


'''simple docstring'''




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 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      str    )				->  Any:
        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(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Union[str, Any]:
        lowerCAmelCase     =			'sgugger/tiny-distilbert-classification'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      only_pretrain_model=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      torchscript=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        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 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      fpaa=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  List[str]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        # set architectures equal to `None`
        lowerCAmelCase     =			None
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      List[Any]    )				->  str:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        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 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      fpaa=UpperCAmelCase__    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result    )
        self.check_results_dict_not_empty(results.memory_train_result    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      Dict    )				->  str:
        lowerCAmelCase     =			'sshleifer/tinier_bart'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        lowerCAmelCase     =			'sshleifer/tinier_bart'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
           lowerCAmelCase     =			PyTorchBenchmarkArguments(
               models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      save_to_csv=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      inference_time_csv_file=os.path.join(UpperCAmelCase__    ,      'inf_time.csv'    )    ,      train_memory_csv_file=os.path.join(UpperCAmelCase__    ,      'train_mem.csv'    )    ,      inference_memory_csv_file=os.path.join(UpperCAmelCase__    ,      'inf_mem.csv'    )    ,      train_time_csv_file=os.path.join(UpperCAmelCase__    ,      'train_time.csv'    )    ,      env_info_csv_file=os.path.join(UpperCAmelCase__    ,      'env.csv'    )    ,      multi_process=UpperCAmelCase__    ,      )
           lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
           benchmark.run()
           self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,      'inf_time.csv'    )    ).exists()    )
           self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,      'train_time.csv'    )    ).exists()    )
           self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,      'inf_mem.csv'    )    ).exists()    )
           self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,      'train_mem.csv'    )    ).exists()    )
           self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,      'env.csv'    )    ).exists()    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(UpperCAmelCase__       :      str    ):
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'sequential'    )    )
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'cumulative'    )    )
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'current'    )    )
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'total'    )    )

        with tempfile.TemporaryDirectory() as tmp_dir:
           lowerCAmelCase     =			PyTorchBenchmarkArguments(
               models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      log_filename=os.path.join(UpperCAmelCase__    ,      'log.txt'    )    ,      log_print=UpperCAmelCase__    ,      trace_memory_line_by_line=UpperCAmelCase__    ,      multi_process=UpperCAmelCase__    ,      )
           lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
           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(UpperCAmelCase__    ,      'log.txt'    )    ).exists()    )









'''simple docstring'''




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))





