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


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






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






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



class 							a				:







 """simple docstring"""






 def __init__(   self						,							*,
     lowerCAmelCase_ = 0.85						,							)    ->   Optional[int]:
        _A	  =    duplication_jaccard_threshold
        _A	  =    NUM_PERM
        _A	  =    MinHashLSH(threshold=self._duplication_jaccard_threshold						,							num_perm=self._num_perm				)

        _A	  =    defaultdict(lowerCAmelCase_				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   None:
        _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 	UpperCAmelCase				(   self				)    ->   List[List[Dict]]:
        _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 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   None:
        _A	  =    self.get_duplicate_clusters()
        with open(lowerCAmelCase_						,							"""w"""				) as f:
               json.dump(lowerCAmelCase_						,							lowerCAmelCase_				)






def 					snake_case    (			snake_case__     :Union[str, Any])		->  Optional[Any]:
       _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 					snake_case    (			snake_case__     :Union[str, Any])		->  List[Any]:
       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash  ,      ThreadedIterator(__snake_case  ,      max_queue_size=10_000)  ,      chunksize=100  ,      ):
                     if data is not None:
                            yield data






def 					snake_case    (			snake_case__     :Optional[Any]  ,      snake_case__     :List[Any])		->  Tuple:
       _A	  =    DuplicationIndex(duplication_jaccard_threshold=__snake_case)

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

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






def 					snake_case    (			snake_case__     :Any  ,      snake_case__     :Tuple)		->  float:
       _A	  =    get_tokens(__snake_case)
       _A	  =    get_tokens(__snake_case)
       return len(tokensa & tokensa) / len(tokensa | tokensa)


_SCREAMING_SNAKE_CASE     				=		None






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






def 					snake_case    (			snake_case__     :List[Any]  ,      snake_case__     :Dict  ,      snake_case__     :List[str])		->  List[str]:
       global _shared_dataset
       _A	  =    dataset
       _A	  =    []
       _A	  =    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 					snake_case    (			snake_case__     :Any  ,      snake_case__     :Optional[Any] = 0.85)		->  Tuple[Type[Dataset], List[List[Dict]]]:
       _A	  =    make_duplicate_clusters(__snake_case  ,      __snake_case)
       _A	  =    {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
       _A	  =    {}
       _A	  =    find_extremes(__snake_case  ,      __snake_case  ,      __snake_case)
       for extremes in extremes_clusters:
              for element in extremes:
                     _A	  =    element
       _A	  =    duplicate_indices - set(extreme_dict.keys())
       _A	  =    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:
                     _A	  =    element["""base_index"""] in extreme_dict
                     if element["is_extreme"]:
                            _A	  =    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












import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  LongformerTokenizer
  _lowerCamelCase    =  True
  _lowerCamelCase    =  LongformerTokenizerFast
  _lowerCamelCase    =  True


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


         """simple docstring"""
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _UpperCAmelCase							=    [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         _UpperCAmelCase							=    {"""unk_token""": """<unk>"""}

         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""merges_file"""]							)
         with open(self.vocab_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write(json.dumps(lowerCamelCase							) + """\n"""							)
         with open(self.merges_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write("""\n""".join(lowerCamelCase							)							)


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


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int       ,				**lowerCamelCase		:			str							)						->	int:


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    """lower newer"""
         return input_text, output_text


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


         """simple docstring"""
         _UpperCAmelCase							=    self.tokenizer_class(self.vocab_file       ,				self.merges_file       ,				**self.special_tokens_map							)
         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         _UpperCAmelCase							=    tokenizer.tokenize(lowerCamelCase							)  # , add_prefix_space=True)
         self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)

         _UpperCAmelCase							=    tokens + [tokenizer.unk_token]
         _UpperCAmelCase							=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase							)       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("""Hello world!"""       ,				add_special_tokens=lowerCamelCase							)       ,				[0, 3_1414, 232, 328, 2]							)
         self.assertListEqual(
             tokenizer.encode("""Hello world! cécé herlolip 418"""       ,				add_special_tokens=lowerCamelCase							)       ,				[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]       ,				)


  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""							)

         _UpperCAmelCase							=    tokenizer.encode("""sequence builders"""       ,				add_special_tokens=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.encode("""multi-sequence build"""       ,				add_special_tokens=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.encode(
             """sequence builders"""       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.encode(
             """sequence builders"""       ,				"""multi-sequence build"""       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase       ,				lowerCamelCase							)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode


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


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    """Encode this sequence."""
         _UpperCAmelCase							=    tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

         # Testing encoder arguments
         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertNotEqual(lowerCamelCase       ,				lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)

         tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
         self.assertNotEqual(lowerCamelCase       ,				lowerCamelCase							)

         # Testing spaces after special tokens
         _UpperCAmelCase							=    """<mask>"""
         tokenizer.add_special_tokens(
             {"""mask_token""": AddedToken(lowerCamelCase       ,				lstrip=lowerCamelCase       ,				rstrip=lowerCamelCase							)}							)  # mask token has a left space
         _UpperCAmelCase							=    tokenizer.convert_tokens_to_ids(lowerCamelCase							)

         _UpperCAmelCase							=    """Encode <mask> sequence"""
         _UpperCAmelCase							=    """Encode <mask>sequence"""

         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase							)
         _UpperCAmelCase							=    encoded.index(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase							)
         _UpperCAmelCase							=    encoded.index(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertNotEqual(lowerCamelCase       ,				lowerCamelCase							)


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


         """simple docstring"""
         pass


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


         """simple docstring"""
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(lowerCamelCase       ,				**lowerCamelCase							)
                       _UpperCAmelCase							=    self.tokenizer_class.from_pretrained(lowerCamelCase       ,				**lowerCamelCase							)
                       _UpperCAmelCase							=    """A, <mask> AllenNLP sentence."""
                       _UpperCAmelCase							=    tokenizer_r.encode_plus(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				return_token_type_ids=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_p.encode_plus(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				return_token_type_ids=lowerCamelCase							)

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["""token_type_ids"""]							)       ,				sum(tokens_p["""token_type_ids"""]							)							)

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)       ,				sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)       ,				)

                       _UpperCAmelCase							=    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
                       _UpperCAmelCase							=    tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p["""input_ids"""]       ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]       ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)

                       self.assertSequenceEqual(
                           lowerCamelCase       ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
                       self.assertSequenceEqual(
                           lowerCamelCase       ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         for trim_offsets, add_prefix_space in itertools.product([True, False]       ,				repeat=2							):
                _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                    self.tmpdirname       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)

                _UpperCAmelCase							=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
                _UpperCAmelCase							=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

                self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]       ,				lowerCamelCase							)

                self.assertEqual(post_processor_state["""add_prefix_space"""]       ,				lowerCamelCase							)
                self.assertEqual(post_processor_state["""trim_offsets"""]       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """simple docstring"""
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                       _UpperCAmelCase							=    """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       _UpperCAmelCase							=    f"""{text_of_1_token} {text_of_1_token}"""

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(len(lowerCamelCase							) + 1, len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(len(lowerCamelCase							) + 1, len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(len(lowerCamelCase							), len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(len(lowerCamelCase							), len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)

                       _UpperCAmelCase							=    f""" {text}"""

                       # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                       #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                       # )
                       # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                       # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                       # self.assertEqual(
                       #     encoding.offset_mapping[1],
                       #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                       # )

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(1, 1 + len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(1 + len(lowerCamelCase							) + 1, 1 + len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(0, 1 + len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(1 + len(lowerCamelCase							), 1 + len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)

                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase       ,				use_fast=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_r(lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				add_special_tokens=lowerCamelCase							)
                       self.assertEqual(encoding.offset_mapping[0]       ,				(0, 1 + len(lowerCamelCase							))							)
                       self.assertEqual(
                           encoding.offset_mapping[1]       ,				(1 + len(lowerCamelCase							), 1 + len(lowerCamelCase							) + 1 + len(lowerCamelCase							))       ,				)