





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


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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






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

       _snake_case     		=				defaultdict(lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				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	):
       _snake_case     		=				[]
       for base, duplicates in self._duplicate_clusters.items():
         _snake_case     		=				[base] + list(lowerCamelCase	)
         # reformat the cluster to be a list of dict
         _snake_case     		=				[{"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	):
       _snake_case     		=				self.get_duplicate_clusters()
       with open(lowerCamelCase						,   "w"	) as f:
         json.dump(lowerCamelCase						,   lowerCamelCase	)







def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				element
  _snake_case     		=				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_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__       )

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				get_tokens(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				get_tokens(SCREAMING_SNAKE_CASE__       )
  return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


__magic_name__   :						Union[str, Any]    =			None




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  global _shared_dataset
  _snake_case     		=				dataset
  _snake_case     		=				[]
  _snake_case     		=				partial(_find_cluster_extremes_shared					,	jaccard_threshold=SCREAMING_SNAKE_CASE__       )
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	)					,	total=len(SCREAMING_SNAKE_CASE__       )					,	):
      extremes_list.append(SCREAMING_SNAKE_CASE__       )
  return extremes_list




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 0.85       ):







  '''simple docstring'''



  _snake_case     		=				make_duplicate_clusters(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				{x["base_index"] for cluster in duplicate_clusters for x in cluster}
  _snake_case     		=				{}
  _snake_case     		=				find_extremes(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  for extremes in extremes_clusters:
    for element in extremes:
      _snake_case     		=				element
  _snake_case     		=				duplicate_indices - set(extreme_dict.keys()       )
  _snake_case     		=				dataset.filter(lambda SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       : idx not in remove_indices					,	with_indices=SCREAMING_SNAKE_CASE__       )

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      _snake_case     		=				element["base_index"] in extreme_dict
      if element["is_extreme"]:
        _snake_case     		=				extreme_dict[element["base_index"]]["copies"]

  print(f'''Original dataset size: {len(SCREAMING_SNAKE_CASE__       )}'''       )
  print(f'''Number of duplicate clusters: {len(SCREAMING_SNAKE_CASE__       )}'''       )
  print(f'''Files in duplicate cluster: {len(SCREAMING_SNAKE_CASE__       )}'''       )
  print(f'''Unique files in duplicate cluster: {len(SCREAMING_SNAKE_CASE__       )}'''       )
  print(f'''Filtered dataset size: {len(SCREAMING_SNAKE_CASE__       )}'''       )

  return ds_filter, duplicate_clusters









'''simple docstring'''



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'''






     UpperCAmelCase__       :						List[Any]      =   LongformerTokenizer
     UpperCAmelCase__       :						Dict      =   True
     UpperCAmelCase__       :						Union[str, Any]      =   LongformerTokenizerFast
     UpperCAmelCase__       :						List[str]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

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

       _snake_case     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["vocab_file"]	)
       _snake_case     		=				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 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"lower newer"
       _snake_case     		=				"lower newer"
       return input_text, output_text

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

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       self.assertListEqual(tokenizer.encode("Hello world!"						,   add_special_tokens=lowerCamelCase	)						,   [0, 31_414, 232, 328, 2]	)
       self.assertListEqual(
           tokenizer.encode("Hello world! cécé herlolip 418"						,   add_special_tokens=lowerCamelCase	)						,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]						,   )

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"	)

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

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

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

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

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

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

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

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

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

       _snake_case     		=				"Encode <mask> sequence"
       _snake_case     		=				"Encode <mask>sequence"

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

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

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				self.tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				"A, <mask> AllenNLP sentence."
           _snake_case     		=				tokenizer_r.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)
           _snake_case     		=				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"]	)						,   )

           _snake_case     		=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
           _snake_case     		=				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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
           self.assertSequenceEqual(tokens_r["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 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 	UpperCamelCase(					self	):
       for trim_offsets, add_prefix_space in itertools.product([True, False]						,   repeat=2	):
         _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)

         _snake_case     		=				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
         _snake_case     		=				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 	UpperCamelCase(					self	):
       # 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})'''	):
           _snake_case     		=				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
           _snake_case     		=				F'''{text_of_1_token} {text_of_1_token}'''

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )

           _snake_case     		=				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)),
           # )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				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	))						,   )


