





'''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


__lowercase  :   List[str]						      =os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
     from run_translation import main  # noqa


set_seed(42)
__lowercase  :   Tuple						      ="""sshleifer/student_marian_en_ro_6_1"""
__lowercase  :   Optional[int]						      ="""sshleifer/tiny-mbart"""

@require_torch
class      A       (	__lowercase						):



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Optional[int]=False		,  _lowerCAmelCase:						Optional[int]=None		,  _lowerCAmelCase:						Union[str, Any]=True		,  _lowerCAmelCase:						Tuple=True		,  _lowerCAmelCase:						Any=True		,  _lowerCAmelCase:						Dict=True		,  )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.run_trainer(
             eval_steps=1		,  max_len=12		,  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_											=TrainerState.load_from_json(os.path.join(_lowerCAmelCase		,  "trainer_state.json"       )       ).log_history

         if not do_eval:
            return

         UpperCAmelCase_											=[log for log in logs if "eval_loss" in log.keys()]

         UpperCAmelCase_											=eval_metrics[0]
         if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[str]       )						->    Tuple:
         '''simple docstring'''

         self.run_seqaseq_quick()



      @require_torch_multi_gpu
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Dict:
         '''simple docstring'''

         self.run_seqaseq_quick(distributed=_lowerCAmelCase       )



      @require_torch_multi_gpu
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Dict:
         '''simple docstring'''

         self.run_seqaseq_quick(distributed=_lowerCAmelCase       )



      @unittest.skip("Requires an update of the env running those tests"       )
      @require_torch_multi_gpu
      @require_fairscale
      def 	lowerCAmelCase__    (	self:						int       )						->    List[Any]:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[Any]:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						List[Any]       )						->    List[str]:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						Any       )						->    Dict:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						List[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             # 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_											=experiments[experiment_id]
         UpperCAmelCase_											={"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
         UpperCAmelCase_											="Running training"
         with CaptureStderr() as cl:
            self.run_seqaseq_quick(**_lowerCAmelCase		,  extra_args_str=data["extra_args_str"]       )
         UpperCAmelCase_											=len(re.findall(_lowerCAmelCase		,  cl.err       )       )
         self.assertEqual(_lowerCAmelCase		,  data["n_matches"]       )



      @slow
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.run_trainer(
             eval_steps=2		,  max_len=128		,  model_name=_lowerCAmelCase		,  learning_rate=3e-4		,  num_train_epochs=10		,  distributed=_lowerCAmelCase		,  )

         # Check metrics
         UpperCAmelCase_											=TrainerState.load_from_json(os.path.join(_lowerCAmelCase		,  "trainer_state.json"       )       ).log_history
         UpperCAmelCase_											=[log for log in logs if "eval_loss" in log.keys()]
         UpperCAmelCase_											=eval_metrics[0]
         UpperCAmelCase_											=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_											=os.listdir(_lowerCAmelCase       )
         UpperCAmelCase_											={os.path.basename(_lowerCAmelCase       ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents



      @slow
      @require_bitsandbytes
      def 	lowerCAmelCase__    (	self:						Dict       )						->    List[str]:
         '''simple docstring'''

         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(_lowerCAmelCase:						str       ) -> Tuple[int, float]:
            UpperCAmelCase_											="--skip_memory_metrics 0"

            UpperCAmelCase_											=self.run_trainer(
                max_len=128		,  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_											=TrainerState.load_from_json(Path(_lowerCAmelCase		,  "trainer_state.json"       )       ).log_history
            UpperCAmelCase_											=int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20       )
            UpperCAmelCase_											=int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20       )

            UpperCAmelCase_											=logs[0]["train_loss"]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
         UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

         UpperCAmelCase_											=gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase_											=gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase_											=gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase_											=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_											=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(
             _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 	lowerCAmelCase__    (	self:						str		,  _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		,  )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
         UpperCAmelCase_											=self.get_auto_remove_tmp_dir()
         UpperCAmelCase_											=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(_lowerCAmelCase       )}\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(_lowerCAmelCase       )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

         UpperCAmelCase_											=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(_lowerCAmelCase       )}\n        '.split()

         UpperCAmelCase_											="\n            --do_predict\n        ".split()

         UpperCAmelCase_											=[]
         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_											=get_gpu_count()
            UpperCAmelCase_											=get_torch_dist_unique_port()
            UpperCAmelCase_											=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_											=[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_											=["run_translation.py"] + args
            with patch.object(_lowerCAmelCase		,  "argv"		,  _lowerCAmelCase       ):
               main()

         return output_dir











__lowercase  :   Dict						      ="""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich




