



'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


UpperCAmelCase__						:int          =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCAmelCase__						:List[Any]          =			"""cuda""" if torch.cuda.is_available() else """cpu"""
def     __lowercase  (_lowercase,						_lowercase=100,						_lowercase=" "     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Optional[int]				   =				text.split(_UpperCamelCase     )
     return [character.join(text[i : i + n]     ).strip() for i in range(0,						len(_UpperCamelCase     ),						_UpperCamelCase     )]
def     __lowercase  (_lowercase     )   ->     dict:


     """simple docstring"""
     __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				[], []
     for title, text in zip(documents["""title"""],						documents["""text"""]     ):
          if text is not None:
               for passage in split_text(_UpperCamelCase     ):
                    titles.append(title if title is not None else """"""     )
                    texts.append(_UpperCamelCase     )
     return {"title": titles, "text": texts}
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     dict:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				ctx_tokenizer(
         documents["""title"""],						documents["""text"""],						truncation=_UpperCamelCase,						padding="""longest""",						return_tensors="""pt"""     )["""input_ids"""]
     __lowerCamelCase						:       List[Any]				   =				ctx_encoder(input_ids.to(device=_UpperCamelCase     ),						return_dict=_UpperCamelCase     ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						)   ->     Tuple:


     """simple docstring"""
     logger.info("""Step 1 - Create the dataset"""     )
     ######################################

     # The dataset needed for RAG must have three columns:
     # - title (string): title of the document
     # - text (string): text of a passage of the document
     # - embeddings (array of dimension d): DPR representation of the passage
     # Let's say you have documents in tab-separated csv files with columns "title" and "text"
     assert os.path.isfile(rag_example_args.csv_path     ), "Please provide a valid path to a csv file"

     # You can load a Dataset object this way
     __lowerCamelCase						:       str				   =				load_dataset(
         """csv""",						data_files=[rag_example_args.csv_path],						split="""train""",						delimiter="""\t""",						column_names=["""title""", """text"""]     )

     # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

     # Then split the documents into passages of 100 words
     __lowerCamelCase						:       Optional[Any]				   =				dataset.map(_UpperCamelCase,						batched=_UpperCamelCase,						num_proc=processing_args.num_proc     )

     # And compute the embeddings
     __lowerCamelCase						:       Optional[int]				   =				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=_UpperCamelCase     )
     __lowerCamelCase						:       str				   =				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
     __lowerCamelCase						:       List[Any]				   =				Features(
         {"""text""": Value("""string"""     ), """title""": Value("""string"""     ), """embeddings""": Sequence(Value("""float32"""     )     )}     )  # optional, save as float32 instead of float64 to save space
     __lowerCamelCase						:       List[str]				   =				dataset.map(
         partial(_UpperCamelCase,						ctx_encoder=_UpperCamelCase,						ctx_tokenizer=_UpperCamelCase     ),						batched=_UpperCamelCase,						batch_size=processing_args.batch_size,						features=_UpperCamelCase,						)

     # And finally save your dataset
     __lowerCamelCase						:       int				   =				os.path.join(rag_example_args.output_dir,						"""my_knowledge_dataset"""     )
     dataset.save_to_disk(_UpperCamelCase     )
     # from datasets import load_from_disk
     # dataset = load_from_disk(passages_path)  # to reload the dataset

     ######################################
     logger.info("""Step 2 - Index the dataset"""     )
     ######################################

     # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
     __lowerCamelCase						:       Union[str, Any]				   =				faiss.IndexHNSWFlat(index_hnsw_args.d,						index_hnsw_args.m,						faiss.METRIC_INNER_PRODUCT     )
     dataset.add_faiss_index("""embeddings""",						custom_index=_UpperCamelCase     )

     # And save the index
     __lowerCamelCase						:       Optional[int]				   =				os.path.join(rag_example_args.output_dir,						"""my_knowledge_dataset_hnsw_index.faiss"""     )
     dataset.get_index("""embeddings"""     ).save(_UpperCamelCase     )
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     str   		=			field(
           default=str(Path(lowerCAmelCase_  ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'  )    ,   metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}    ,   )
       snake_case__    :     Optional[str]   		=			field(
           default=lowerCAmelCase_    ,   metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}    ,   )
       snake_case__    :     str   		=			field(
           default='facebook/rag-sequence-nq'    ,   metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}    ,   )
       snake_case__    :     str   		=			field(
           default='facebook/dpr-ctx_encoder-multiset-base'    ,   metadata={
               'help': (
                   'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
                   ' \'facebook/dpr-ctx_encoder-multiset-base\''
               )
           }    ,   )
       snake_case__    :     Optional[str]   		=			field(
           default=str(Path(lowerCAmelCase_  ).parent / 'test_run' / 'dummy-kb'  )    ,   metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}    ,   )







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     Optional[int]   		=			field(
           default=lowerCAmelCase_    ,   metadata={
               'help': 'The number of processes to use to split the documents into passages. Default is single process.'
           }    ,   )
       snake_case__    :     int   		=			field(
           default=1_6    ,   metadata={
               'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
           }    ,   )







@dataclass
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     int   		=			field(
           default=7_6_8    ,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}    ,   )
       snake_case__    :     int   		=			field(
           default=1_2_8    ,   metadata={
               'help': (
                   'The number of bi-directional links created for every new element during the HNSW index construction.'
               )
           }    ,   )


if __name__ == "__main__":
   logging.basicConfig(level=logging.WARNING)
   logger.setLevel(logging.INFO)

   UpperCAmelCase__						:List[Any]          =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   UpperCAmelCase__       ,				UpperCAmelCase__       ,				UpperCAmelCase__						:List[Any]          =			parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      UpperCAmelCase__						:Tuple          =			rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)











'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     str   		=			RobertaTokenizer
       snake_case__    :     Optional[int]   		=			RobertaTokenizerFast
       snake_case__    :     Union[str, Any]   		=			True
       snake_case__    :     Tuple   		=			{'cls_token': '<s>'}




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            super().setUp()

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

            __lowerCamelCase						:       Optional[int]				   =				os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]					)
            __lowerCamelCase						:       Union[str, Any]				   =				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(A__					) + """\n"""					)
            with open(self.merges_file	,    """w"""	,    encoding="""utf-8"""					) as fp:
                 fp.write("""\n""".join(A__					)					)




       def 					a_					(					self      : Tuple	,    **A__      : Dict					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : str	,    **A__      : List[Any]					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : Tuple	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				"""lower newer"""
            __lowerCamelCase						:       int				   =				"""lower newer"""
            return input_text, output_text




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.tokenizer_class(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map					)
            __lowerCamelCase						:       Any				   =				"""lower newer"""
            __lowerCamelCase						:       Union[str, Any]				   =				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.tokenize(A__					)  # , add_prefix_space=True)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       List[str]				   =				tokens + [tokenizer.unk_token]
            __lowerCamelCase						:       str				   =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)	,    A__					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("""Hello world!"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 2]					)
            self.assertListEqual(
                tokenizer.encode("""Hello world! cécé herlolip 418"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]	,    )




       @slow
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained("""roberta-base"""					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode("""sequence builders"""	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Dict				   =				tokenizer.encode("""multi-sequence build"""	,    add_special_tokens=A__					)

            __lowerCamelCase						:       Dict				   =				tokenizer.encode(
                """sequence builders"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.encode(
                """sequence builders"""	,    """multi-sequence build"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.build_inputs_with_special_tokens(A__					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.build_inputs_with_special_tokens(A__	,    A__					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_tokenizer()

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

            # Testing encoder arguments
            __lowerCamelCase						:       Union[str, Any]				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertNotEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       int				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(A__	,    A__					)

            tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
            self.assertNotEqual(A__	,    A__					)

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

            __lowerCamelCase						:       str				   =				"""Encode <mask> sequence"""
            __lowerCamelCase						:       Optional[int]				   =				"""Encode <mask>sequence"""

            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				encoded.index(A__					)
            __lowerCamelCase						:       Any				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				encoded.index(A__					)
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(A__	,    A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            pass




       def 					a_					(					self      : str					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				"""A, <mask> AllenNLP sentence."""
                      __lowerCamelCase						:       int				   =				tokenizer_r.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)

                      # 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"""]					)	,    )

                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
                      __lowerCamelCase						:       Any				   =				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, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

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




       def 					a_					(					self      : str					):


            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]	,    repeat=2					):
                 __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)

                 __lowerCamelCase						:       Union[str, Any]				   =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                 __lowerCamelCase						:       Dict				   =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

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

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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                      __lowerCamelCase						:       Optional[Any]				   =				f"{text_of_1_token} {text_of_1_token}"

                      __lowerCamelCase						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       str				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       int				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Tuple				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       str				   =				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)),
                      # )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (1, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					) + 1, 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Dict				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Dict				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )



