



"""simple docstring"""
from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
A:     int										=	1_0

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	list[int]    ,	UpperCamelCase		:	int		):
 for i in range(UpperCamelCase    ,	UpperCamelCase		):
  if array[i] == target:
   return i
 return -1

def    _snake_case		(		UpperCamelCase		:	list[int]    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      int			=			0
 UpperCAmelCase			:      Optional[int]			=			len(UpperCamelCase		)
 while left <= right:
  if right - left < precision:
   return lin_search(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

  UpperCAmelCase			:      int			=			(left + right) // 3 + 1
  UpperCAmelCase			:      Optional[Any]			=			2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   UpperCAmelCase			:      str			=			one_third - 1
  elif array[two_third] < target:
   UpperCAmelCase			:      int			=			two_third + 1

  else:
   UpperCAmelCase			:      str			=			one_third + 1
   UpperCAmelCase			:      List[str]			=			two_third - 1
 else:
  return -1

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	list[int]    ,	UpperCamelCase		:	int		):
 if left < right:
  if right - left < precision:
   return lin_search(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)
  UpperCAmelCase			:      Union[str, Any]			=			(left + right) // 3 + 1
  UpperCAmelCase			:      List[str]			=			2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(UpperCamelCase    ,	one_third - 1    ,	UpperCamelCase    ,	UpperCamelCase		)
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)
  else:
   return rec_ternary_search(one_third + 1    ,	two_third - 1    ,	UpperCamelCase    ,	UpperCamelCase		)
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      A:     Dict										=	input("Enter numbers separated by comma:\n").strip()
      A:     Dict										=	[int(item.strip()) for item in user_input.split(",")]
      assert collection == sorted(collection), f"List must be ordered.\n{collection}."
      A:     Any										=	int(input("Enter the number to be found in the list:\n").strip())
      A:     List[Any]										=	ite_ternary_search(collection, target)
      A:     Dict										=	rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(f"""Iterative search: {target} found at positions: {resulta}""")
            print(f"""Recursive search: {target} found at positions: {resulta}""")
      else:
            print("Not found")





"""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()		)
