





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       ):

  """simple docstring"""


  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Dict	)				->		str:



     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(lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[str]	)				->		str:
     __lowerCAmelCase				      =      'sshleifer/tiny-gpt2'
     __lowerCAmelCase				      =      PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,    training=lowerCAmelCase_   ,    inference=lowerCAmelCase_   ,    sequence_lengths=[8]   ,    batch_sizes=[1]   ,    multi_process=lowerCAmelCase_   ,    )
     __lowerCAmelCase				      =      PyTorchBenchmark(lowerCAmelCase_	)
     __lowerCAmelCase				      =      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result	)
     self.check_results_dict_not_empty(results.memory_inference_result	)






  def 							lowercase						(							self					:						Tuple	)				->		Dict:
     __lowerCAmelCase				      =      'sgugger/tiny-distilbert-classification'
     __lowerCAmelCase				      =      PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,    training=lowerCAmelCase_   ,    inference=lowerCAmelCase_   ,    sequence_lengths=[8]   ,    batch_sizes=[1]   ,    multi_process=lowerCAmelCase_   ,    only_pretrain_model=lowerCAmelCase_   ,    )
     __lowerCAmelCase				      =      PyTorchBenchmark(lowerCAmelCase_	)
     __lowerCAmelCase				      =      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result	)
     self.check_results_dict_not_empty(results.memory_inference_result	)






  def 							lowercase						(							self					:						Any	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      'sshleifer/tiny-gpt2'
     __lowerCAmelCase				      =      PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,    training=lowerCAmelCase_   ,    inference=lowerCAmelCase_   ,    torchscript=lowerCAmelCase_   ,    sequence_lengths=[8]   ,    batch_sizes=[1]   ,    multi_process=lowerCAmelCase_   ,    )
     __lowerCAmelCase				      =      PyTorchBenchmark(lowerCAmelCase_	)
     __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 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      'sshleifer/tiny-gpt2'
     __lowerCAmelCase				      =      PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,    training=lowerCAmelCase_   ,    inference=lowerCAmelCase_   ,    fpaa=lowerCAmelCase_   ,    sequence_lengths=[8]   ,    batch_sizes=[1]   ,    multi_process=lowerCAmelCase_   ,    )
     __lowerCAmelCase				      =      PyTorchBenchmark(lowerCAmelCase_	)
     __lowerCAmelCase				      =      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result	)
     self.check_results_dict_not_empty(results.memory_inference_result	)






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






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






  def 							lowercase						(							self					:						Dict	)				->		str:
     __lowerCAmelCase				      =      'sshleifer/tiny-gpt2'

     def _check_summary_is_not_empty(lowerCAmelCase_					:						int	):
        self.assertTrue(hasattr(lowerCAmelCase_   ,    'sequential'	)	)
        self.assertTrue(hasattr(lowerCAmelCase_   ,    'cumulative'	)	)
        self.assertTrue(hasattr(lowerCAmelCase_   ,    'current'	)	)
        self.assertTrue(hasattr(lowerCAmelCase_   ,    'total'	)	)

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






'''simple docstring'''





def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if digit_amount > 0:
          return round(number - int(__A)    ,      __A)
     return number - int(__A)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))



