'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowercase__  :      List[str]        =					logging.getLogger()



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  argparse.ArgumentParser()
      parser.add_argument('''-f'''					)
      UpperCAmelCase_            =  parser.parse_args()
      return args.f



def  __lowerCamelCase       (      _UpperCamelCase  :  Any					):


      '''simple docstring'''


      UpperCAmelCase_            =  {}
      UpperCAmelCase_            =  os.path.join(_UpperCamelCase							,    '''all_results.json'''					)
      if os.path.exists(_UpperCamelCase					):
            with open(_UpperCamelCase							,    '''r'''					) as f:
                  UpperCAmelCase_            =  json.load(_UpperCamelCase					)
      else:
            raise ValueError(F"""can't find {path}"""					)
      return results



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  torch.cuda.is_available() and torch_device == '''cuda'''
      return is_using_cuda and is_apex_available()


lowercase__  :      int        =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  @classmethod
  def  lowerCAmelCase__ (      cls			:       int  )				->List[str]:
        # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
        UpperCAmelCase_            =  tempfile.mkdtemp()
        UpperCAmelCase_            =  os.path.join(cls.tmpdir  ,							'''default_config.yml'''  )
        write_basic_config(save_location=cls.configPath  )
        UpperCAmelCase_            =  ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]






  @classmethod
  def  lowerCAmelCase__ (      cls			:       Union[str, Any]  )				->List[Any]:
        shutil.rmtree(cls.tmpdir  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if is_cuda_and_apex_available():
              testargs.append('''--fp16'''  )

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.75  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''glue_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[str]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if torch.cuda.device_count() > 1:
              # Skipping because there are not enough batches to train the model + would need a drop_last to work.
              return

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertLess(result['''perplexity''']  ,							100  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''clm_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       str  )				->str:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertLess(result['''perplexity''']  ,							42  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''mlm_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Any  )				->Tuple:
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        UpperCAmelCase_            =  7 if get_gpu_count() > 1 else 2

        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.75  )
        self.assertLess(result['''train_loss''']  ,							0.5  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''ner_no_trainer'''  )  )  )






  @unittest.skip(reason='''Fix me @muellerzr'''  )
  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Tuple  )				->str:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result['''eval_f1''']  ,							28  )
        self.assertGreaterEqual(result['''eval_exact''']  ,							28  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''qa_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->Any:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.8  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''swag_no_trainer'''  )  )  )






  @slow
  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Dict  )				->List[str]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertGreaterEqual(result['''eval_rouge1''']  ,							10  )
        self.assertGreaterEqual(result['''eval_rouge2''']  ,							2  )
        self.assertGreaterEqual(result['''eval_rougeL''']  ,							7  )
        self.assertGreaterEqual(result['''eval_rougeLsum''']  ,							7  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''summarization_no_trainer'''  )  )  )






  @slow
  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       List[str]  )				->Any:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertGreaterEqual(result['''eval_bleu''']  ,							30  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''translation_no_trainer'''  )  )  )






  @slow
  def  lowerCAmelCase__ (      self			:       List[Any]  )				->List[str]:
        UpperCAmelCase_            =  logging.StreamHandler(sys.stdout  )
        logger.addHandler(UpperCAmelCase__  )

        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        self.assertGreaterEqual(result['''eval_overall_accuracy''']  ,							0.10  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Dict  )				->Any:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

        if is_cuda_and_apex_available():
              testargs.append('''--fp16'''  )

        run_command(self._launch_args + testargs  )
        UpperCAmelCase_            =  get_results(UpperCAmelCase__  )
        # The base model scores a 25%
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.6  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''step_1'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase__  ,							'''image_classification_no_trainer'''  )  )  )






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  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(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # 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''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

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





