



"""simple docstring"""
from typing import Any



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			data
    UpperCAmelCase			:      str			=			None




   def __repr__(     self		)						->   str:





    '''simple docstring'''




    return F"Node({self.data})"



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			None

   def __iter__(     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			self.head
    while node:
     yield node.data
     UpperCAmelCase			:      Optional[Any]			=			node.next

   def __len__(     self		)						->   int:





    '''simple docstring'''




    return sum(1 for _ in self		)

   def __repr__(     self		)						->   str:





    '''simple docstring'''




    return "->".join([str(_SCREAMING_SNAKE_CASE		) for item in self]		)

   def __getitem__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    if not 0 <= index < len(self		):
     raise ValueError("""list index out of range."""		)
    for i, node in enumerate(self		):
     if i == index:
      return node
    return None

   def __setitem__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    if not 0 <= index < len(self		):
     raise ValueError("""list index out of range."""		)
    UpperCAmelCase			:      int			=			self.head
    for _ in range(_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Optional[int]			=			current.next
    UpperCAmelCase			:      List[Any]			=			data

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    self.insert_nth(len(self		)   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    self.insert_nth(0   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    if not 0 <= index <= len(self		):
     raise IndexError("""list index out of range"""		)
    UpperCAmelCase			:      Tuple			=			Node(_SCREAMING_SNAKE_CASE		)
    if self.head is None:
     UpperCAmelCase			:      List[str]			=			new_node
    elif index == 0:
     UpperCAmelCase			:      Dict			=			self.head  # link new_node to head
     UpperCAmelCase			:      Any			=			new_node
    else:
     UpperCAmelCase			:      List[Any]			=			self.head
     for _ in range(index - 1		):
      UpperCAmelCase			:      Union[str, Any]			=			temp.next
     UpperCAmelCase			:      int			=			temp.next
     UpperCAmelCase			:      List[Any]			=			new_node

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   None:  # print every node data





    '''simple docstring'''




    print(self		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    return self.delete_nth(0		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:  # delete from tail





    '''simple docstring'''




    return self.delete_nth(len(self		) - 1		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = 0		)						->   Any:





    '''simple docstring'''




    if not 0 <= index <= len(self		) - 1:  # test if index is valid
     raise IndexError("""List index out of range."""		)
    UpperCAmelCase			:      Dict			=			self.head  # default first node
    if index == 0:
     UpperCAmelCase			:      Any			=			self.head.next
    else:
     UpperCAmelCase			:      Dict			=			self.head
     for _ in range(index - 1		):
      UpperCAmelCase			:      List[str]			=			temp.next
     UpperCAmelCase			:      Tuple			=			temp.next
     UpperCAmelCase			:      List[Any]			=			temp.next.next
    return delete_node.data

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   bool:





    '''simple docstring'''




    return self.head is None




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			None
    UpperCAmelCase			:      str			=			self.head

    while current:
     # Store the current node's next node.
     UpperCAmelCase			:      Tuple			=			current.next
     # Make the current node's next point backwards
     UpperCAmelCase			:      Any			=			prev
     # Make the previous node be the current node
     UpperCAmelCase			:      Tuple			=			current
     # Make the current node the next node (to progress iteration)
     UpperCAmelCase			:      Any			=			next_node
    # Return prev in order to put the head at the end
    UpperCAmelCase			:      Any			=			prev

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			LinkedList()
 assert linked_list.is_empty() is True
 assert str(UpperCamelCase		) == ""

 try:
  linked_list.delete_head()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 try:
  linked_list.delete_tail()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 for i in range(10		):
  assert len(UpperCamelCase		) == i
  linked_list.insert_nth(UpperCamelCase    ,	i + 1		)
 assert str(UpperCamelCase		) == "->".join(str(UpperCamelCase		) for i in range(1    ,	11		)		)

 linked_list.insert_head(0		)
 linked_list.insert_tail(11		)
 assert str(UpperCamelCase		) == "->".join(str(UpperCamelCase		) for i in range(0    ,	12		)		)

 assert linked_list.delete_head() == 0
 assert linked_list.delete_nth(9		) == 10
 assert linked_list.delete_tail() == 11
 assert len(UpperCamelCase		) == 9
 assert str(UpperCamelCase		) == "->".join(str(UpperCamelCase		) for i in range(1    ,	10		)		)

 assert all(linked_list[i] == i + 1 for i in range(0    ,	9		)		) is True

 for i in range(0    ,	9		):
  UpperCAmelCase			:      Dict			=			-i
 assert all(linked_list[i] == -i for i in range(0    ,	9		)		) is True

 linked_list.reverse()
 assert str(UpperCamelCase		) == "->".join(str(UpperCamelCase		) for i in range(-8    ,	1		)		)

def    _snake_case		(		):
 UpperCAmelCase			:      Any			=			[
     -9,
     100,
     Node(77345112		),
     """dlrow olleH""",
     7,
     5555,
     0,
     -192.55555,
     """Hello, world!""",
     77.9,
     Node(10		),
     None,
     None,
     12.20,
 ]
 UpperCAmelCase			:      Optional[Any]			=			LinkedList()

 for i in test_input:
  linked_list.insert_tail(UpperCamelCase		)

 # Check if it's empty or not
 assert linked_list.is_empty() is False
 assert (
     str(UpperCamelCase		) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
     "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
 )

 # Delete the head
 UpperCAmelCase			:      Any			=			linked_list.delete_head()
 assert result == -9
 assert (
     str(UpperCamelCase		) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None->None->12.2"
 )

 # Delete the tail
 UpperCAmelCase			:      List[Any]			=			linked_list.delete_tail()
 assert result == 12.2
 assert (
     str(UpperCamelCase		) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None->None"
 )

 # Delete a node in specific location in linked list
 UpperCAmelCase			:      Tuple			=			linked_list.delete_nth(10		)
 assert result is None
 assert (
     str(UpperCamelCase		) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None"
 )

 # Add a Node instance to its head
 linked_list.insert_head(Node("""Hello again, world!"""		)		)
 assert (
     str(UpperCamelCase		)
     == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
     "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
 )

 # Add None to its tail
 linked_list.insert_tail(UpperCamelCase		)
 assert (
     str(UpperCamelCase		)
     == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
     "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
 )

 # Reverse the linked list
 linked_list.reverse()
 assert (
     str(UpperCamelCase		)
     == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
     "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
 )

def    _snake_case		(		):
 from doctest import testmod

 testmod()

 UpperCAmelCase			:      List[str]			=			LinkedList()
 linked_list.insert_head(input("""Inserting 1st at head """		).strip()		)
 linked_list.insert_head(input("""Inserting 2nd at head """		).strip()		)
 print("""\nPrint list:"""		)
 linked_list.print_list()
 linked_list.insert_tail(input("""\nInserting 1st at tail """		).strip()		)
 linked_list.insert_tail(input("""Inserting 2nd at tail """		).strip()		)
 print("""\nPrint list:"""		)
 linked_list.print_list()
 print("""\nDelete head"""		)
 linked_list.delete_head()
 print("""Delete tail"""		)
 linked_list.delete_tail()
 print("""\nPrint list:"""		)
 linked_list.print_list()
 print("""\nReverse linked list"""		)
 linked_list.reverse()
 print("""\nPrint list:"""		)
 linked_list.print_list()
 print("""\nString representation of linked list:"""		)
 print(UpperCamelCase		)
 print("""\nReading/changing Node data using indexing:"""		)
 print(F"Element at Position 1: {linked_list[1]}"		)
 UpperCAmelCase			:      str			=			input("""Enter New Value: """		).strip()
 print("""New list:"""		)
 print(UpperCamelCase		)
 print(F"length of linked_list is : {len(UpperCamelCase		)}"		)


if __name__ == "__main__":
      main()





"""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    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Tuple			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			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:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sgugger/tiny-distilbert-classification"""
    UpperCAmelCase			:      Any			=			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   ,  )
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[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		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[Any]			=			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   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			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		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[int]			=			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   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      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		)						->   Optional[Any]:





    '''simple docstring'''




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





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      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		)

   @unittest.skipIf(torch_device == """cpu"""   ,  """Can't do half precision"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      int			=			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   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[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		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      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		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      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		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      List[str]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      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		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Optional[Any]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      int			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      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]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			"""sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      Any			=			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   ,  )
     UpperCAmelCase			:      Union[str, Any]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""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:
     UpperCAmelCase			:      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   ,  )
     UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[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(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)		).exists()		)
