


'''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


__UpperCAmelCase    :int				       =     os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
      from run_translation import main  # noqa


set_seed(4_2)
__UpperCAmelCase    :int				       =     "sshleifer/student_marian_en_ro_6_1"
__UpperCAmelCase    :int				       =     "sshleifer/tiny-mbart"



@require_torch
class 			a       (	_a ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	int=False							,				snake_case  :	str=None							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	Optional[int]=True							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	Any=True							,				)		->							int:
          __UpperCAmelCase			:				List[str]													=    self.run_trainer(
              eval_steps=1							,				max_len=12							,				model_name=snake_case							,				num_train_epochs=1							,				distributed=snake_case							,				extra_args_str=snake_case							,				predict_with_generate=snake_case							,				do_train=snake_case							,				do_eval=snake_case							,				do_predict=snake_case							,				)
          __UpperCAmelCase			:				int													=    TrainerState.load_from_json(os.path.join(snake_case							,				'''trainer_state.json'''    )    ).log_history

          if not do_eval:
               return

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

     @require_torch_non_multi_gpu
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Any:
          self.run_seqaseq_quick()

     @require_torch_multi_gpu
     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[Any]:
          self.run_seqaseq_quick(distributed=snake_case    )

     @require_torch_multi_gpu
     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[int]:
          self.run_seqaseq_quick(distributed=snake_case    )

     @unittest.skip('''Requires an update of the env running those tests'''    )
     @require_torch_multi_gpu
     @require_fairscale
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
          self.run_seqaseq_quick(distributed=snake_case							,				extra_args_str='''--sharded_ddp simple'''    )

     @unittest.skip('''Requires an update of the env running those tests'''    )
     @require_torch_multi_gpu
     @require_fairscale
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[int]:
          self.run_seqaseq_quick(distributed=snake_case							,				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 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							List[str]:
          self.run_seqaseq_quick(distributed=snake_case							,				extra_args_str='''--sharded_ddp zero_dp_2'''							,				predict_with_generate=snake_case    )

     @unittest.skip('''Requires an update of the env running those tests'''    )
     @require_torch_multi_gpu
     @require_fairscale
     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[str]:
          self.run_seqaseq_quick(
              distributed=snake_case							,				extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,				predict_with_generate=snake_case    )

     @require_apex
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							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=snake_case							,				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=snake_case							,				extra_args_str='''--fp16 --fp16_backend=apex'''    )

     @parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']    )
     @require_torch_multi_gpu
     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int    )		->							List[Any]:
          # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
          __UpperCAmelCase			:				Optional[int]													=    {
              # 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			:				Tuple													=    experiments[experiment_id]
          __UpperCAmelCase			:				Union[str, Any]													=    {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
          __UpperCAmelCase			:				Tuple													=    '''Running training'''
          with CaptureStderr() as cl:
               self.run_seqaseq_quick(**snake_case							,				extra_args_str=data['''extra_args_str''']    )
          __UpperCAmelCase			:				Optional[Any]													=    len(re.findall(snake_case							,				cl.err    )    )
          self.assertEqual(snake_case							,				data['''n_matches''']    )

     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Any:
          __UpperCAmelCase			:				List[Any]													=    self.run_trainer(
              eval_steps=2							,				max_len=128							,				model_name=snake_case							,				learning_rate=3E-4							,				num_train_epochs=10							,				distributed=snake_case							,				)

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

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

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

     @slow
     @require_bitsandbytes
     def 				lowerCamelCase__				(       self  :	Dict    )		->							int:
          from transformers.training_args import OptimizerNames

          def train_and_return_metrics(snake_case  :	str    ) -> Tuple[int, float]:
               __UpperCAmelCase			:				Optional[Any]													=    '''--skip_memory_metrics 0'''

               __UpperCAmelCase			:				List[Any]													=    self.run_trainer(
                   max_len=128							,				model_name=snake_case							,				learning_rate=3E-4							,				num_train_epochs=1							,				optim=snake_case							,				distributed=snake_case							,				extra_args_str=snake_case							,				do_eval=snake_case							,				do_predict=snake_case							,				n_gpus_to_use=1							,				)

               # Check metrics
               __UpperCAmelCase			:				Dict													=    TrainerState.load_from_json(Path(snake_case							,				'''trainer_state.json'''    )    ).log_history
               __UpperCAmelCase			:				int													=    int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20    )
               __UpperCAmelCase			:				Optional[Any]													=    int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20    )

               __UpperCAmelCase			:				Union[str, Any]													=    logs[0]['''train_loss''']
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value    )
          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    train_and_return_metrics(OptimizerNames.ADAMW_BNB.value    )

          __UpperCAmelCase			:				Dict													=    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

          __UpperCAmelCase			:				Tuple													=    gpu_peak_mem_orig + gpu_alloc_mem_orig
          __UpperCAmelCase			:				Optional[int]													=    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
          __UpperCAmelCase			:				Union[str, Any]													=    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			:				List[str]													=    120

          # 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(
              snake_case							,				snake_case							,				'''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(
              snake_case							,				snake_case							,				'''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(
              snake_case							,				snake_case							,				f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'    )






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

          __UpperCAmelCase			:				List[str]													=    f'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(snake_case    )}\n        '.split()

          __UpperCAmelCase			:				Dict													=    '''
            --do_predict
        '''.split()

          __UpperCAmelCase			:				Optional[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			:				int													=    get_gpu_count()
               __UpperCAmelCase			:				Optional[Any]													=    get_torch_dist_unique_port()
               __UpperCAmelCase			:				Any													=    f'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
               __UpperCAmelCase			:				List[str]													=    [sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(snake_case							,				env=self.get_env()    )
          else:
               __UpperCAmelCase			:				Union[str, Any]													=    ['''run_translation.py'''] + args
               with patch.object(snake_case							,				'''argv'''							,				snake_case    ):
                    main()

          return output_dir



'''simple docstring'''



def 			_a			(		_lowercase      :			int = 4000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    b, a + b
     return sum(_lowercase						)


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