
'''simple docstring'''



class    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__      )    ->			None:
          _SCREAMING_SNAKE_CASE      =				size
          _SCREAMING_SNAKE_CASE      =				[0] * size
          _SCREAMING_SNAKE_CASE      =				[0] * size






       @staticmethod
       def        UpperCamelCase				(							A__      )    ->			int:
          return index | (index + 1)






       @staticmethod
       def        UpperCamelCase				(							A__      )    ->			int:
          return (index & (index + 1)) - 1






       def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			None:
          _SCREAMING_SNAKE_CASE      =				value
          while index < self.size:
             _SCREAMING_SNAKE_CASE      =				self.get_prev(A__      ) + 1
             if current_left_border == index:
                _SCREAMING_SNAKE_CASE      =				value
             else:
                _SCREAMING_SNAKE_CASE      =				max(A__       ,							A__       ,							A__      )
             _SCREAMING_SNAKE_CASE      =				self.get_next(A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			int:
          right -= 1  # Because of right is exclusive
          _SCREAMING_SNAKE_CASE      =				0
          while left <= right:
             _SCREAMING_SNAKE_CASE      =				self.get_prev(A__      )
             if left <= current_left:
                _SCREAMING_SNAKE_CASE      =				max(A__       ,							self.tree[right]      )
                _SCREAMING_SNAKE_CASE      =				current_left
             else:
                _SCREAMING_SNAKE_CASE      =				max(A__       ,							self.arr[right]      )
                right -= 1
          return result


if __name__ == "__main__":
     import doctest

     doctest.testmod()








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


set_seed(42)
UpperCamelCase__      :		Union[str, Any]       							=  "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__      :		str       							=  "sshleifer/tiny-mbart"

@require_torch
class    _a				(_lowerCamelCase):





       """simple docstring"""




       def        UpperCamelCase				(							self       ,							A__=False       ,							A__=None       ,							A__=True       ,							A__=True       ,							A__=True       ,							A__=True       ,							)    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=1       ,							max_len=12       ,							model_name=A__       ,							num_train_epochs=1       ,							distributed=A__       ,							extra_args_str=A__       ,							predict_with_generate=A__       ,							do_train=A__       ,							do_eval=A__       ,							do_predict=A__       ,							)
          _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(os.path.join(A__       ,							"""trainer_state.json"""      )      ).log_history

          if not do_eval:
             return

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

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

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






       @require_torch_non_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          self.run_seqaseq_quick()






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			Any:
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--sharded_ddp simple"""      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			Tuple:
          self.run_seqaseq_quick(distributed=A__       ,							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        UpperCamelCase				(							self      )    ->			str:
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--sharded_ddp zero_dp_2"""       ,							predict_with_generate=A__      )






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






       @require_apex
       @require_torch_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          # 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=A__       ,							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=A__       ,							extra_args_str="""--fp16 --fp16_backend=apex"""      )






       @parameterized.expand(["""base""", """low""", """high""", """mixed"""]      )
       @require_torch_multi_gpu
       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
          _SCREAMING_SNAKE_CASE      =				{
              # 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},
          }

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






       @slow
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=2       ,							max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=10       ,							distributed=A__       ,							)

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

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

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






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

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

             _SCREAMING_SNAKE_CASE      =				self.run_trainer(
                 max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=1       ,							optim=A__       ,							distributed=A__       ,							extra_args_str=A__       ,							do_eval=A__       ,							do_predict=A__       ,							n_gpus_to_use=1       ,							)

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

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

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

          _SCREAMING_SNAKE_CASE      =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

          _SCREAMING_SNAKE_CASE      =				gpu_peak_mem_orig + gpu_alloc_mem_orig
          _SCREAMING_SNAKE_CASE      =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
          _SCREAMING_SNAKE_CASE      =				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
          _SCREAMING_SNAKE_CASE      =				1_20

          # 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(
              A__       ,							A__       ,							"""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(
              A__       ,							A__       ,							"""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(
              A__       ,							A__       ,							F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = 3E-3       ,							A__ = "adafactor"       ,							A__ = False       ,							A__ = None       ,							A__ = 0       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = None       ,							)    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
          _SCREAMING_SNAKE_CASE      =				self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE      =				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(A__      )}\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(A__      )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

          _SCREAMING_SNAKE_CASE      =				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(A__      )}\n        ".split()

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

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

          return output_dir






