






"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset

_lowerCAmelCase            =	random.Random()


def      UpperCamelCase       ( _A  ,				_A=1.0  ,				_A=None  ,				_A=None )     ->       str:
				if rng is None:
								lowercase    :      List[str]       = global_rng

				lowercase    :      List[str]       = []
				for batch_idx in range(shape[0] ):
								values.append([] )
								for _ in range(shape[1] ):
												values[-1].append(rng.random() * scale )

				return values


class      UpperCamelCase		(unittest.TestCase     ):






						def __init__(		self  :Optional[int]			, __magic_name__  :int			, __magic_name__  :List[str]=7			, __magic_name__  :List[Any]=400			, __magic_name__  :List[Any]=2_000			, __magic_name__  :List[str]=2_048			, __magic_name__  :Any=128			, __magic_name__  :Optional[Any]=1			, __magic_name__  :Union[str, Any]=512			, __magic_name__  :List[Any]=30			, __magic_name__  :Any=44_100			, )      ->Optional[int]:
										lowercase    :      int       = parent
										lowercase    :      Optional[Any]       = batch_size
										lowercase    :      Optional[int]       = min_seq_length
										lowercase    :      Any       = max_seq_length
										lowercase    :      Tuple       = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowercase    :      Union[str, Any]       = spectrogram_length
										lowercase    :      Dict       = feature_size
										lowercase    :      Union[str, Any]       = num_audio_channels
										lowercase    :      Optional[int]       = hop_length
										lowercase    :      List[Any]       = chunk_length
										lowercase    :      Any       = sampling_rate






						def      __snake_case      (		self  :Dict				)      ->Dict:
										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Any=False			, __magic_name__  :str=False				)      ->Tuple:
										def _flatten(__magic_name__  :str				):
														return list(itertools.chain(*__magic_name__				)				)

										if equal_length:
														lowercase    :      List[Any]       = [floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
										else:
														# make sure that inputs increase in size
														lowercase    :      Optional[int]       = [
														    floats_list((x, self.feature_size)				)
														    for x in range(self.min_seq_length			, self.max_seq_length			, self.seq_length_diff				)
														]
										if numpify:
														lowercase    :      List[Any]       = [np.asarray(__magic_name__				) for x in speech_inputs]
										return speech_inputs


@require_torch
@require_torchaudio
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		TvltFeatureExtractor






						def      __snake_case      (		self  :Dict				)      ->Optional[int]:
										lowercase    :      List[Any]       = TvltFeatureExtractionTester(self				)






						def      __snake_case      (		self  :Any				)      ->Tuple:
										lowercase    :      Tuple       = self.feature_extraction_class(**self.feat_extract_dict				)
										self.assertTrue(hasattr(__magic_name__			, """spectrogram_length"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """feature_size"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """num_audio_channels"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """hop_length"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """chunk_length"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """sampling_rate"""				)				)






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

										with tempfile.TemporaryDirectory() as tmpdirname:
														lowercase    :      List[Any]       = feat_extract_first.save_pretrained(__magic_name__				)[0]
														check_json_file_has_correct_format(__magic_name__				)
														lowercase    :      List[str]       = self.feature_extraction_class.from_pretrained(__magic_name__				)

										lowercase    :      Tuple       = feat_extract_first.to_dict()
										lowercase    :      Any       = feat_extract_second.to_dict()
										lowercase    :      Optional[int]       = dict_first.pop("""mel_filters"""				)
										lowercase    :      Dict       = dict_second.pop("""mel_filters"""				)
										self.assertTrue(np.allclose(__magic_name__			, __magic_name__				)				)
										self.assertEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->List[Any]:
										lowercase    :      Tuple       = self.feature_extraction_class(**self.feat_extract_dict				)

										with tempfile.TemporaryDirectory() as tmpdirname:
														lowercase    :      List[str]       = os.path.join(__magic_name__			, """feat_extract.json"""				)
														feat_extract_first.to_json_file(__magic_name__				)
														lowercase    :      Tuple       = self.feature_extraction_class.from_json_file(__magic_name__				)

										lowercase    :      Any       = feat_extract_first.to_dict()
										lowercase    :      str       = feat_extract_second.to_dict()
										lowercase    :      Tuple       = dict_first.pop("""mel_filters"""				)
										lowercase    :      Any       = dict_second.pop("""mel_filters"""				)
										self.assertTrue(np.allclose(__magic_name__			, __magic_name__				)				)
										self.assertEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :List[Any]				)      ->Dict:
										# Initialize feature_extractor
										lowercase    :      int       = self.feature_extraction_class(**self.feat_extract_dict				)

										# create three inputs of length 800, 1000, and 1200
										lowercase    :      List[Any]       = [floats_list((1, x)				)[0] for x in range(800			, 1_400			, 200				)]
										lowercase    :      List[str]       = [np.asarray(__magic_name__				) for speech_input in speech_inputs]

										# Test not batched input
										lowercase    :      Optional[Any]       = feature_extractor(np_speech_inputs[0]			, return_tensors="""np"""			, sampling_rate=44_100				).audio_values

										self.assertTrue(encoded_audios.ndim == 4				)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

										# Test batched
										lowercase    :      int       = feature_extractor(__magic_name__			, return_tensors="""np"""			, sampling_rate=44_100				).audio_values

										self.assertTrue(encoded_audios.ndim == 4				)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

										# Test audio masking
										lowercase    :      Any       = feature_extractor(
										    __magic_name__			, return_tensors="""np"""			, sampling_rate=44_100			, mask_audio=__magic_name__				).audio_values

										self.assertTrue(encoded_audios.ndim == 4				)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

										# Test 2-D numpy arrays are batched.
										lowercase    :      Union[str, Any]       = [floats_list((1, x)				)[0] for x in (800, 800, 800)]
										lowercase    :      List[Any]       = np.asarray(__magic_name__				)
										lowercase    :      int       = feature_extractor(__magic_name__			, return_tensors="""np"""			, sampling_rate=44_100				).audio_values
										self.assertTrue(encoded_audios.ndim == 4				)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)






						def      __snake_case      (		self  :str			, __magic_name__  :Optional[Any]				)      ->List[str]:
										lowercase    :      Any       = load_dataset("""hf-internal-testing/librispeech_asr_dummy"""			, """clean"""			, split="""validation"""				)
										# automatic decoding with librispeech
										lowercase    :      str       = ds.sort("""id"""				).select(range(__magic_name__				)				)[:num_samples]["""audio"""]

										return [x["array"] for x in speech_samples]






						def      __snake_case      (		self  :str				)      ->Optional[int]:
										lowercase    :      List[str]       = self._load_datasamples(1				)
										lowercase    :      Union[str, Any]       = TvltFeatureExtractor()
										lowercase    :      Tuple       = feature_extractor(__magic_name__			, return_tensors="""pt"""				).audio_values

										self.assertEquals(audio_values.shape			, (1, 1, 192, 128)				)

										lowercase    :      List[Any]       = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]				)
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			, __magic_name__			, atol=1E-4				)				)








"""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__				))			, )
