

'''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


lowerCAmelCase__						:       List[Any]										=						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)

lowerCAmelCase__						:       Dict										=						{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

lowerCAmelCase__						:       Dict										=						"""zero2"""
lowerCAmelCase__						:       Any										=						"""zero3"""
lowerCAmelCase__						:       Optional[Any]										=						[ZEROa, ZEROa]




def 	_a       (				__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       int       						=  parameterized.to_safe_name('''_'''.join(str(__lowerCAmelCase     ) for x in param.args     )     )
		return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
lowerCAmelCase__						:       List[Any]										=						list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@parameterized.expand(snake_case_			,			name_func=snake_case_  )
						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



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







						@require_torch_multi_gpu
						@parameterized.expand(snake_case_			,			name_func=snake_case_  )
						def       __magic_name__							(							self					:      str			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



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







						@parameterized.expand(snake_case_			,			name_func=snake_case_  )
						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



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







						@require_torch_multi_gpu
						@parameterized.expand(snake_case_			,			name_func=snake_case_  )
						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Dict			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								pass







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



								'''simple docstring'''



								snake_case__						:       Tuple       						=  models[model]

								snake_case__						:       Any       						=  self.run_trainer(
								    stage=snake_case_			,			model_name=snake_case_			,			eval_steps=snake_case_			,			num_train_epochs=1			,			distributed=snake_case_			,			fpaa=snake_case_			,			)

								self.do_checks(snake_case_  )

								return output_dir







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      str			,			snake_case_					:      int = 1_0			,			snake_case_					:      int = 1			,			snake_case_					:      bool = True			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  self.get_auto_remove_tmp_dir('''./xxx'''			,			after=snake_case_  )
								snake_case__						:       List[Any]       						=  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(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
								snake_case__						:       Optional[Any]       						=  F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
								snake_case__						:       Dict       						=  [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
								snake_case__						:       List[Any]       						=  self.get_launcher(snake_case_  )

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

								return output_dir







						def       __magic_name__							(							self					:      int			,			snake_case_					:      str=False  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  min(2			,			get_gpu_count()  ) if distributed else 1
								return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()






'''simple docstring'''


from collections.abc import Generator




def 	_a       (				):

		"""simple docstring"""

		snake_case__						,       snake_case__						:       List[Any]       						=  0, 1
		while True:
				snake_case__						,       snake_case__						:       str       						=  b, a + b
				yield b




def 	_a       (				__lowerCAmelCase		:      int = 10_00     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  1
		snake_case__						:       Tuple       						=  fibonacci_generator()
		while len(str(next(__lowerCAmelCase     )     )     ) < n:
				answer += 1
		return answer + 1


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



