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


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

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[MinHash]:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Set[str]:




       '''simple docstring'''






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





class      lowerCAmelCase						:



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

            _A=	defaultdict(lowerCAmelCase__       )



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





     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	self.get_duplicate_clusters()
            with open(lowerCAmelCase__				,      'w'       ) as f:
                   json.dump(lowerCAmelCase__				,      lowerCAmelCase__       )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       _A,			_A=	element
       _A=	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 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash       ,					ThreadedIterator(lowerCAmelCase_       ,					max_queue_size=1_00_00   )       ,					chunksize=1_00       ,					):
                     if data is not None:
                            yield data

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       _A=	DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_   )

       for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_   )   )       ,					max_queue_size=1_00   )   ):
              di.add(lowerCAmelCase_       ,					lowerCAmelCase_   )

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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						float:




       '''simple docstring'''






       _A=	get_tokens(lowerCAmelCase_   )
       _A=	get_tokens(lowerCAmelCase_   )
       return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


UpperCAmelCase_								=							None

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






       global _shared_dataset
       _A=	dataset
       _A=	[]
       _A=	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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_ = 0.85   )		->						Tuple[Type[Dataset], List[List[Dict]]]:




       '''simple docstring'''






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

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )