





"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__UpperCamelCase     :						List[str]						       =  [
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]







class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case = None				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= None
  lowerCAmelCase    				= os.path.abspath(os.path.join('examples'				,       'by_feature'     )     )
  lowerCAmelCase    				= os.path.abspath('examples'     )
  for item in os.listdir(_snake_case     ):
   if item not in EXCLUDE_EXAMPLES:
    lowerCAmelCase    				= os.path.join(_snake_case				,       _snake_case     )
    if os.path.isfile(_snake_case     ) and ".py" in item_path:
     with self.subTest(
         tested_script=_snake_case				,       feature_script=_snake_case				,       tested_section='main()' if parser_only else 'training_function()'				,       ):
      lowerCAmelCase    				= compare_against_test(
          os.path.join(_snake_case				,       _snake_case     )				,       _snake_case				,       _snake_case				,       _snake_case     )
      lowerCAmelCase    				= '\n'.join(_snake_case     )
      if special_strings is not None:
       for string in special_strings:
        lowerCAmelCase    				= diff.replace(_snake_case				,       ''     )
      self.assertEqual(_snake_case				,       ''     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.one_complete_example('complete_nlp_example.py'				,       _snake_case     )
  self.one_complete_example('complete_nlp_example.py'				,       _snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= os.path.abspath(os.path.join('examples'				,       'cv_example.py'     )     )
  lowerCAmelCase    				= [
      ' ' * 16 + '{\n\n',
      ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
      ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
      ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
      ' ' * 20 + '"epoch": epoch,\n\n',
      ' ' * 16 + '},\n\n',
      ' ' * 16 + 'step=epoch,\n',
      ' ' * 12,
      ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
  ]
  self.one_complete_example('complete_cv_example.py'				,       _snake_case				,       _snake_case				,       _snake_case     )
  self.one_complete_example('complete_cv_example.py'				,       _snake_case				,       _snake_case				,       _snake_case     )







@mock.patch.dict(os.environ   , {'''TESTING_MOCKED_DATALOADERS''': '''1'''}  )
class       a							(   a__  ):
 snake_case__             =       False



 @classmethod
 def    UpperCamelCase__  (     cls     ):







  """simple docstring"""






  super().setUpClass()
  lowerCAmelCase    				= tempfile.mkdtemp()
  lowerCAmelCase    				= os.path.join(cls._tmpdir				,       'default_config.yml'     )

  write_basic_config(save_location=cls.configPath     )
  lowerCAmelCase    				= ['accelerate', 'launch', '--config_file', cls.configPath]



 @classmethod
 def    UpperCamelCase__  (     cls     ):







  """simple docstring"""






  super().tearDownClass()
  shutil.rmtree(cls._tmpdir     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
  run_command(self._launch_args + testargs     )
  self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,       'epoch_0'     )     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
  lowerCAmelCase    				= run_command(self._launch_args + testargs     )
  self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,       'step_2'     )     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,       "epoch_0"     )}\n        '.split()
  lowerCAmelCase    				= run_command(self._launch_args + testargs				,       return_stdout=_snake_case     )
  self.assertNotIn('epoch 0:'				,       _snake_case     )
  self.assertIn('epoch 1:'				,       _snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,       "step_2"     )}\n        '.split()
  lowerCAmelCase    				= run_command(self._launch_args + testargs				,       return_stdout=_snake_case     )
  if torch.cuda.is_available():
   lowerCAmelCase    				= torch.cuda.device_count()
  else:
   lowerCAmelCase    				= 1
  if num_processes > 1:
   self.assertNotIn('epoch 0:'				,       _snake_case     )
   self.assertIn('epoch 1:'				,       _snake_case     )
  else:
   self.assertIn('epoch 0:'				,       _snake_case     )
   self.assertIn('epoch 1:'				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
  with mock.patch.dict(os.environ				,       {'TESTING_MOCKED_DATALOADERS': '0'}     ):
   lowerCAmelCase    				= run_command(self._launch_args + testargs				,       return_stdout=_snake_case     )
   lowerCAmelCase    				= re.findall('({.+})'				,       _snake_case     )
   lowerCAmelCase    				= [r for r in results if 'accuracy' in r][-1]
   lowerCAmelCase    				= ast.literal_eval(_snake_case     )
   self.assertGreaterEqual(results['accuracy']				,       0.75     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ['examples/by_feature/multi_process_metrics.py']
  run_command(self._launch_args + testargs     )



 @require_trackers
 @mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  with tempfile.TemporaryDirectory() as tmpdir:
   lowerCAmelCase    				= F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
   run_command(self._launch_args + testargs     )
   self.assertTrue(os.path.exists(os.path.join(_snake_case				,       'tracking'     )     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ['examples/by_feature/gradient_accumulation.py']
  run_command(self._launch_args + testargs     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ['examples/by_feature/local_sgd.py']
  run_command(self._launch_args + testargs     )













"""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__				))			, )
