




import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


UpperCamelCase								=					logging.getLogger(__name__)





class 			__lowerCamelCase	(   a__   ):




    """simple docstring"""

    snake_case__ 							=				'token-classification'


    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	str:
         if type(_lowerCamelCase       ) == dict:
              lowerCAmelCase__          =			Namespace(**_lowerCamelCase       )
         lowerCAmelCase__          =			import_module("tasks"       )
         try:
              lowerCAmelCase__          =			getattr(_lowerCamelCase       ,     hparams.task_type       )
              lowerCAmelCase__          =			token_classification_task_clazz()
         except AttributeError:
              raise ValueError(
                  f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
                  f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'       )
         lowerCAmelCase__          =			self.token_classification_task.get_labels(hparams.labels       )
         lowerCAmelCase__          =			CrossEntropyLoss().ignore_index
         super().__init__(_lowerCamelCase       ,     len(self.labels       )       ,     self.mode       )


    def        a				(	self					:					Optional[Any]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	List[Any]:
         return self.model(**_lowerCamelCase       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Tuple:
         lowerCAmelCase__          =			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
              lowerCAmelCase__          =			(
                  batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
              )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase__          =			self(**_lowerCamelCase       )
         lowerCAmelCase__          =			outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}


    def        a				(	self					:					List[str]       )				->	Optional[int]:
         lowerCAmelCase__          =			self.hparams
         for mode in ["train", "dev", "test"]:
              lowerCAmelCase__          =			self._feature_file(_lowerCamelCase       )
              if os.path.exists(_lowerCamelCase       ) and not args.overwrite_cache:
                   logger.info("Loading features from cached file %s"       ,     _lowerCamelCase       )
                   lowerCAmelCase__          =			torch.load(_lowerCamelCase       )
              else:
                   logger.info("Creating features from dataset file at %s"       ,     args.data_dir       )
                   lowerCAmelCase__          =			self.token_classification_task.read_examples_from_file(args.data_dir       ,     _lowerCamelCase       )
                   lowerCAmelCase__          =			self.token_classification_task.convert_examples_to_features(
                       _lowerCamelCase       ,     self.labels       ,     args.max_seq_length       ,     self.tokenizer       ,     cls_token_at_end=bool(self.config.model_type in ["xlnet"]       )       ,     cls_token=self.tokenizer.cls_token       ,     cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0       ,     sep_token=self.tokenizer.sep_token       ,     sep_token_extra=_lowerCamelCase       ,     pad_on_left=bool(self.config.model_type in ["xlnet"]       )       ,     pad_token=self.tokenizer.pad_token_id       ,     pad_token_segment_id=self.tokenizer.pad_token_type_id       ,     pad_token_label_id=self.pad_token_label_id       ,     )
                   logger.info("Saving features into cached file %s"       ,     _lowerCamelCase       )
                   torch.save(_lowerCamelCase       ,     _lowerCamelCase       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict = False       )				->	DataLoader:
         lowerCAmelCase__          =			self._feature_file(_lowerCamelCase       )
         logger.info("Loading features from cached file %s"       ,     _lowerCamelCase       )
         lowerCAmelCase__          =			torch.load(_lowerCamelCase       )
         lowerCAmelCase__          =			torch.tensor([f.input_ids for f in features]       ,     dtype=torch.long       )
         lowerCAmelCase__          =			torch.tensor([f.attention_mask for f in features]       ,     dtype=torch.long       )
         if features[0].token_type_ids is not None:
              lowerCAmelCase__          =			torch.tensor([f.token_type_ids for f in features]       ,     dtype=torch.long       )
         else:
              lowerCAmelCase__          =			torch.tensor([0 for f in features]       ,     dtype=torch.long       )
              # HACK(we will not use this anymore soon)
         lowerCAmelCase__          =			torch.tensor([f.label_ids for f in features]       ,     dtype=torch.long       )
         return DataLoader(
             TensorDataset(_lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase       )       ,     batch_size=_lowerCamelCase       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         """Compute validation""" ""
         lowerCAmelCase__          =			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
              lowerCAmelCase__          =			(
                  batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
              )  # XLM and RoBERTa don"t use token_type_ids
         lowerCAmelCase__          =			self(**_lowerCamelCase       )
         lowerCAmelCase__          =			outputs[:2]
         lowerCAmelCase__          =			logits.detach().cpu().numpy()
         lowerCAmelCase__          =			inputs['''labels'''].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
         lowerCAmelCase__          =			torch.stack([x["val_loss"] for x in outputs]       ).mean()
         lowerCAmelCase__          =			np.concatenate([x["pred"] for x in outputs]       ,     axis=0       )
         lowerCAmelCase__          =			np.argmax(_lowerCamelCase       ,     axis=2       )
         lowerCAmelCase__          =			np.concatenate([x["target"] for x in outputs]       ,     axis=0       )

         lowerCAmelCase__          =			dict(enumerate(self.labels       )       )
         lowerCAmelCase__          =			[[] for _ in range(out_label_ids.shape[0]       )]
         lowerCAmelCase__          =			[[] for _ in range(out_label_ids.shape[0]       )]

         for i in range(out_label_ids.shape[0]       ):
              for j in range(out_label_ids.shape[1]       ):
                   if out_label_ids[i, j] != self.pad_token_label_id:
                        out_label_list[i].append(label_map[out_label_ids[i][j]]       )
                        preds_list[i].append(label_map[preds[i][j]]       )

         lowerCAmelCase__          =			{
             '''val_loss''': val_loss_mean,
             '''accuracy_score''': accuracy_score(_lowerCamelCase       ,     _lowerCamelCase       ),
             '''precision''': precision_score(_lowerCamelCase       ,     _lowerCamelCase       ),
             '''recall''': recall_score(_lowerCamelCase       ,     _lowerCamelCase       ),
             '''f1''': fa_score(_lowerCamelCase       ,     _lowerCamelCase       ),
         }

         lowerCAmelCase__          =			dict(results.items()       )
         lowerCAmelCase__          =			results
         return ret, preds_list, out_label_list


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Tuple:
         # when stable
         lowerCAmelCase__          =			self._eval_end(_lowerCamelCase       )
         lowerCAmelCase__          =			ret['''log''']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Tuple:
         # updating to test_epoch_end instead of deprecated test_end
         lowerCAmelCase__          =			self._eval_end(_lowerCamelCase       )

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowerCAmelCase__          =			ret['''log''']
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


    @staticmethod
    def        a				(	SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	Dict:
         # Add NER specific options
         BaseTransformer.add_model_specific_args(_lowerCamelCase       ,     _lowerCamelCase       )
         parser.add_argument(
             "--task_type"       ,     default="NER"       ,     type=_lowerCamelCase       ,     help="Task type to fine tune in training (e.g. NER, POS, etc)"       )
         parser.add_argument(
             "--max_seq_length"       ,     default=128       ,     type=_lowerCamelCase       ,     help=(
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )       ,     )

         parser.add_argument(
             "--labels"       ,     default=""       ,     type=_lowerCamelCase       ,     help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."       ,     )
         parser.add_argument(
             "--gpus"       ,     default=0       ,     type=_lowerCamelCase       ,     help="The number of GPUs allocated for this, it is by default 0 meaning none"       ,     )

         parser.add_argument(
             "--overwrite_cache"       ,     action="store_true"       ,     help="Overwrite the cached training and evaluation sets"       )

         return parser


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     UpperCamelCase								=					NERTransformer.add_model_specific_args(parser, os.getcwd())
     UpperCamelCase								=					parser.parse_args()
     UpperCamelCase								=					NERTransformer(args)
     UpperCamelCase								=					generic_train(model, args)

     if args.do_predict:
          # See https://github.com/huggingface/transformers/issues/3159
          # pl use this default format to create a checkpoint:
          # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
          # /pytorch_lightning/callbacks/model_checkpoint.py#L322
          UpperCamelCase								=					sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
          UpperCamelCase								=					model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)






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()       )
