






"""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


_lowerCAmelCase            =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCAmelCase            =	'cuda' if torch.cuda.is_available() else 'cpu'


def      UpperCamelCase       ( _A  ,				_A=100  ,				_A=" " )     ->       List[str]:
				lowercase    :      List[Any]       = text.split(_A )
				return [character.join(text[i : i + n] ).strip() for i in range(0  ,				len(_A )  ,				_A )]


def      UpperCamelCase       ( _A )     ->       dict:
				lowercase      ,      lowercase    :      str       = [], []
				for title, text in zip(documents["""title"""]  ,				documents["""text"""] ):
								if text is not None:
												for passage in split_text(_A ):
																titles.append(title if title is not None else """""" )
																texts.append(_A )
				return {"title": titles, "text": texts}


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       dict:
				lowercase    :      Tuple       = ctx_tokenizer(
				    documents["""title"""]  ,				documents["""text"""]  ,				truncation=_A  ,				padding="""longest"""  ,				return_tensors="""pt""" )["""input_ids"""]
				lowercase    :      List[Any]       = ctx_encoder(input_ids.to(device=_A )  ,				return_dict=_A ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				)     ->       List[str]:
				######################################
				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
				lowercase    :      Dict       = 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
				lowercase    :      Tuple       = dataset.map(_A  ,				batched=_A  ,				num_proc=processing_args.num_proc )

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

				# And finally save your dataset
				lowercase    :      Any       = os.path.join(rag_example_args.output_dir  ,				"""my_knowledge_dataset""" )
				dataset.save_to_disk(_A )
				# 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
				lowercase    :      str       = faiss.IndexHNSWFlat(index_hnsw_args.d  ,				index_hnsw_args.m  ,				faiss.METRIC_INNER_PRODUCT )
				dataset.add_faiss_index("""embeddings"""  ,				custom_index=_A )

				# And save the index
				lowercase    :      Dict       = os.path.join(rag_example_args.output_dir  ,				"""my_knowledge_dataset_hnsw_index.faiss""" )
				dataset.get_index("""embeddings""" ).save(_A )
				# dataset.load_faiss_index("embeddings", index_path)  # to reload the index


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    default=str(Path(__snake_case     ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""     )						,						metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}						,						)
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}						,						)
						_SCREAMING_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'"""}						,						)
						_SCREAMING_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'"""
						        )
						    }						,						)
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=str(Path(__snake_case     ).parent / """test_run""" / """dummy-kb"""     )						,						metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}						,						)


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		field(
						    default=__snake_case						,						metadata={
						        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
						    }						,						)
						_SCREAMING_SNAKE_CASE  : int												=		field(
						    default=16						,						metadata={
						        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
						    }						,						)


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : int												=		field(
						    default=768						,						metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}						,						)
						_SCREAMING_SNAKE_CASE  : int												=		field(
						    default=128						,						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)

				_lowerCAmelCase            =	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
				_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase            =	parser.parse_args_into_dataclasses()
				with TemporaryDirectory() as tmp_dir:
								_lowerCAmelCase            =	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, 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      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : List[Any]												=		LongformerTokenizer
						_SCREAMING_SNAKE_CASE  : int												=		True
						_SCREAMING_SNAKE_CASE  : List[str]												=		LongformerTokenizerFast
						_SCREAMING_SNAKE_CASE  : Dict												=		True






						def      __snake_case      (		self  :Dict				)      ->Tuple:
										super().setUp()

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

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






						def      __snake_case      (		self  :List[Any]			, **__magic_name__  :Any				)      ->Tuple:
										kwargs.update(self.special_tokens_map				)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, **__magic_name__  :Optional[Any]				)      ->Dict:
										kwargs.update(self.special_tokens_map				)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Dict				)      ->str:
										lowercase    :      List[str]       = """lower newer"""
										lowercase    :      Any       = """lower newer"""
										return input_text, output_text






						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										lowercase    :      Union[str, Any]       = self.tokenizer_class(self.vocab_file			, self.merges_file			, **self.special_tokens_map				)
										lowercase    :      List[str]       = """lower newer"""
										lowercase    :      Tuple       = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
										lowercase    :      Optional[int]       = tokenizer.tokenize(__magic_name__				)  # , add_prefix_space=True)
										self.assertListEqual(__magic_name__			, __magic_name__				)

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






						def      __snake_case      (		self  :Any				)      ->str:
										lowercase    :      int       = self.get_tokenizer()

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






						@slow
						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										lowercase    :      Any       = self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""				)

										lowercase    :      Tuple       = tokenizer.encode("""sequence builders"""			, add_special_tokens=__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.encode("""multi-sequence build"""			, add_special_tokens=__magic_name__				)

										lowercase    :      Union[str, Any]       = tokenizer.encode(
										    """sequence builders"""			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.encode(
										    """sequence builders"""			, """multi-sequence build"""			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)

										lowercase    :      int       = tokenizer.build_inputs_with_special_tokens(__magic_name__				)
										lowercase    :      List[Any]       = tokenizer.build_inputs_with_special_tokens(__magic_name__			, __magic_name__				)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode






						def      __snake_case      (		self  :Optional[Any]				)      ->int:
										lowercase    :      Optional[int]       = self.get_tokenizer()

										lowercase    :      Tuple       = """Encode this sequence."""
										lowercase    :      Dict       = tokenizer.byte_encoder[""" """.encode("""utf-8"""				)[0]]

										# Testing encoder arguments
										lowercase    :      List[Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      int       = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Optional[Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__			, add_prefix_space=__magic_name__				)
										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
										self.assertEqual(__magic_name__			, __magic_name__				)

										tokenizer.add_special_tokens({"""bos_token""": """<s>"""}				)
										lowercase    :      Union[str, Any]       = tokenizer.encode(__magic_name__			, add_special_tokens=__magic_name__				)
										lowercase    :      List[str]       = tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)

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

										lowercase    :      Any       = """Encode <mask> sequence"""
										lowercase    :      str       = """Encode <mask>sequence"""

										lowercase    :      Optional[int]       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = encoded.index(__magic_name__				)
										lowercase    :      Tuple       = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
										self.assertEqual(__magic_name__			, __magic_name__				)

										lowercase    :      Tuple       = tokenizer.encode(__magic_name__				)
										lowercase    :      List[str]       = encoded.index(__magic_name__				)
										lowercase    :      Optional[Any]       = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
										self.assertNotEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->int:
										pass






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

																		# 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"""]				)			, )

																		lowercase    :      Union[str, Any]       = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
																		lowercase    :      Dict       = 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(
																		    __magic_name__			, ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
																		self.assertSequenceEqual(
																		    __magic_name__			, ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)






						def      __snake_case      (		self  :List[str]				)      ->Tuple:
										for trim_offsets, add_prefix_space in itertools.product([True, False]			, repeat=2				):
														lowercase    :      str       = self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname			, use_fast=__magic_name__			, add_prefix_space=__magic_name__			, trim_offsets=__magic_name__				)

														lowercase    :      str       = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
														lowercase    :      Optional[Any]       = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

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

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






						def      __snake_case      (		self  :Dict				)      ->List[str]:
										# 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})"""				):
																		lowercase    :      Optional[Any]       = """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																		lowercase    :      Optional[Any]       = f"""{text_of_1_token} {text_of_1_token}"""

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

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

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

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

																		lowercase    :      Optional[int]       = 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)),
																		# )

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

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

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