


'''simple docstring'''







# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE_      =			Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

SCREAMING_SNAKE_CASE_      =			{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

SCREAMING_SNAKE_CASE_      =			'zero2'
SCREAMING_SNAKE_CASE_      =			'zero3'
SCREAMING_SNAKE_CASE_      =			[ZEROa, ZEROa]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   parameterized.to_safe_name("""_""".join(str(__SCREAMING_SNAKE_CASE		) for x in param.args		)		)
     return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE_      =			list(itertools.product(stages, models.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  @require_torch_multi_gpu
  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  @require_torch_multi_gpu
  @parameterized.expand(SCREAMING_SNAKE_CASE__				,							name_func=SCREAMING_SNAKE_CASE__						)
  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       self.run_and_check(
           stage=SCREAMING_SNAKE_CASE__				,							model=SCREAMING_SNAKE_CASE__				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							int = 1_0				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

       __a       =   models[model]

       __a       =   self.run_trainer(
           stage=SCREAMING_SNAKE_CASE__				,							model_name=SCREAMING_SNAKE_CASE__				,							eval_steps=SCREAMING_SNAKE_CASE__				,							num_train_epochs=1				,							distributed=SCREAMING_SNAKE_CASE__				,							fpaa=SCREAMING_SNAKE_CASE__				,							)

       self.do_checks(SCREAMING_SNAKE_CASE__						)

       return output_dir

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							int = 1_0				,							SCREAMING_SNAKE_CASE__     :							int = 1				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

       __a       =   self.get_auto_remove_tmp_dir("""./xxx"""				,							after=SCREAMING_SNAKE_CASE__						)
       __a       =   f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(SCREAMING_SNAKE_CASE__						)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

       if fpaa:
            args.extend(["""--fp16"""]						)

       # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
       # hence the separate config files
       __a       =   f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
       __a       =   [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
       __a       =   self.get_launcher(SCREAMING_SNAKE_CASE__						)

       __a       =   launcher + script + args + ds_args
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       execute_subprocess_async(SCREAMING_SNAKE_CASE__				,							env=self.get_env()						)

       return output_dir


  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Tuple=False						):





       '''simple docstring'''

       __a       =   min(2				,							get_gpu_count()						) if distributed else 1
       return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE = 100_0000		)				->					int:
     """simple docstring"""




     __a       =   1
     __a       =   1
     __a       =   {1: 1}

     for inputa in range(2				,  __SCREAMING_SNAKE_CASE		):
          __a       =   0
          __a       =   inputa

          while True:
               if number in counters:
                    counter += counters[number]
                    break
               if number % 2 == 0:
                    number //= 2
                    counter += 1
               else:
                    __a       =   (3 * number) + 1
                    counter += 1

          if inputa not in counters:
               __a       =   counter

          if counter > pre_counter:
               __a       =   inputa
               __a       =   counter
     return largest_number


if __name__ == "__main__":
 print(solution(int(input().strip())))






