





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        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   for model_result in results.values():
     for batch_size, sequence_length in zip(model_result["bs"]	,			model_result["ss"]):
       lowerCamelCase__:				str        =model_result["result"][batch_size][sequence_length]
       self.assertIsNotNone(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        ="sshleifer/tiny-gpt2"
   lowerCamelCase__:				Optional[int]        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				List[Any]        =PyTorchBenchmark(UpperCAmelCase_)
   lowerCamelCase__:				str        =benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        ="sgugger/tiny-distilbert-classification"
   lowerCamelCase__:				Optional[Any]        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			only_pretrain_model=UpperCAmelCase_	,			)
   lowerCamelCase__:				List[str]        =PyTorchBenchmark(UpperCAmelCase_)
   lowerCamelCase__:				Any        =benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        ="sshleifer/tiny-gpt2"
   lowerCamelCase__:				Union[str, Any]        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			torchscript=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				Optional[int]        =PyTorchBenchmark(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =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 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        ="sshleifer/tiny-gpt2"
   lowerCamelCase__:				Union[str, Any]        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			fpaa=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				Any        =PyTorchBenchmark(UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->List[Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        ="sshleifer/tiny-gpt2"
   lowerCamelCase__:				str        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				int        =PyTorchBenchmark(UpperCAmelCase_)
   lowerCamelCase__:				Optional[Any]        =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 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        ="sshleifer/tiny-gpt2"
   lowerCamelCase__:				Optional[int]        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			fpaa=UpperCAmelCase_	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				str        =PyTorchBenchmark(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        ="sshleifer/tinier_bart"
   lowerCamelCase__:				Tuple        =AutoConfig.from_pretrained(UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				Dict        =PyTorchBenchmark(UpperCAmelCase_	,			configs=[config])
   lowerCamelCase__:				int        =benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				str        ="sshleifer/tiny-gpt2"
   lowerCamelCase__:				Dict        =AutoConfig.from_pretrained(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				int        =PyTorchBenchmark(UpperCAmelCase_	,			configs=[config])
   lowerCamelCase__:				Optional[int]        =benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Any        ="sshleifer/tinier_bart"
   lowerCamelCase__:				Optional[Any]        =AutoConfig.from_pretrained(UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =PyTorchBenchmarkArguments(
       models=[MODEL_ID]	,			training=UpperCAmelCase_	,			inference=UpperCAmelCase_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=UpperCAmelCase_	,			)
   lowerCamelCase__:				List[Any]        =PyTorchBenchmark(UpperCAmelCase_	,			configs=[config])
   lowerCamelCase__:				Any        =benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Any        ="sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
     lowerCamelCase__:				Optional[Any]        =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__:				str        =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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        ="sshleifer/tiny-gpt2"

   def _check_summary_is_not_empty(UpperCAmelCase_    :       Any):
     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__:				Dict        =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__:				Dict        =PyTorchBenchmark(UpperCAmelCase_)
     lowerCamelCase__:				Any        =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())








def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  assert x is not None
  assert y is not None

  lowerCamelCase__:				Any        =len(__a		)
  lowerCamelCase__:				int        =len(__a		)

  # declaring the array for storing the dp values
  lowerCamelCase__:				List[Any]        =[[0] * (n + 1) for _ in range(m + 1		)]  # noqa: E741

  for i in range(1     ,   m + 1		):
    for j in range(1     ,   n + 1		):
      lowerCamelCase__:				str        =1 if x[i - 1] == y[j - 1] else 0

      lowerCamelCase__:				str        =max(l[i - 1][j]     ,   l[i][j - 1]     ,   l[i - 1][j - 1] + match		)

  lowerCamelCase__:				Any        =""
  lowerCamelCase__  , lowerCamelCase__:				str        =m, n
  while i > 0 and j > 0:
    lowerCamelCase__:				Union[str, Any]        =1 if x[i - 1] == y[j - 1] else 0

    if l[i][j] == l[i - 1][j - 1] + match:
      if match == 1:
        lowerCamelCase__:				Any        =x[i - 1] + seq
      i -= 1
      j -= 1
    elif l[i][j] == l[i - 1][j]:
      i -= 1
    else:
      j -= 1

  return l[m][n], seq


if __name__ == "__main__":
     __A     =    "AGGTAB"
     __A     =    "GXTXAYB"
     __A     =    4
     __A     =    "GTAB"

     __A ,     __A     =    longest_common_subsequence(a, b)
     print("len =", ln, ", sub-sequence =", subseq)
     import doctest

     doctest.testmod()

