

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


lowerCAmelCase  : Optional[Any]      =	re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
lowerCAmelCase  : Optional[int]      =	10
lowerCAmelCase  : List[Any]      =	2_56

def  A_	(				a	):

      """simple docstring"""



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

def  A_	(				a	):

      """simple docstring"""



      return {t for t in NON_ALPHA.split(a	) if len(t.strip()	) > 0}
class 			_A		:

  def __init__(					self			,       *,
      _SCREAMING_SNAKE_CASE = 0.85			,       ):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_					:	str     							=  defaultdict(lowercase__       )

  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  self._index.query(lowercase__       )
        if code_key in self._index.keys:
              print(f"Duplicate key {code_key}"       )
              return

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

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







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



  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  self.get_duplicate_clusters()
        with open(lowercase__			,       'w'       ) as f:
              json.dump(lowercase__			,       lowercase__       )

def  A_	(				a	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	str     							=  element
      SCREAMING_SNAKE_CASE_					:	int     							=  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  A_	(				a	):

      """simple docstring"""



      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash       , ThreadedIterator(a       , max_queue_size=1_0_0_0_0	)       , chunksize=1_0_0       , ):
                  if data is not None:
                        yield data

def  A_	(				a       , a	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  DuplicationIndex(duplication_jaccard_threshold=a	)

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

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

def  A_	(				a       , a	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	int     							=  get_tokens(a	)
      SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  get_tokens(a	)
      return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowerCAmelCase  : Optional[int]      =	None

def  A_	(				a       , a	):

      """simple docstring"""



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

def  A_	(				a       , a       , a	):

      """simple docstring"""



      global _shared_dataset
      SCREAMING_SNAKE_CASE_					:	int     							=  dataset
      SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  []
      SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  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  A_	(				a       , a = 0.85	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	str     							=  make_duplicate_clusters(a       , a	)
      SCREAMING_SNAKE_CASE_					:	Tuple     							=  {x['base_index'] for cluster in duplicate_clusters for x in cluster}
      SCREAMING_SNAKE_CASE_					:	Dict     							=  {}
      SCREAMING_SNAKE_CASE_					:	str     							=  find_extremes(a       , a       , a	)
      for extremes in extremes_clusters:
            for element in extremes:
                  SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  element
      SCREAMING_SNAKE_CASE_					:	int     							=  duplicate_indices - set(extreme_dict.keys()	)
      SCREAMING_SNAKE_CASE_					:	int     							=  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:
                  SCREAMING_SNAKE_CASE_					:	List[Any]     							=  element['base_index'] in extreme_dict
                  if element["is_extreme"]:
                        SCREAMING_SNAKE_CASE_					:	List[str]     							=  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







import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowercase:	str       			=		'''sshleifer/bart-tiny-random'''
_lowercase:	Union[str, Any]       			=		'''patrickvonplaten/t5-tiny-random'''



@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@cached_property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return AutoConfig.from_pretrained(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.num_hidden_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  self.teacher_config.encoder_layers						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										with self.assertRaises(lowercase__						):
																	create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=lowercase__  ,  d=lowercase__						)






