

'''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    A__ ( _snake_case     ):



    def    snake_case_     (   self						,			UpperCamelCase__=False						,			UpperCamelCase__=None						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			)  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.run_trainer(
               eval_steps=1						,			max_len=12						,			model_name=UpperCamelCase__						,			num_train_epochs=1						,			distributed=UpperCamelCase__						,			extra_args_str=UpperCamelCase__						,			predict_with_generate=UpperCamelCase__						,			do_train=UpperCamelCase__						,			do_eval=UpperCamelCase__						,			do_predict=UpperCamelCase__						,			)
           A_		   = TrainerState.load_from_json(os.path.join(UpperCamelCase__						,			"""trainer_state.json"""    )    ).log_history

           if not do_eval:
                  return

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

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

                  A_		   = eval_metrics[-1]
                  assert isinstance(last_step_stats["""eval_bleu"""]						,			UpperCamelCase__    )
                  assert not math.isnan(float(last_step_stats["""eval_loss"""]    )    ), "eval_loss must not be `nan`"



    @require_torch_non_multi_gpu
    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           self.run_seqaseq_quick()



    @require_torch_multi_gpu
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           self.run_seqaseq_quick(distributed=UpperCamelCase__    )



    @require_torch_multi_gpu
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           self.run_seqaseq_quick(distributed=UpperCamelCase__    )



    @unittest.skip("""Requires an update of the env running those tests"""    )
    @require_torch_multi_gpu
    @require_fairscale
    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           self.run_seqaseq_quick(distributed=UpperCamelCase__						,			extra_args_str="""--sharded_ddp simple"""    )



    @unittest.skip("""Requires an update of the env running those tests"""    )
    @require_torch_multi_gpu
    @require_fairscale
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           self.run_seqaseq_quick(distributed=UpperCamelCase__						,			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    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           self.run_seqaseq_quick(distributed=UpperCamelCase__						,			extra_args_str="""--sharded_ddp zero_dp_2"""						,			predict_with_generate=UpperCamelCase__    )



    @unittest.skip("""Requires an update of the env running those tests"""    )
    @require_torch_multi_gpu
    @require_fairscale
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           self.run_seqaseq_quick(
               distributed=UpperCamelCase__						,			extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""						,			predict_with_generate=UpperCamelCase__    )



    @require_apex
    @require_torch_gpu
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           # 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=UpperCamelCase__						,			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=UpperCamelCase__						,			extra_args_str="""--fp16 --fp16_backend=apex"""    )



    @parameterized.expand(["""base""", """low""", """high""", """mixed"""]    )
    @require_torch_multi_gpu
    def    snake_case_     (   self						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
           A_		   = {
               # 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},
           }

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



    @slow
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = self.run_trainer(
               eval_steps=2						,			max_len=128						,			model_name=UpperCamelCase__						,			learning_rate=3e-4						,			num_train_epochs=10						,			distributed=UpperCamelCase__						,			)

           # Check metrics
           A_		   = TrainerState.load_from_json(os.path.join(UpperCamelCase__						,			"""trainer_state.json"""    )    ).log_history
           A_		   = [log for log in logs if """eval_loss""" in log.keys()]
           A_		   = eval_metrics[0]
           A_		   = eval_metrics[-1]

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

           # test if do_predict saves generations and metrics
           A_		   = os.listdir(UpperCamelCase__    )
           A_		   = {os.path.basename(UpperCamelCase__    ) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents



    @slow
    @require_bitsandbytes
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(UpperCamelCase__    ) -> Tuple[int, float]:
                  A_		   = """--skip_memory_metrics 0"""

                  A_		   = self.run_trainer(
                      max_len=128						,			model_name=UpperCamelCase__						,			learning_rate=3e-4						,			num_train_epochs=1						,			optim=UpperCamelCase__						,			distributed=UpperCamelCase__						,			extra_args_str=UpperCamelCase__						,			do_eval=UpperCamelCase__						,			do_predict=UpperCamelCase__						,			n_gpus_to_use=1						,			)

                  # Check metrics
                  A_		   = TrainerState.load_from_json(Path(UpperCamelCase__						,			"""trainer_state.json"""    )    ).log_history
                  A_		   = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20    )
                  A_		   = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20    )

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

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

           A_		   = gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           A_		   = gpu_peak_mem_orig + gpu_alloc_mem_orig
           A_		   = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           A_		   = 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
           A_		   = 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(
               UpperCamelCase__						,			UpperCamelCase__						,			"""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(
               UpperCamelCase__						,			UpperCamelCase__						,			"""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(
               UpperCamelCase__						,			UpperCamelCase__						,			f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = 3e-3						,			UpperCamelCase__ = "adafactor"						,			UpperCamelCase__ = False						,			UpperCamelCase__ = None						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = True						,			UpperCamelCase__ = True						,			UpperCamelCase__ = True						,			UpperCamelCase__ = True						,			UpperCamelCase__ = None						,			)  -> List[Any]:





           '''simple docstring'''
           A_		   = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
           A_		   = self.get_auto_remove_tmp_dir()
           A_		   = 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(UpperCamelCase__    )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase__    )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

           A_		   = 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(UpperCamelCase__    )}
        '''.split()

           A_		   = """
            --do_predict
        """.split()

           A_		   = []
           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:
                         A_		   = get_gpu_count()
                  A_		   = get_torch_dist_unique_port()
                  A_		   = 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()
                  A_		   = [sys.executable] + distributed_args + args
                  # keep for quick debug
                  # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                  execute_subprocess_async(UpperCamelCase__						,			env=self.get_env()    )
           else:
                  A_		   = ["""run_translation.py"""] + args
                  with patch.object(UpperCamelCase__						,			"""argv"""						,			UpperCamelCase__    ):
                         main()

           return output_dir



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
