'''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


_lowerCAmelCase  :Union[str, Any]  				=    re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_lowerCAmelCase  :Tuple  				=    10
_lowerCAmelCase  :Optional[int]  				=    256


def     __lowerCAmelCase						(       a_		)      ->	Optional[MinHash]:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       a_		)      ->	Set[str]:





     '''simple docstring'''





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






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							*,
        lowercase__ = 0.8_5    ,							)							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       duplication_jaccard_threshold
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       NUM_PERM
         SCREAMING_SNAKE_CASE					:			Tuple    	=       MinHashLSH(threshold=self._duplication_jaccard_threshold    ,							num_perm=self._num_perm )

         SCREAMING_SNAKE_CASE					:			Any    	=       defaultdict(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> None:
         SCREAMING_SNAKE_CASE					:			Dict    	=       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 )							-> List[List[Dict]]:
         SCREAMING_SNAKE_CASE					:			Any    	=       []
         for base, duplicates in self._duplicate_clusters.items():
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [base] + list(lowercase__ )
              # reformat the cluster to be a list of dict
              SCREAMING_SNAKE_CASE					:			List[str]    	=       [{'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    ,							lowercase__ )							-> None:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_duplicate_clusters()
         with open(lowercase__    ,							'w' ) as f:
              json.dump(lowercase__    ,							lowercase__ )






def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       element
     SCREAMING_SNAKE_CASE					:			str    	=       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     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash       ,      ThreadedIterator(a_       ,      max_queue_size=1_0000		)       ,      chunksize=100       ,      ):
               if data is not None:
                    yield data


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       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     __lowerCAmelCase						(       a_       ,      a_		)      ->	float:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       get_tokens(a_		)
     SCREAMING_SNAKE_CASE					:			str    	=       get_tokens(a_		)
     return len(tokensa & tokensa		) / len(tokensa | tokensa		)


_lowerCAmelCase  :List[str]  				=    None


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Dict:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Optional[int]:





     '''simple docstring'''





     global _shared_dataset
     SCREAMING_SNAKE_CASE					:			List[str]    	=       dataset
     SCREAMING_SNAKE_CASE					:			int    	=       []
     SCREAMING_SNAKE_CASE					:			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     __lowerCAmelCase						(       a_       ,      a_ = 0.85		)      ->	Tuple[Type[Dataset], List[List[Dict]]]:





     '''simple docstring'''





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




'''simple docstring'''





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=True    ,							lowercase__=None    ,							**lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       parent
         SCREAMING_SNAKE_CASE					:			str    	=       config_class
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       has_text_modality
         SCREAMING_SNAKE_CASE					:			Any    	=       kwargs
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       common_properties






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.config_class(**self.inputs_dict )
         SCREAMING_SNAKE_CASE					:			int    	=       (
             ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
             if self.common_properties is None
             else self.common_properties
         )

         # Add common fields for text models
         if self.has_text_modality:
              common_properties.extend(['vocab_size'] )

         # Test that config has the common properties as getters
         for prop in common_properties:
              self.parent.assertTrue(hasattr(lowercase__    ,							lowercase__ )    ,							msg=F"""`{prop}` does not exist""" )

         # Test that config has the common properties as setter
         for idx, name in enumerate(lowercase__ ):
              try:
                   setattr(lowercase__    ,							lowercase__    ,							lowercase__ )
                   self.parent.assertEqual(
                       getattr(lowercase__    ,							lowercase__ )    ,							lowercase__    ,							msg=F"""`{name} value {idx} expected, but was {getattr(lowercase__    ,							lowercase__ )}""" )
              except NotImplementedError:
                   # Some models might not be able to implement setters for common_properties
                   # In that case, a NotImplementedError is raised
                   pass

        # Test if config class can be called with Config(prop_name=..)
         for idx, name in enumerate(lowercase__ ):
              try:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.config_class(**{name: idx} )
                   self.parent.assertEqual(
                       getattr(lowercase__    ,							lowercase__ )    ,							lowercase__    ,							msg=F"""`{name} value {idx} expected, but was {getattr(lowercase__    ,							lowercase__ )}""" )
              except NotImplementedError:
                   # Some models might not be able to implement setters for common_properties
                   # In that case, a NotImplementedError is raised
                   pass






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.config_class(**self.inputs_dict )
         SCREAMING_SNAKE_CASE					:			Any    	=       json.loads(config.to_json_string() )
         for key, value in self.inputs_dict.items():
              self.parent.assertEqual(obj[key]    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       os.path.join(lowercase__    ,							'config.json' )
              config_first.to_json_file(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.config_class.from_json_file(lowercase__ )

         self.parent.assertEqual(config_second.to_dict()    ,							config_first.to_dict() )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
              config_first.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.config_class.from_pretrained(lowercase__ )

         self.parent.assertEqual(config_second.to_dict()    ,							config_first.to_dict() )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.config_class(**self.inputs_dict )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       'test'
         with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       os.path.join(lowercase__    ,							lowercase__ )
              config_first.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.config_class.from_pretrained(lowercase__    ,							subfolder=lowercase__ )

         self.parent.assertEqual(config_second.to_dict()    ,							config_first.to_dict() )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.config_class(**self.inputs_dict    ,							num_labels=5 )
         self.parent.assertEqual(len(config.idalabel )    ,							5 )
         self.parent.assertEqual(len(config.labelaid )    ,							5 )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       3
         self.parent.assertEqual(len(config.idalabel )    ,							3 )
         self.parent.assertEqual(len(config.labelaid )    ,							3 )






    def       _UpperCamelCase    (       self )							-> Dict:
         if self.config_class.is_composition:
              return
         SCREAMING_SNAKE_CASE					:			int    	=       self.config_class()
         self.parent.assertIsNotNone(lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       copy.deepcopy(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       self.config_class(**lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       []
         for key, value in config_common_kwargs.items():
              if key == "torch_dtype":
                   if not is_torch_available():
                        continue
                   else:
                        import torch

                        if config.torch_dtype != torch.floataa:
                             wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa) )
              elif getattr(lowercase__    ,							lowercase__ ) != value:
                   wrong_values.append((key, getattr(lowercase__    ,							lowercase__ ), value) )

         if len(lowercase__ ) > 0:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       '\n'.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
              raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
         self.create_and_test_config_from_and_save_pretrained()
         self.create_and_test_config_from_and_save_pretrained_subfolder()
         self.create_and_test_config_with_num_labels()
         self.check_config_can_be_init_without_params()
         self.check_config_arguments_init()



