






"""simple docstring"""


from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Dict,       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     __A									= data
     __A									= None





class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     str  ):

     '''simple docstring'''
     __A									= None
     __A									= None


   def __iter__(	self :     Any  ):

     '''simple docstring'''
     __A									= self.head
     while self.head:
       yield node.data
       __A									= node.next
       if node == self.head:
         break


   def __len__(	self :     int  ):

     '''simple docstring'''
     return sum(1 for _ in self  )


   def __repr__(	self :     str  ):

     '''simple docstring'''
     return "->".join(str(_lowerCamelCase  ) for item in iter(self  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     self.insert_nth(len(self  ),       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     self.insert_nth(0,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     if index < 0 or index > len(self  ):
       raise IndexError('''list index out of range.'''  )
     __A									= Node(_lowerCamelCase  )
     if self.head is None:
       __A									= new_node  # first node points itself
       __A									= __A									= new_node
     elif index == 0:  # insert at head
       __A									= self.head
       __A									= __A									= new_node
     else:
       __A									= self.head
       for _ in range(index - 1  ):
         __A									= temp.next
       __A									= temp.next
       __A									= new_node
       if index == len(self  ) - 1:  # insert at tail
         __A									= new_node


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return self.delete_nth(0  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     return self.delete_nth(len(self  ) - 1  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int = 0  ):

     '''simple docstring'''
     if not 0 <= index < len(self  ):
       raise IndexError('''list index out of range.'''  )
     __A									= self.head
     if self.head == self.tail:  # just one node
       __A									= __A									= None
     elif index == 0:  # delete head node
       __A									= self.tail.next.next
       __A									= self.head.next
     else:
       __A									= self.head
       for _ in range(index - 1  ):
         __A									= temp.next
       __A									= temp.next
       __A									= temp.next.next
       if index == len(self  ) - 1:  # delete at tail
         __A									= temp
     return delete_node.data






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     return len(self  ) == 0





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= CircularLinkedList()
  assert len(__UpperCamelCase ) == 0
  assert circular_linked_list.is_empty() is True
  assert str(__UpperCamelCase ) == ""

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

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

  try:
    circular_linked_list.delete_nth(-1 )
    raise AssertionError
  except IndexError:
    assert True

  try:
    circular_linked_list.delete_nth(0 )
    raise AssertionError
  except IndexError:
    assert True

  assert circular_linked_list.is_empty() is True
  for i in range(5 ):
    assert len(__UpperCamelCase ) == i
    circular_linked_list.insert_nth(__UpperCamelCase		,       i + 1 )
  assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1		,       6 ) )

  circular_linked_list.insert_tail(6 )
  assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1		,       7 ) )
  circular_linked_list.insert_head(0 )
  assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(0		,       7 ) )

  assert circular_linked_list.delete_front() == 0
  assert circular_linked_list.delete_tail() == 6
  assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1		,       6 ) )
  assert circular_linked_list.delete_nth(2 ) == 3

  circular_linked_list.insert_nth(2		,       3 )
  assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1		,       6 ) )

  assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
       import doctest

       doctest.testmod()












"""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 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     for model_result in results.values():
       for batch_size, sequence_length in zip(model_result['''bs'''],       model_result['''ss''']  ):
         __A									= model_result['''result'''][batch_size][sequence_length]
         self.assertIsNotNone(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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'''
     __A									= '''sgugger/tiny-distilbert-classification'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       only_pretrain_model=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       torchscript=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       fpaa=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     # set architectures equal to `None`
     __A									= None
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= 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 :     int  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 :     Optional[int]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       fpaa=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 :     str  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= 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'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= 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'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= 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'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= 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]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= 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									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase :     List[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									= 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									= PyTorchBenchmark(_lowerCamelCase  )
       __A									= 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()  )




