


"""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      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
      for model_result in results.values():
        for batch_size, sequence_length in zip(model_result["bs"]    ,			model_result["ss"]):
          a						:		Optional[int]											=      model_result["result"][batch_size][sequence_length]
          self.assertIsNotNone(_lowerCamelCase)






    def      __snake_case							( self    :   Optional[int]):
      a						:		int											=      "sshleifer/tiny-gpt2"
      a						:		List[Any]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		int											=      PyTorchBenchmark(_lowerCamelCase)
      a						:		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      __snake_case							( self    :   int):
      a						:		List[str]											=      "sgugger/tiny-distilbert-classification"
      a						:		List[str]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			only_pretrain_model=_lowerCamelCase    ,			)
      a						:		List[Any]											=      PyTorchBenchmark(_lowerCamelCase)
      a						:		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      __snake_case							( self    :   Any):
      a						:		Any											=      "sshleifer/tiny-gpt2"
      a						:		List[str]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			torchscript=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		Union[str, Any]											=      PyTorchBenchmark(_lowerCamelCase)
      a						:		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      __snake_case							( self    :   Union[str, Any]):
      a						:		Tuple											=      "sshleifer/tiny-gpt2"
      a						:		str											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			fpaa=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		Optional[int]											=      PyTorchBenchmark(_lowerCamelCase)
      a						:		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      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      "sshleifer/tiny-gpt2"
      a						:		Union[str, Any]											=      AutoConfig.from_pretrained(_lowerCamelCase)
      # set architectures equal to `None`
      a						:		int											=      None
      a						:		List[Any]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		Optional[int]											=      PyTorchBenchmark(_lowerCamelCase    ,			configs=[config])
      a						:		str											=      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def      __snake_case							( self    :   str):
      a						:		Optional[Any]											=      "sshleifer/tiny-gpt2"
      a						:		Optional[Any]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		Union[str, Any]											=      PyTorchBenchmark(_lowerCamelCase)
      a						:		str											=      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      __snake_case							( self    :   int):
      a						:		Tuple											=      "sshleifer/tiny-gpt2"
      a						:		Any											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			fpaa=_lowerCamelCase    ,			multi_process=_lowerCamelCase    ,			)
      a						:		Union[str, Any]											=      PyTorchBenchmark(_lowerCamelCase)
      a						:		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      __snake_case							( self    :   List[str]):
      a						:		Dict											=      "sshleifer/tiny-gpt2"
      a						:		Optional[int]											=      AutoConfig.from_pretrained(_lowerCamelCase)
      a						:		int											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		List[str]											=      PyTorchBenchmark(_lowerCamelCase    ,			configs=[config])
      a						:		int											=      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def      __snake_case							( self    :   List[str]):
      a						:		Any											=      "sshleifer/tinier_bart"
      a						:		List[Any]											=      AutoConfig.from_pretrained(_lowerCamelCase)
      a						:		Optional[int]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		List[Any]											=      PyTorchBenchmark(_lowerCamelCase    ,			configs=[config])
      a						:		str											=      benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def      __snake_case							( self    :   Dict):
      a						:		Union[str, Any]											=      "sshleifer/tiny-gpt2"
      a						:		List[str]											=      AutoConfig.from_pretrained(_lowerCamelCase)
      a						:		List[Any]											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		List[str]											=      PyTorchBenchmark(_lowerCamelCase    ,			configs=[config])
      a						:		Any											=      benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result)
      self.check_results_dict_not_empty(results.memory_train_result)






    def      __snake_case							( self    :   Optional[Any]):
      a						:		str											=      "sshleifer/tinier_bart"
      a						:		int											=      AutoConfig.from_pretrained(_lowerCamelCase)
      a						:		Any											=      PyTorchBenchmarkArguments(
          models=[MODEL_ID]    ,			training=_lowerCamelCase    ,			inference=_lowerCamelCase    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=_lowerCamelCase    ,			)
      a						:		Any											=      PyTorchBenchmark(_lowerCamelCase    ,			configs=[config])
      a						:		str											=      benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result)
      self.check_results_dict_not_empty(results.memory_train_result)






    def      __snake_case							( self    :   Optional[Any]):
      a						:		int											=      "sshleifer/tiny-gpt2"
      with tempfile.TemporaryDirectory() as tmp_dir:
        a						:		Optional[Any]											=      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    ,			)
        a						:		Any											=      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      __snake_case							( self    :   Tuple):
      a						:		List[Any]											=      "sshleifer/tiny-gpt2"

      def _check_summary_is_not_empty(__UpperCAmelCase    :   Union[str, Any]):
        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:
        a						:		int											=      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    ,			)
        a						:		Optional[Any]											=      PyTorchBenchmark(_lowerCamelCase)
        a						:		Dict											=      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"""







import sys
import turtle


def 					lowercase      (    A_  ,       A_	)->				tuple[float, float]:
  '''simple docstring'''







  return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       )->				None:
  '''simple docstring'''







  my_pen.up()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.down()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)

  if depth == 0:
    return

  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise ValueError(
      """Correct format for using this script: """
      """python fractals.py <int:depth_for_fractal>"""
  )
 __lowercase    =				turtle.Turtle()
 my_pen.ht()
 my_pen.speed(5)
 my_pen.pencolor("""red""")

 __lowercase    =				[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
 triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






