




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
     from fairscale.optim import OSS


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=None       ,     *SCREAMING_SNAKE_CASE__					:					List[Any]       ,     **SCREAMING_SNAKE_CASE__					:					str       )				->	List[str]:
         super().__init__(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if config is None:
              assert isinstance(self.model       ,     SCREAMING_SNAKE_CASE__       ), (
                  "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                  f' {self.model.__class__}'
              )
              lowerCAmelCase__          =			self.model.config
         else:
              lowerCAmelCase__          =			config

         lowerCAmelCase__          =			data_args
         lowerCAmelCase__          =			self.config.tgt_vocab_size if isinstance(self.config       ,     SCREAMING_SNAKE_CASE__       ) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
              assert self.config.pad_token_id is not None, (
                  "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                  " calculation or doing label smoothing."
              )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
              logger.warning(
                  f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
                  " padding.."       )

         if self.args.label_smoothing == 0:
              lowerCAmelCase__          =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
         else:
              # dynamically import label_smoothed_nll_loss
              from utils import label_smoothed_nll_loss

              lowerCAmelCase__          =			label_smoothed_nll_loss


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Dict:
         if self.optimizer is None:
              lowerCAmelCase__          =			["bias", "LayerNorm.weight"]
              lowerCAmelCase__          =			[
                  {
                      "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                      "weight_decay": self.args.weight_decay,
                  },
                  {
                      "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                      "weight_decay": 0.0,
                  },
              ]
              lowerCAmelCase__          =			Adafactor if self.args.adafactor else AdamW
              if self.args.adafactor:
                   lowerCAmelCase__          =			Adafactor
                   lowerCAmelCase__          =			{"scale_parameter": False, "relative_step": False}
              else:
                   lowerCAmelCase__          =			AdamW
                   lowerCAmelCase__          =			{
                       "betas": (self.args.adam_betaa, self.args.adam_betaa),
                       "eps": self.args.adam_epsilon,
                   }
              lowerCAmelCase__          =			self.args.learning_rate
              if self.sharded_ddp:
                   lowerCAmelCase__          =			OSS(
                       params=SCREAMING_SNAKE_CASE__       ,     optim=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
              else:
                   lowerCAmelCase__          =			optimizer_cls(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if self.lr_scheduler is None:
              lowerCAmelCase__          =			self._get_lr_scheduler(SCREAMING_SNAKE_CASE__       )
         else:  # ignoring --lr_scheduler
              logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."       )


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	List[str]:
         lowerCAmelCase__          =			arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
              lowerCAmelCase__          =			schedule_func(self.optimizer       )
         elif self.args.lr_scheduler == "constant_w_warmup":
              lowerCAmelCase__          =			schedule_func(self.optimizer       ,     num_warmup_steps=self.args.warmup_steps       )
         else:
              lowerCAmelCase__          =			schedule_func(
                  self.optimizer       ,     num_warmup_steps=self.args.warmup_steps       ,     num_training_steps=SCREAMING_SNAKE_CASE__       )
         return scheduler


    def        a				(	self					:					Tuple       )				->	Optional[torch.utils.data.Sampler]:
         if isinstance(self.train_dataset       ,     torch.utils.data.IterableDataset       ):
              return None
         elif is_torch_tpu_available():
              return get_tpu_sampler(self.train_dataset       )
         else:
              if self.args.sortish_sampler:
                   self.train_dataset.make_sortish_sampler(
                       self.args.per_device_train_batch_size       ,     distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,     )

              return (
                  RandomSampler(self.train_dataset       )
                  if self.args.local_rank == -1
                  else DistributedSampler(self.train_dataset       )
              )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	List[Any]:
         if self.args.label_smoothing == 0:
              if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                   # force training to ignore pad token
                   lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       ,     use_cache=SCREAMING_SNAKE_CASE__       )[0]
                   lowerCAmelCase__          =			self.loss_fn(logits.view(-1       ,     logits.shape[-1]       )       ,     labels.view(-1       )       )
              else:
                   # compute usual loss via models
                   lowerCAmelCase__     ,		lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       ,     labels=SCREAMING_SNAKE_CASE__       ,     use_cache=SCREAMING_SNAKE_CASE__       )[:2]
         else:
              # compute label smoothed loss
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       ,     use_cache=SCREAMING_SNAKE_CASE__       )[0]
              lowerCAmelCase__          =			torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE__       ,     dim=-1       )
              lowerCAmelCase__     ,		lowerCAmelCase__          =			self.loss_fn(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     self.args.label_smoothing       ,     ignore_index=self.config.pad_token_id       )
         return loss, logits


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Union[str, Any]:
         lowerCAmelCase__          =			inputs.pop("labels"       )
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self._compute_loss(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         return loss


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					nn.Module       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, Union[torch.Tensor, Any]]       ,     SCREAMING_SNAKE_CASE__					:					bool       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[str]] = None       ,     )				->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         lowerCAmelCase__          =			self._prepare_inputs(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			{
             "max_length": self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
              lowerCAmelCase__          =			self.model.generate(
                  inputs["input_ids"]       ,     attention_mask=inputs["attention_mask"]       ,     **SCREAMING_SNAKE_CASE__       ,     )
              # in case the batch is shorter than max length, the output should be padded
              if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   lowerCAmelCase__          =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__       ,     gen_kwargs["max_length"]       )

         lowerCAmelCase__          =			inputs.pop("labels"       )
         with torch.no_grad():
              # compute loss on predict data
              lowerCAmelCase__     ,		lowerCAmelCase__          =			self._compute_loss(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			loss.mean().detach()
         if self.args.prediction_loss_only:
              return (loss, None, None)

         lowerCAmelCase__          =			generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
              lowerCAmelCase__          =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__       ,     gen_kwargs["max_length"]       )

         return (loss, logits, labels)


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Union[str, Any]:
         # If PAD token is not defined at least EOS token has to be defined
         lowerCAmelCase__          =			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
              raise ValueError(
                  "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
                  f' padded to `max_length`={max_length}'       )

         lowerCAmelCase__          =			pad_token_id * torch.ones(
             (tensor.shape[0], max_length)       ,     dtype=tensor.dtype       ,     device=tensor.device       )
         lowerCAmelCase__          =			tensor
         return padded_tensor






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         for model_result in results.values():
              for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
                   lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Cant do half precision"       )
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Can't do half precision"       )
    def        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     save_to_csv=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     log_filename=os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ,     log_print=SCREAMING_SNAKE_CASE__       ,     trace_memory_line_by_line=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			benchmark.run()
              _check_summary_is_not_empty(result.inference_summary       )
              _check_summary_is_not_empty(result.train_summary       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )
