






import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

__UpperCAmelCase								=						logging.get_logger(__name__)




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							str  ):


					'''simple docstring'''


					def run_func(__snake_case    :							Optional[Any]  ):
										@wraps(lowerCamelCase_  )
										def run_in_eager_mode(*__snake_case    :							int     ,       **__snake_case    :							str  ):
															return func(*lowerCamelCase_     ,       **lowerCamelCase_  )

										@wraps(lowerCamelCase_  )
										@tf.function(experimental_compile=lowerCamelCase_  )
										def run_in_graph_mode(*__snake_case    :							Optional[int]     ,       **__snake_case    :							Optional[Any]  ):
															return func(*lowerCamelCase_     ,       **lowerCamelCase_  )

										if do_eager_mode is True:
															if use_xla is not False:
																				raise ValueError(
																				    'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'  )
															return run_in_eager_mode
										else:
															return run_in_graph_mode

					return run_func




def      lowercase__      (     __snake_case    :							List[Any]     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						random.Random()
					UpperCAmelCase_    :      List[Any]            =						[rng.randint(0     ,       vocab_size - 1  ) for i in range(batch_size * sequence_length  )]
					return tf.constant(lowerCamelCase_     ,       shape=(batch_size, sequence_length)     ,       dtype=tf.intaa  )






class   lowerCamelCase      (_a			):







		'''simple docstring'''



		_snake_case    :    TensorFlowBenchmarkArguments
		_snake_case    :    PretrainedConfig
		_snake_case    :    str       						=  "TensorFlow"



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return tf.__version__



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							float:
							UpperCAmelCase_    :      Union[str, Any]            =						self.args.strategy
							if strategy is None:
												raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )
							UpperCAmelCase_    :      Optional[int]            =						self._prepare_inference_func(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							return self._measure_speed(_inference   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							float:
							UpperCAmelCase_    :      Dict            =						self.args.strategy
							if strategy is None:
												raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )
							UpperCAmelCase_    :      str            =						self._prepare_train_func(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							return self._measure_speed(_train   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							[Memory, Optional[MemorySummary]]:

							if self.args.is_gpu:
												tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						self.args.strategy
							if strategy is None:
												raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )
							UpperCAmelCase_    :      Any            =						self._prepare_inference_func(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							return self._measure_memory(_inference   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							[Memory, Optional[MemorySummary]]:

							if self.args.is_gpu:
												tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						self.args.strategy
							if strategy is None:
												raise ValueError('A device strategy has to be initialized before using TensorFlow.'   )

							UpperCAmelCase_    :      Union[str, Any]            =						self._prepare_train_func(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							return self._measure_memory(_train   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Callable[[], None]:
							UpperCAmelCase_    :      List[str]            =						self.config_dict[model_name]

							if self.args.fpaa:
												raise NotImplementedError('Mixed precision is currently not supported.'   )

							UpperCAmelCase_    :      Union[str, Any]            =						(
							    hasattr(_UpperCamelCase				,		'architectures'   )
							    and isinstance(config.architectures				,		_UpperCamelCase   )
							    and len(config.architectures   ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
												try:
																	UpperCAmelCase_    :      List[Any]            =						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
																	UpperCAmelCase_    :      Union[str, Any]            =						__import__('transformers'				,		fromlist=[model_class]   )
																	UpperCAmelCase_    :      Dict            =						getattr(_UpperCamelCase				,		_UpperCamelCase   )
																	UpperCAmelCase_    :      Union[str, Any]            =						model_cls(_UpperCamelCase   )
												except ImportError:
																	raise ImportError(
																	    f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																	    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'   )
							else:
												UpperCAmelCase_    :      Dict            =						TF_MODEL_MAPPING[config.__class__](_UpperCamelCase   )

							# encoder-decoder has vocab size saved differently
							UpperCAmelCase_    :      Tuple            =						config.vocab_size if hasattr(_UpperCamelCase				,		'vocab_size'   ) else config.encoder.vocab_size
							UpperCAmelCase_    :      List[Any]            =						random_input_ids(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							@run_with_tf_optimizations(self.args.eager_mode				,		self.args.use_xla   )
							def encoder_decoder_forward():
												return model(_UpperCamelCase				,		decoder_input_ids=_UpperCamelCase				,		training=_UpperCamelCase   )

							@run_with_tf_optimizations(self.args.eager_mode				,		self.args.use_xla   )
							def encoder_forward():
												return model(_UpperCamelCase				,		training=_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Callable[[], None]:
							UpperCAmelCase_    :      List[Any]            =						self.config_dict[model_name]

							if self.args.eager_mode is not False:
												raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'   )

							if self.args.fpaa:
												raise NotImplementedError('Mixed precision is currently not supported.'   )

							UpperCAmelCase_    :      Optional[Any]            =						(
							    hasattr(_UpperCamelCase				,		'architectures'   )
							    and isinstance(config.architectures				,		_UpperCamelCase   )
							    and len(config.architectures   ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
												try:
																	UpperCAmelCase_    :      List[Any]            =						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
																	UpperCAmelCase_    :      str            =						__import__('transformers'				,		fromlist=[model_class]   )
																	UpperCAmelCase_    :      Tuple            =						getattr(_UpperCamelCase				,		_UpperCamelCase   )
																	UpperCAmelCase_    :      Optional[int]            =						model_cls(_UpperCamelCase   )
												except ImportError:
																	raise ImportError(
																	    f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																	    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'   )
							else:
												UpperCAmelCase_    :      str            =						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_UpperCamelCase   )

							# encoder-decoder has vocab size saved differently
							UpperCAmelCase_    :      List[str]            =						config.vocab_size if hasattr(_UpperCamelCase				,		'vocab_size'   ) else config.encoder.vocab_size
							UpperCAmelCase_    :      Union[str, Any]            =						random_input_ids(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							@run_with_tf_optimizations(self.args.eager_mode				,		self.args.use_xla   )
							def encoder_decoder_train():
												UpperCAmelCase_    :      Any            =						model(_UpperCamelCase				,		decoder_input_ids=_UpperCamelCase				,		labels=_UpperCamelCase				,		training=_UpperCamelCase   )[0]
												UpperCAmelCase_    :      List[str]            =						tf.gradients(_UpperCamelCase				,		model.trainable_variables   )
												return gradients

							@run_with_tf_optimizations(self.args.eager_mode				,		self.args.use_xla   )
							def encoder_train():
												UpperCAmelCase_    :      Optional[Any]            =						model(_UpperCamelCase				,		labels=_UpperCamelCase				,		training=_UpperCamelCase   )[0]
												UpperCAmelCase_    :      List[str]            =						tf.gradients(_UpperCamelCase				,		model.trainable_variables   )
												return gradients

							UpperCAmelCase_    :      str            =						encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							float:

							with self.args.strategy.scope():
												try:
																	if self.args.is_tpu or self.args.use_xla:
																						# run additional 10 times to stabilize compilation for tpu
																						logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'   )
																						timeit.repeat(_UpperCamelCase				,		repeat=1				,		number=5   )

																	# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																	UpperCAmelCase_    :      Dict            =						timeit.repeat(
																	    _UpperCamelCase				,		repeat=self.args.repeat				,		number=1_0				,		)

																	return min(_UpperCamelCase   ) / 1_0.0
												except ResourceExhaustedError as e:
																	self.print_fn(f"Doesn\'t fit on GPU. {e}"   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							[Memory, MemorySummary]:
							logger.info(
							    'Note that TensorFlow allocates more memory than '
							    'it might need to speed up computation. '
							    'The memory reported here corresponds to the memory '
							    'reported by `nvidia-smi`, which can vary depending '
							    'on total available memory on the GPU that is used.'   )
							with self.args.strategy.scope():
												try:
																	if self.args.trace_memory_line_by_line:
																						if not self.args.eager_mode:
																											raise ValueError(
																											    '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
																											    ' consumption line by line.'   )
																						UpperCAmelCase_    :      str            =						start_memory_tracing('transformers'   )

																	if self.args.is_tpu:
																						# tpu
																						raise NotImplementedError(
																						    'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
																						    ' with `args.memory=False`'   )
																	elif self.args.is_gpu:
																						# gpu
																						if not is_pyanvml_available():
																											logger.warning(
																											    'py3nvml not installed, we won\'t log GPU memory usage. '
																											    'Install py3nvml (pip install py3nvml) to log information about GPU.'   )
																											UpperCAmelCase_    :      Dict            =						'N/A'
																						else:
																											logger.info(
																											    'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
																											    ' running on the same GPU.'   )
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											UpperCAmelCase_    :      int            =						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
																											UpperCAmelCase_    :      str            =						nvml.nvmlDeviceGetMemoryInfo(_UpperCamelCase   )
																											UpperCAmelCase_    :      Dict            =						meminfo.used
																											UpperCAmelCase_    :      Any            =						Memory(_UpperCamelCase   )
																											# shutdown nvml
																											nvml.nvmlShutdown()
																	else:
																						# cpu
																						if self.args.trace_memory_line_by_line:
																											logger.info(
																											    'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
																											    ' TensorFlow.'   )
																											UpperCAmelCase_    :      str            =						None
																						else:
																											UpperCAmelCase_    :      Any            =						measure_peak_memory_cpu(_UpperCamelCase   )
																											UpperCAmelCase_    :      Dict            =						Memory(_UpperCamelCase   ) if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) else memory_bytes
																	if self.args.trace_memory_line_by_line:
																						UpperCAmelCase_    :      Optional[int]            =						stop_memory_tracing(_UpperCamelCase   )
																						if memory is None:
																											UpperCAmelCase_    :      List[str]            =						summary.total
																	else:
																						UpperCAmelCase_    :      Optional[int]            =						None

																	return memory, summary
												except ResourceExhaustedError as e:
																	self.print_fn(f"Doesn\'t fit on GPU. {e}"   )
																	return "N/A", None










import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class _lowerCamelCase(						unittest.TestCase   ):
       lowercase_ :	Dict     =			JukeboxTokenizer
       lowercase_ :	Dict     =			{
           """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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           import torch

           _lowercase       :							str      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics')
           _lowercase       :							Optional[Any]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 71_69, 5_07, 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, 10_69, 11]]),
               torch.tensor([[0, 0, 0, 10_69, 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 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           import torch

           _lowercase       :							List[str]      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics')
           _lowercase       :							List[str]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 10_69, 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, 10_69, 11, -1, -1, -1, -1]]),
               torch.tensor([[0, 0, 0, 10_69, 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]))


