




from __future__ import annotations

import math
import random
from typing import Any





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					str       )				->	None:
         lowerCAmelCase__          =			[]
         lowerCAmelCase__          =			0
         lowerCAmelCase__          =			0


    def        a				(	self					:					Any       )				->	bool:
         return self.head == self.tail


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	None:
         self.data.append(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.tail + 1


    def        a				(	self					:					int       )				->	Any:
         lowerCAmelCase__          =			self.data[self.head]
         lowerCAmelCase__          =			self.head + 1
         return ret


    def        a				(	self					:					str       )				->	int:
         return self.tail - self.head


    def        a				(	self					:					List[str]       )				->	None:
         print(self.data       )
         print("**************"       )
         print(self.data[self.head : self.tail]       )






class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	None:
         lowerCAmelCase__          =			data
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			1


    def        a				(	self					:					Union[str, Any]       )				->	Any:
         return self.data


    def        a				(	self					:					Tuple       )				->	MyNode | None:
         return self.left


    def        a				(	self					:					List[str]       )				->	MyNode | None:
         return self.right


    def        a				(	self					:					Any       )				->	int:
         return self.height


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	None:
         lowerCAmelCase__          =			data


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					MyNode | None       )				->	None:
         lowerCAmelCase__          =			node


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					MyNode | None       )				->	None:
         lowerCAmelCase__          =			node


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	None:
         lowerCAmelCase__          =			height






def 							_A       (						lowerCAmelCase_					: MyNode | None      ):
     """simple docstring"""

     if node is None:
          return 0
     return node.get_height()



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     if a > b:
          return a
     return b



def 							_A       (						lowerCAmelCase_					: MyNode      ):
     """simple docstring"""

     print("left rotation node:"       ,    node.get_data()      )
     lowerCAmelCase__          =			node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()      )
     ret.set_right(lowerCAmelCase_      )
     lowerCAmelCase__          =			my_max(get_height(node.get_right()      )       ,    get_height(node.get_left()      )      ) + 1
     node.set_height(lowerCAmelCase_      )
     lowerCAmelCase__          =			my_max(get_height(ret.get_right()      )       ,    get_height(ret.get_left()      )      ) + 1
     ret.set_height(lowerCAmelCase_      )
     return ret



def 							_A       (						lowerCAmelCase_					: MyNode      ):
     """simple docstring"""

     print("right rotation node:"       ,    node.get_data()      )
     lowerCAmelCase__          =			node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()      )
     ret.set_left(lowerCAmelCase_      )
     lowerCAmelCase__          =			my_max(get_height(node.get_right()      )       ,    get_height(node.get_left()      )      ) + 1
     node.set_height(lowerCAmelCase_      )
     lowerCAmelCase__          =			my_max(get_height(ret.get_right()      )       ,    get_height(ret.get_left()      )      ) + 1
     ret.set_height(lowerCAmelCase_      )
     return ret



def 							_A       (						lowerCAmelCase_					: MyNode      ):
     """simple docstring"""

     lowerCAmelCase__          =			node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(lowerCAmelCase_      )      )
     return right_rotation(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: MyNode      ):
     """simple docstring"""

     lowerCAmelCase__          =			node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(lowerCAmelCase_      )      )
     return left_rotation(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: MyNode | None       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     if node is None:
          return MyNode(lowerCAmelCase_      )
     if data < node.get_data():
          node.set_left(insert_node(node.get_left()       ,    lowerCAmelCase_      )      )
          if (
              get_height(node.get_left()      ) - get_height(node.get_right()      ) == 2
          ):  # an unbalance detected
               lowerCAmelCase__          =			node.get_left()
               assert left_child is not None
               if (
                   data < left_child.get_data()
               ):  # new node is the left child of the left child
                    lowerCAmelCase__          =			right_rotation(lowerCAmelCase_      )
               else:
                    lowerCAmelCase__          =			lr_rotation(lowerCAmelCase_      )
     else:
          node.set_right(insert_node(node.get_right()       ,    lowerCAmelCase_      )      )
          if get_height(node.get_right()      ) - get_height(node.get_left()      ) == 2:
               lowerCAmelCase__          =			node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    lowerCAmelCase__          =			rl_rotation(lowerCAmelCase_      )
               else:
                    lowerCAmelCase__          =			left_rotation(lowerCAmelCase_      )
     lowerCAmelCase__          =			my_max(get_height(node.get_right()      )       ,    get_height(node.get_left()      )      ) + 1
     node.set_height(lowerCAmelCase_      )
     return node



def 							_A       (						lowerCAmelCase_					: MyNode      ):
     """simple docstring"""

     while True:
          lowerCAmelCase__          =			root.get_right()
          if right_child is None:
               break
          lowerCAmelCase__          =			right_child
     return root.get_data()



def 							_A       (						lowerCAmelCase_					: MyNode      ):
     """simple docstring"""

     while True:
          lowerCAmelCase__          =			root.get_left()
          if left_child is None:
               break
          lowerCAmelCase__          =			left_child
     return root.get_data()



def 							_A       (						lowerCAmelCase_					: MyNode       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			root.get_left()
     lowerCAmelCase__          =			root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               lowerCAmelCase__          =			get_left_most(lowerCAmelCase_      )
               root.set_data(lowerCAmelCase_      )
               root.set_right(del_node(lowerCAmelCase_       ,    lowerCAmelCase_      )      )
          elif left_child is not None:
               lowerCAmelCase__          =			left_child
          elif right_child is not None:
               lowerCAmelCase__          =			right_child
          else:
               return None
     elif root.get_data() > data:
          if left_child is None:
               print("No such data"      )
               return root
          else:
               root.set_left(del_node(lowerCAmelCase_       ,    lowerCAmelCase_      )      )
     else:  # root.get_data() < data
          if right_child is None:
               return root
          else:
               root.set_right(del_node(lowerCAmelCase_       ,    lowerCAmelCase_      )      )

     if get_height(lowerCAmelCase_      ) - get_height(lowerCAmelCase_      ) == 2:
          assert right_child is not None
          if get_height(right_child.get_right()      ) > get_height(right_child.get_left()      ):
               lowerCAmelCase__          =			left_rotation(lowerCAmelCase_      )
          else:
               lowerCAmelCase__          =			rl_rotation(lowerCAmelCase_      )
     elif get_height(lowerCAmelCase_      ) - get_height(lowerCAmelCase_      ) == -2:
          assert left_child is not None
          if get_height(left_child.get_left()      ) > get_height(left_child.get_right()      ):
               lowerCAmelCase__          =			right_rotation(lowerCAmelCase_      )
          else:
               lowerCAmelCase__          =			lr_rotation(lowerCAmelCase_      )
     lowerCAmelCase__          =			my_max(get_height(root.get_right()      )       ,    get_height(root.get_left()      )      ) + 1
     root.set_height(lowerCAmelCase_      )
     return root





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					Any       )				->	None:
         lowerCAmelCase__          =			None


    def        a				(	self					:					Dict       )				->	int:
         return get_height(self.root       )


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	None:
         print("insert:" + str(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			insert_node(self.root       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	None:
         print("delete:" + str(SCREAMING_SNAKE_CASE__       )       )
         if self.root is None:
              print("Tree is empty!"       )
              return
         lowerCAmelCase__          =			del_node(self.root       ,     SCREAMING_SNAKE_CASE__       )


    def __str__(	self					:					Tuple       ,     )				->	str:  # a level traversale, gives a more intuitive look on the tree
         lowerCAmelCase__          =			""
         lowerCAmelCase__          =			MyQueue()
         q.push(self.root       )
         lowerCAmelCase__          =			self.get_height()
         if layer == 0:
              return output
         lowerCAmelCase__          =			0
         while not q.is_empty():
              lowerCAmelCase__          =			q.pop()
              lowerCAmelCase__          =			" " * int(math.pow(2       ,     layer - 1       )       )
              output += space
              if node is None:
                   output += "*"
                   q.push(SCREAMING_SNAKE_CASE__       )
                   q.push(SCREAMING_SNAKE_CASE__       )
              else:
                   output += str(node.get_data()       )
                   q.push(node.get_left()       )
                   q.push(node.get_right()       )
              output += space
              lowerCAmelCase__          =			cnt + 1
              for i in range(100       ):
                   if cnt == math.pow(2       ,     SCREAMING_SNAKE_CASE__       ) - 1:
                        lowerCAmelCase__          =			layer - 1
                        if layer == 0:
                             output += "\n*************************************"
                             return output
                        output += "\n"
                        break
         output += "\n*************************************"
         return output






def 							_A       (						):
     """simple docstring"""

     import doctest

     doctest.testmod()


if __name__ == "__main__":
     _test()
     UpperCamelCase								=					AVLtree()
     UpperCamelCase								=					list(range(10))
     random.shuffle(lst)
     for i in lst:
          t.insert(i)
          print(str(t))
     random.shuffle(lst)
     for i in lst:
          t.del_node(i)
          print(str(t))






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 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         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(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			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__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			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__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         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        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			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__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         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        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			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__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     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        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			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__          =			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        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              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__          =			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__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              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(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )
