


'''simple docstring'''





import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__lowerCamelCase												=						os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
    from run_translation import main  # noqa


set_seed(42)
__lowerCamelCase												=						'''sshleifer/student_marian_en_ro_6_1'''
__lowerCamelCase												=						'''sshleifer/tiny-mbart'''




@require_torch
class 				UpperCAmelCase			(			_snake_case						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	Dict=False			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	List[str]=True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	List[Any]=True			,       __lowerCamelCase							:	int=True			,       ):
    UpperCAmelCase__	:Tuple      =      self.run_trainer(
        eval_steps=1			,       max_len=1_2			,       model_name=__lowerCamelCase			,       num_train_epochs=1			,       distributed=__lowerCamelCase			,       extra_args_str=__lowerCamelCase			,       predict_with_generate=__lowerCamelCase			,       do_train=__lowerCamelCase			,       do_eval=__lowerCamelCase			,       do_predict=__lowerCamelCase			,       )
    UpperCAmelCase__	:int      =      TrainerState.load_from_json(os.path.join(__lowerCamelCase			,       '''trainer_state.json''' ) ).log_history

    if not do_eval:
       return

    UpperCAmelCase__	:Union[str, Any]      =      [log for log in logs if '''eval_loss''' in log.keys()]

    UpperCAmelCase__	:Optional[int]      =      eval_metrics[0]
    if predict_with_generate:
       assert "eval_bleu" in first_step_stats

       UpperCAmelCase__	:Any      =      eval_metrics[-1]
       assert isinstance(last_step_stats['''eval_bleu''']			,       __lowerCamelCase )
       assert not math.isnan(float(last_step_stats['''eval_loss'''] ) ), "eval_loss must not be `nan`"


 @require_torch_non_multi_gpu
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    self.run_seqaseq_quick()


 @require_torch_multi_gpu
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    self.run_seqaseq_quick(distributed=__lowerCamelCase )


 @require_torch_multi_gpu
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    self.run_seqaseq_quick(distributed=__lowerCamelCase )


 @unittest.skip('''Requires an update of the env running those tests''' )
 @require_torch_multi_gpu
 @require_fairscale
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    self.run_seqaseq_quick(distributed=__lowerCamelCase			,       extra_args_str='''--sharded_ddp simple''' )


 @unittest.skip('''Requires an update of the env running those tests''' )
 @require_torch_multi_gpu
 @require_fairscale
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    self.run_seqaseq_quick(distributed=__lowerCamelCase			,       extra_args_str='''--sharded_ddp simple --fp16''' )


 @unittest.skip('''Requires an update of the env running those tests''' )
 @require_torch_multi_gpu
 @require_fairscale
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    self.run_seqaseq_quick(distributed=__lowerCamelCase			,       extra_args_str='''--sharded_ddp zero_dp_2'''			,       predict_with_generate=__lowerCamelCase )


 @unittest.skip('''Requires an update of the env running those tests''' )
 @require_torch_multi_gpu
 @require_fairscale
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    self.run_seqaseq_quick(
        distributed=__lowerCamelCase			,       extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''			,       predict_with_generate=__lowerCamelCase )


 @require_apex
 @require_torch_gpu
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
    # program and it breaks other tests that run from the same pytest worker, therefore until this is
    # sorted out it must be run only in an external program, that is distributed=True in this
    # test and only under one or more gpus - if we want cpu will need to make a special test
    #
    # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
    # 2nd main() call it botches the future eval.
    #
    self.run_seqaseq_quick(distributed=__lowerCamelCase			,       extra_args_str='''--fp16 --fp16_backend=apex''' )
    # test 2nd time - was getting eval_loss': nan'
    # to reproduce the problem set distributed=False
    self.run_seqaseq_quick(distributed=__lowerCamelCase			,       extra_args_str='''--fp16 --fp16_backend=apex''' )


 @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''] )
 @require_torch_multi_gpu
 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	List[Any] ):
    # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
    UpperCAmelCase__	:Optional[Any]      =      {
        # test with the default log_level - should be info and thus log info once
        '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
        # test with low log_level and log_level_replica - should be noisy on all processes
        # now the info string should appear twice on 2 processes
        '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
        # test with high log_level and low log_level_replica
        # now the info string should appear once only on the replica
        '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
        # test with high log_level and log_level_replica - should be quiet on all processes
        '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
    }

    UpperCAmelCase__	:int      =      experiments[experiment_id]
    UpperCAmelCase__	:List[Any]      =      {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
    UpperCAmelCase__	:int      =      '''Running training'''
    with CaptureStderr() as cl:
       self.run_seqaseq_quick(**__lowerCamelCase			,       extra_args_str=data['''extra_args_str'''] )
    UpperCAmelCase__	:Any      =      len(re.findall(__lowerCamelCase			,       cl.err ) )
    self.assertEqual(__lowerCamelCase			,       data['''n_matches'''] )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:List[Any]      =      self.run_trainer(
        eval_steps=2			,       max_len=1_2_8			,       model_name=__lowerCamelCase			,       learning_rate=3e-4			,       num_train_epochs=1_0			,       distributed=__lowerCamelCase			,       )

    # Check metrics
    UpperCAmelCase__	:List[str]      =      TrainerState.load_from_json(os.path.join(__lowerCamelCase			,       '''trainer_state.json''' ) ).log_history
    UpperCAmelCase__	:Union[str, Any]      =      [log for log in logs if '''eval_loss''' in log.keys()]
    UpperCAmelCase__	:List[str]      =      eval_metrics[0]
    UpperCAmelCase__	:Any      =      eval_metrics[-1]

    assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
    assert isinstance(last_step_stats['''eval_bleu''']			,       __lowerCamelCase )

    # test if do_predict saves generations and metrics
    UpperCAmelCase__	:Tuple      =      os.listdir(__lowerCamelCase )
    UpperCAmelCase__	:List[str]      =      {os.path.basename(__lowerCamelCase ) for p in contents}
    assert "generated_predictions.txt" in contents
    assert "predict_results.json" in contents


 @slow
 @require_bitsandbytes
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    from transformers.training_args import OptimizerNames

    def train_and_return_metrics(__lowerCamelCase							:	str ) -> Tuple[int, float]:
       UpperCAmelCase__	:List[Any]      =      '''--skip_memory_metrics 0'''

       UpperCAmelCase__	:str      =      self.run_trainer(
           max_len=1_2_8			,       model_name=__lowerCamelCase			,       learning_rate=3e-4			,       num_train_epochs=1			,       optim=__lowerCamelCase			,       distributed=__lowerCamelCase			,       extra_args_str=__lowerCamelCase			,       do_eval=__lowerCamelCase			,       do_predict=__lowerCamelCase			,       n_gpus_to_use=1			,       )

       # Check metrics
       UpperCAmelCase__	:Optional[int]      =      TrainerState.load_from_json(Path(__lowerCamelCase			,       '''trainer_state.json''' ) ).log_history
       UpperCAmelCase__	:Optional[Any]      =      int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0 )
       UpperCAmelCase__	:Tuple      =      int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0 )

       UpperCAmelCase__	:Optional[Any]      =      logs[0]['''train_loss''']
       return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )

    UpperCAmelCase__	:Dict      =      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

    UpperCAmelCase__	:List[Any]      =      gpu_peak_mem_orig + gpu_alloc_mem_orig
    UpperCAmelCase__	:Optional[int]      =      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
    UpperCAmelCase__	:Dict      =      gpu_total_mem_orig - gpu_total_mem_bnb

    # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
    # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
    # in 2 bytes and the diff in optim memory usage is derived as so:
    #
    # - normal 25*8=~200MB (8 bytes per param)
    # - bnb    25*2= ~50MB (2 bytes per param)
    #
    # Thus we should expect ~150MB total memory saved.
    #
    # Peak memory should be the same - the total should be different by about that same margin
    #
    # After leaving a small margin to accommodate for differences between gpus let's check
    # that we have at least 120MB in savings
    UpperCAmelCase__	:Tuple      =      1_2_0

    # uncomment the following if this test starts failing - requires py38 for a new print feature
    # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
    # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
    # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
    # print(f"{gpu_alloc_mem_diff=}MB")
    # print(f"{gpu_peak_mem_diff=}MB")
    # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
    # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

    self.assertGreater(
        __lowerCamelCase			,       __lowerCamelCase			,       '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
        f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
        f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''			,       )

    self.assertGreater(
        __lowerCamelCase			,       __lowerCamelCase			,       '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
        f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
        f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''			,       )

    self.assertEqual(
        __lowerCamelCase			,       __lowerCamelCase			,       f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	float = 3e-3			,       __lowerCamelCase							:	str = "adafactor"			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	str = None			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = None			,       ):
    UpperCAmelCase__	:str      =      self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
    UpperCAmelCase__	:Optional[Any]      =      self.get_auto_remove_tmp_dir()
    UpperCAmelCase__	:str      =      f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowerCamelCase )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

    UpperCAmelCase__	:Union[str, Any]      =      f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowerCamelCase )}
        '''.split()

    UpperCAmelCase__	:Tuple      =      '''
            --do_predict
        '''.split()

    UpperCAmelCase__	:Any      =      []
    if do_train:
       args += args_train

    if do_eval:
       args += args_eval

    if do_predict:
       args += args_predict

    if predict_with_generate:
       args += "--predict_with_generate".split()

    if do_train:
       if optim == "adafactor":
          args += "--adafactor".split()
       else:
          args += f'''--optim {optim}'''.split()

    if extra_args_str is not None:
       args += extra_args_str.split()

    if distributed:
       if n_gpus_to_use is None:
          UpperCAmelCase__	:Dict      =      get_gpu_count()
       UpperCAmelCase__	:Dict      =      get_torch_dist_unique_port()
       UpperCAmelCase__	:List[Any]      =      f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
       UpperCAmelCase__	:Any      =      [sys.executable] + distributed_args + args
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       execute_subprocess_async(__lowerCamelCase			,       env=self.get_env() )
    else:
       UpperCAmelCase__	:int      =      ['''run_translation.py'''] + args
       with patch.object(__lowerCamelCase			,       '''argv'''			,       __lowerCamelCase ):
          main()

    return output_dir







'''simple docstring'''





from functools import reduce

__lowerCamelCase												=						(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)


def        a__     (					UpperCamelCase_						:      str = N						):
   return max(
       # mypy cannot properly interpret reduce
       int(reduce(lambda UpperCamelCase_,   UpperCamelCase_						: str(int(UpperCamelCase_						) * int(UpperCamelCase_						)						),   n[i : i + 13]						)						)
       for i in range(len(UpperCamelCase_						) - 12						)						)


if __name__ == "__main__":
    print(F'''{solution() = }''')



