCAmelCase							=    Queue()
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    timeout


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool = False							)						->	Optional[int]:


         """simple docstring"""
         self.text_queue.put(lowerCamelCase       ,				timeout=self.timeout							)
         if stream_end:
                self.text_queue.put(self.stop_signal       ,				timeout=self.timeout							)


  def __iter__(      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         return self





  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.text_queue.get(timeout=self.timeout							)
         if value == self.stop_signal:
                raise StopIteration()
         else:
                return value