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    __lowercase     (						lowerCamelCase					:     List[str]	):





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





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





class 		_lowercase						:

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

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					duplication_jaccard_threshold
  UpperCamelCase_					:						str =					NUM_PERM
  UpperCamelCase_					:						Any =					MinHashLSH(threshold=self._duplication_jaccard_threshold   ,      num_perm=self._num_perm			)

  UpperCamelCase_					:						Optional[int] =					defaultdict(lowerCamelCase_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							Any			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					[]
  for base, duplicates in self._duplicate_clusters.items():
   UpperCamelCase_					:						List[str] =					[base] + list(lowerCamelCase_			)
   # reformat the cluster to be a list of dict
   UpperCamelCase_					:						Optional[int] =					[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
   duplicate_clusters.append(lowerCamelCase_			)
  return duplicate_clusters


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Optional[int]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.get_duplicate_clusters()
  with open(lowerCamelCase_   ,      'w'			) as f:
   json.dump(lowerCamelCase_   ,      lowerCamelCase_			)





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_, UpperCamelCase_					:						Tuple =					element
 UpperCamelCase_					:						List[Any] =					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    __lowercase     (						lowerCamelCase					:     Type[Dataset]	):





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





def    __lowercase     (						lowerCamelCase					:     Type[Dataset]  ,  lowerCamelCase					:     float	):
 UpperCamelCase_					:						Union[str, Any] =					DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase	)

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

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





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					get_tokens(lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					get_tokens(lowerCamelCase	)
 return len(tokensa & tokensa	) / len(tokensa | tokensa	)


a_    					=       None





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						Union[str, Any] =					[]
 for elementa in cluster:
  UpperCamelCase_					:						Any =					_shared_dataset[elementa['base_index']]['content']
  for elementa in extremes:
   UpperCamelCase_					:						int =					_shared_dataset[elementa['base_index']]['content']
   if jaccard_similarity(lowerCamelCase  ,  lowerCamelCase	) >= jaccard_threshold:
    elementa["copies"] += 1
    break
  else:
   UpperCamelCase_					:						List[Any] =					1
   extremes.append(lowerCamelCase	)
 return extremes





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     int	):
 global _shared_dataset
 UpperCamelCase_					:						Tuple =					dataset
 UpperCamelCase_					:						str =					[]
 UpperCamelCase_					:						Dict =					partial(_find_cluster_extremes_shared  ,  jaccard_threshold=lowerCamelCase	)
 with mp.Pool() as pool:
  for extremes in tqdm(
      pool.imap_unordered(
          lowerCamelCase  ,  lowerCamelCase  ,  )  ,  total=len(lowerCamelCase	)  ,  ):
   extremes_list.append(lowerCamelCase	)
 return extremes_list






def    __lowercase     (						lowerCamelCase					:     Type[Dataset]  ,  lowerCamelCase					:     float = 0.8_5	):
 UpperCamelCase_					:						Tuple =					make_duplicate_clusters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					{x['base_index'] for cluster in duplicate_clusters for x in cluster}
 UpperCamelCase_					:						str =					{}
 UpperCamelCase_					:						Any =					find_extremes(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 for extremes in extremes_clusters:
  for element in extremes:
   UpperCamelCase_					:						Union[str, Any] =					element
 UpperCamelCase_					:						Union[str, Any] =					duplicate_indices - set(extreme_dict.keys()	)
 UpperCamelCase_					:						List[str] =					dataset.filter(lambda lowerCamelCase  ,  lowerCamelCase	: idx not in remove_indices  ,  with_indices=lowerCamelCase	)

 # update duplicate_clusters
 for cluster in duplicate_clusters:
  for element in cluster:
   UpperCamelCase_					:						Optional[Any] =					element['base_index'] in extreme_dict
   if element["is_extreme"]:
    UpperCamelCase_					:						int =					extreme_dict[element['base_index']]['copies']

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

 return ds_filter, duplicate_clusters








import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





					return (data["data"], data["target"])






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   xgboost(A ,     A       )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    A ,     A ,     A ,     display_labels=A ,     cmap="Blues" ,     normalize="true" ,     )
					plt.title("Normalized Confusion Matrix - IRIS Dataset"       )
					plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()
