



'''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 __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      for model_result in results.values():
           for batch_size, sequence_length in zip(model_result['''bs''']					,  model_result['''ss''']			):
                _a           =    model_result['''result'''][batch_size][sequence_length]
                self.assertIsNotNone(A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    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			)       ->	Dict:


      """simple docstring"""



      _a           =    '''sgugger/tiny-distilbert-classification'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  only_pretrain_model=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    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]:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  torchscript=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    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			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  fpaa=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    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:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    AutoConfig.from_pretrained(A			)
      # set architectures equal to `None`
      _a           =    None
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  configs=[config]			)
      _a           =    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			)       ->	str:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    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			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  fpaa=A					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    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			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  configs=[config]			)
      _a           =    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]:


      """simple docstring"""



      _a           =    '''sshleifer/tinier_bart'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  configs=[config]			)
      _a           =    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:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  configs=[config]			)
      _a           =    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			)       ->	Dict:


      """simple docstring"""



      _a           =    '''sshleifer/tinier_bart'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  configs=[config]			)
      _a           =    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			)       ->	int:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a           =    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,  training=A					,  inference=A					,  save_to_csv=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  inference_time_csv_file=os.path.join(A					,  '''inf_time.csv'''			)					,  train_memory_csv_file=os.path.join(A					,  '''train_mem.csv'''			)					,  inference_memory_csv_file=os.path.join(A					,  '''inf_mem.csv'''			)					,  train_time_csv_file=os.path.join(A					,  '''train_time.csv'''			)					,  env_info_csv_file=os.path.join(A					,  '''env.csv'''			)					,  multi_process=A					,  )
           _a           =    PyTorchBenchmark(A			)
           benchmark.run()
           self.assertTrue(Path(os.path.join(A					,  '''inf_time.csv'''			)			).exists()			)
           self.assertTrue(Path(os.path.join(A					,  '''train_time.csv'''			)			).exists()			)
           self.assertTrue(Path(os.path.join(A					,  '''inf_mem.csv'''			)			).exists()			)
           self.assertTrue(Path(os.path.join(A					,  '''train_mem.csv'''			)			).exists()			)
           self.assertTrue(Path(os.path.join(A					,  '''env.csv'''			)			).exists()			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''

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

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








'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    credit_card_number
     _a           =    0
     _a           =    len(__A) - 2
     for i in range(__A    ,      -1    ,      -2):
          #  double the value of every second digit
          _a           =    int(cc_number[i])
          digit *= 2
          # If doubling of a number results in a two digit number
          # i.e greater than 9(e.g., 6 × 2 = 12),
          # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
          # to get a single digit number.
          if digit > 9:
               digit %= 10
               digit += 1
          _a           =    cc_number[:i] + str(__A) + cc_number[i + 1 :]
          total += digit

     # Sum up the remaining digits
     for i in range(len(__A) - 1    ,      -1    ,      -2):
          total += int(cc_number[i])

     return total % 10 == 0




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    F'''{credit_card_number} is an invalid credit card number because'''
     if not credit_card_number.isdigit():
          print(F'''{error_message} it has nonnumerical characters.''')
          return False

     if not 13 <= len(__A) <= 16:
          print(F'''{error_message} of its length.''')
          return False

     if not validate_initial_digits(__A):
          print(F'''{error_message} of its first two digits.''')
          return False

     if not luhn_validation(__A):
          print(F'''{error_message} it fails the Luhn check.''')
          return False

     print(F'''{credit_card_number} is a valid credit card number.''')
     return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  validate_credit_card_number("4111111111111111")
  validate_credit_card_number("32323")



