'''simple docstring'''






import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


SCREAMING_SNAKE_CASE_	   =			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
    from run_translation import main  # noqa


set_seed(42)
SCREAMING_SNAKE_CASE_	   =			'sshleifer/student_marian_en_ro_6_1'
SCREAMING_SNAKE_CASE_	   =			'sshleifer/tiny-mbart'




@require_torch
class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def    lowercase_  (							self   ,    snake_case_=False   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.run_trainer(
        eval_steps=1   ,    max_len=12   ,    model_name=snake_case_   ,    num_train_epochs=1   ,    distributed=snake_case_   ,    extra_args_str=snake_case_   ,    predict_with_generate=snake_case_   ,    do_train=snake_case_   ,    do_eval=snake_case_   ,    do_predict=snake_case_   ,    )
    __UpperCAmelCase:					str		= TrainerState.load_from_json(os.path.join(snake_case_   ,    """trainer_state.json"""					)					).log_history

    if not do_eval:
      return

    __UpperCAmelCase:					Dict		= [log for log in logs if """eval_loss""" in log.keys()]

    __UpperCAmelCase:					Any		= eval_metrics[0]
    if predict_with_generate:
      assert "eval_bleu" in first_step_stats

      __UpperCAmelCase:					Any		= eval_metrics[-1]
      assert isinstance(last_step_stats["""eval_bleu"""]   ,    snake_case_					)
      assert not math.isnan(float(last_step_stats["""eval_loss"""]					)					), "eval_loss must not be `nan`"







  @require_torch_non_multi_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick()







  @require_torch_multi_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_					)







  @require_torch_multi_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_					)







  @unittest.skip("""Requires an update of the env running those tests"""					)
  @require_torch_multi_gpu
  @require_fairscale
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_   ,    extra_args_str="""--sharded_ddp simple"""					)







  @unittest.skip("""Requires an update of the env running those tests"""					)
  @require_torch_multi_gpu
  @require_fairscale
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_   ,    extra_args_str="""--sharded_ddp simple --fp16"""					)







  @unittest.skip("""Requires an update of the env running those tests"""					)
  @require_torch_multi_gpu
  @require_fairscale
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_   ,    extra_args_str="""--sharded_ddp zero_dp_2"""   ,    predict_with_generate=snake_case_					)







  @unittest.skip("""Requires an update of the env running those tests"""					)
  @require_torch_multi_gpu
  @require_fairscale
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(
        distributed=snake_case_   ,    extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""   ,    predict_with_generate=snake_case_					)







  @require_apex
  @require_torch_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_   ,    extra_args_str="""--fp16 --fp16_backend=apex"""					)
    # test 2nd time - was getting eval_loss': nan'
    # to reproduce the problem set distributed=False
    self.run_seqaseq_quick(distributed=snake_case_   ,    extra_args_str="""--fp16 --fp16_backend=apex"""					)







  @parameterized.expand(["""base""", """low""", """high""", """mixed"""]					)
  @require_torch_multi_gpu
  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= {
        # test with the default log_level - should be info and thus log info once
        """base""": {"""extra_args_str""": """""", """n_matches""": 1},
        # test with low log_level and log_level_replica - should be noisy on all processes
        # now the info string should appear twice on 2 processes
        """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
        # test with high log_level and low log_level_replica
        # now the info string should appear once only on the replica
        """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
        # test with high log_level and log_level_replica - should be quiet on all processes
        """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
    }

    __UpperCAmelCase:					Optional[int]		= experiments[experiment_id]
    __UpperCAmelCase:					int		= {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
    __UpperCAmelCase:					Optional[Any]		= """Running training"""
    with CaptureStderr() as cl:
      self.run_seqaseq_quick(**snake_case_   ,    extra_args_str=data["""extra_args_str"""]					)
    __UpperCAmelCase:					Tuple		= len(re.findall(snake_case_   ,    cl.err					)					)
    self.assertEqual(snake_case_   ,    data["""n_matches"""]					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.run_trainer(
        eval_steps=2   ,    max_len=128   ,    model_name=snake_case_   ,    learning_rate=3e-4   ,    num_train_epochs=10   ,    distributed=snake_case_   ,    )

    # Check metrics
    __UpperCAmelCase:					str		= TrainerState.load_from_json(os.path.join(snake_case_   ,    """trainer_state.json"""					)					).log_history
    __UpperCAmelCase:					Any		= [log for log in logs if """eval_loss""" in log.keys()]
    __UpperCAmelCase:					Tuple		= eval_metrics[0]
    __UpperCAmelCase:					Any		= eval_metrics[-1]

    assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
    assert isinstance(last_step_stats["""eval_bleu"""]   ,    snake_case_					)

    # test if do_predict saves generations and metrics
    __UpperCAmelCase:					Any		= os.listdir(snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= {os.path.basename(snake_case_					) for p in contents}
    assert "generated_predictions.txt" in contents
    assert "predict_results.json" in contents







  @slow
  @require_bitsandbytes
  def    lowercase_  (							self					):

    '''simple docstring'''







    from transformers.training_args import OptimizerNames

    def train_and_return_metrics(snake_case_					) -> Tuple[int, float]:
      __UpperCAmelCase:					str		= """--skip_memory_metrics 0"""

      __UpperCAmelCase:					Union[str, Any]		= self.run_trainer(
          max_len=128   ,    model_name=snake_case_   ,    learning_rate=3e-4   ,    num_train_epochs=1   ,    optim=snake_case_   ,    distributed=snake_case_   ,    extra_args_str=snake_case_   ,    do_eval=snake_case_   ,    do_predict=snake_case_   ,    n_gpus_to_use=1   ,    )

      # Check metrics
      __UpperCAmelCase:					Optional[int]		= TrainerState.load_from_json(Path(snake_case_   ,    """trainer_state.json"""					)					).log_history
      __UpperCAmelCase:					Any		= int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20					)
      __UpperCAmelCase:					List[str]		= int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20					)

      __UpperCAmelCase:					Optional[Any]		= logs[0]["""train_loss"""]
      return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

    __UpperCAmelCase:					List[str]		= train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value					)
    __UpperCAmelCase:					Tuple		= train_and_return_metrics(OptimizerNames.ADAMW_BNB.value					)

    __UpperCAmelCase:					Optional[int]		= gpu_alloc_mem_orig - gpu_alloc_mem_bnb

    __UpperCAmelCase:					List[str]		= gpu_peak_mem_orig + gpu_alloc_mem_orig
    __UpperCAmelCase:					Tuple		= gpu_peak_mem_bnb + gpu_alloc_mem_bnb
    __UpperCAmelCase:					Tuple		= gpu_total_mem_orig - gpu_total_mem_bnb

    # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
    # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
    # in 2 bytes and the diff in optim memory usage is derived as so:
    #
    # - normal 25*8=~200MB (8 bytes per param)
    # - bnb    25*2= ~50MB (2 bytes per param)
    #
    # Thus we should expect ~150MB total memory saved.
    #
    # Peak memory should be the same - the total should be different by about that same margin
    #
    # After leaving a small margin to accommodate for differences between gpus let's check
    # that we have at least 120MB in savings
    __UpperCAmelCase:					Dict		= 120

    # uncomment the following if this test starts failing - requires py38 for a new print feature
    # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
    # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
    # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
    # print(f"{gpu_alloc_mem_diff=}MB")
    # print(f"{gpu_peak_mem_diff=}MB")
    # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
    # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

    self.assertGreater(
        snake_case_   ,    snake_case_   ,    """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
        F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
        F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''   ,    )

    self.assertGreater(
        snake_case_   ,    snake_case_   ,    """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
        F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
        F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''   ,    )

    self.assertEqual(
        snake_case_   ,    snake_case_   ,    F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = 3e-3   ,    snake_case_ = "adafactor"   ,    snake_case_ = False   ,    snake_case_ = None   ,    snake_case_ = 0   ,    snake_case_ = True   ,    snake_case_ = True   ,    snake_case_ = True   ,    snake_case_ = True   ,    snake_case_ = None   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
    __UpperCAmelCase:					Union[str, Any]		= self.get_auto_remove_tmp_dir()
    __UpperCAmelCase:					Union[str, Any]		= F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(snake_case_					)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(snake_case_					)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

    __UpperCAmelCase:					Optional[int]		= F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(snake_case_					)}
        '''.split()

    __UpperCAmelCase:					Any		= """
            --do_predict
        """.split()

    __UpperCAmelCase:					int		= []
    if do_train:
      args += args_train

    if do_eval:
      args += args_eval

    if do_predict:
      args += args_predict

    if predict_with_generate:
      args += "--predict_with_generate".split()

    if do_train:
      if optim == "adafactor":
        args += "--adafactor".split()
      else:
        args += F'''--optim {optim}'''.split()

    if extra_args_str is not None:
      args += extra_args_str.split()

    if distributed:
      if n_gpus_to_use is None:
        __UpperCAmelCase:					Optional[Any]		= get_gpu_count()
      __UpperCAmelCase:					Union[str, Any]		= get_torch_dist_unique_port()
      __UpperCAmelCase:					str		= F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
      __UpperCAmelCase:					Optional[int]		= [sys.executable] + distributed_args + args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(snake_case_   ,    env=self.get_env()					)
    else:
      __UpperCAmelCase:					str		= ["""run_translation.py"""] + args
      with patch.object(snake_case_   ,    """argv"""   ,    snake_case_					):
        main()

    return output_dir
'''simple docstring'''






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class    a (       unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					JukeboxTokenizer
  __lowerCAmelCase				      =					{
      """artist""": """Zac Brown Band""",
      """genres""": """Country""",
      """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
  }







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					Dict		= JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""					)
    __UpperCAmelCase:					List[str]		= tokenizer(**self.metas					)["""input_ids"""]
    # fmt: off
    __UpperCAmelCase:					int		= [
        torch.tensor([[
            0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
            76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
            44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
            47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
            76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
            30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
            27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
            45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
            41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
            76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
            76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
            64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
            30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
            27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
            34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
            27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
            41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
            44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
            76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
            32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
            40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
            45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
            31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
            45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
            34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
            31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
            40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
            38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
            76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
            41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
            27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
            46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
            41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
            46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
            41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
            40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
            27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
            76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
            41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
            76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
            27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
            34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
            44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
            40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
            46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
            38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
            40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
            27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
            76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
            76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
            76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
            76, 76]]					),
        torch.tensor([[0, 0, 0, 1069, 11]]					),
        torch.tensor([[0, 0, 0, 1069, 11]]					),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,    EXPECTED_OUTPUT[0]					)					)
    self.assertTrue(torch.allclose(tokens[1]   ,    EXPECTED_OUTPUT[1]					)					)
    self.assertTrue(torch.allclose(tokens[2]   ,    EXPECTED_OUTPUT[2]					)					)







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					Any		= JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""					)
    __UpperCAmelCase:					Tuple		= tokenizer(**self.metas					)["""input_ids"""]
    # fmt: off
    __UpperCAmelCase:					int		= [
        torch.tensor([[
            0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
            31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
            31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
            40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
            79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
            77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
            27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
            37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
            32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
            77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
            77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
            46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
            77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
            77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
            77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
            77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
            64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
            40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
            40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
            38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
            31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
            41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
            46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
            41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
            31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
            31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
            44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
            31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
            38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
            40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
            27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
            31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
            34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
            31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
            31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
            45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
            31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
            15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
            11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
            45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
            41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
            44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
            46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
            27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
            35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
            77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
            31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
            41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
            77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
            40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
            77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
            27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
            77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
            77, 77, 77, 77, 77, 77]]					),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]					),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]					),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,    EXPECTED_OUTPUT[0]					)					)
    self.assertTrue(torch.allclose(tokens[1]   ,    EXPECTED_OUTPUT[1]					)					)
    self.assertTrue(torch.allclose(tokens[2]   ,    EXPECTED_OUTPUT[2]					)					)