


"""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 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:







      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(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					List[Any]   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					Dict   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					List[Any]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
      _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 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[str]   =		"""sgugger/tiny-distilbert-classification"""
      _lowerCamelCase	:					Dict   =		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	:					Dict   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
      _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 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					str   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					Dict   =		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	:					Optional[int]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
      _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)





   @unittest.skipIf(torch_device == """cpu"""      ,   """Cant do half precision""")
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					Any   =		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	:					Dict   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		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)  ->  Tuple:
      _lowerCamelCase	:					Optional[int]   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					Optional[int]   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
      # set architectures equal to `None`
      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					str   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Tuple   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      ,   configs=[config])
      _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 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Tuple   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					List[Any]   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Union[str, Any]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		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)  ->  str:
      _lowerCamelCase	:					List[str]   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					Dict   =		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	:					Optional[int]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		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:
      _lowerCamelCase	:					List[str]   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					Any   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Dict   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      ,   configs=[config])
      _lowerCamelCase	:					Optional[int]   =		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[int]:
      _lowerCamelCase	:					Any   =		"""sshleifer/tinier_bart"""
      _lowerCamelCase	:					List[Any]   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					List[str]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      ,   configs=[config])
      _lowerCamelCase	:					List[str]   =		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:
      _lowerCamelCase	:					Union[str, Any]   =		"""sshleifer/tiny-gpt2"""
      _lowerCamelCase	:					int   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Optional[int]   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      ,   configs=[config])
      _lowerCamelCase	:					List[Any]   =		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]:
      _lowerCamelCase	:					Union[str, Any]   =		"""sshleifer/tinier_bart"""
      _lowerCamelCase	:					int   =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		PyTorchBenchmarkArguments(
          models=[MODEL_ID]      ,   training=SCREAMING_SNAKE_CASE      ,   inference=SCREAMING_SNAKE_CASE      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Tuple   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE      ,   configs=[config])
      _lowerCamelCase	:					List[Any]   =		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)  ->  Any:
      _lowerCamelCase	:					Tuple   =		"""sshleifer/tiny-gpt2"""
      with tempfile.TemporaryDirectory() as tmp_dir:
         _lowerCamelCase	:					Optional[int]   =		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	:					Dict   =		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 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					List[str]   =		"""sshleifer/tiny-gpt2"""

      def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE):
         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	:					List[str]   =		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	:					Any   =		PyTorchBenchmark(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					str   =		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())





'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




