






class     lowerCamelCase_					:







				def __init__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   size
								__magic_name__		:Union[str, Any]   =   [0] * size
								__magic_name__		:str   =   [0] * size







				@staticmethod
				def       A						(    __lowerCAmelCase  ):
								"""simple docstring"""
								return index | (index + 1)







				@staticmethod
				def       A						(    __lowerCAmelCase  ):
								"""simple docstring"""
								return (index & (index + 1)) - 1







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   value
								while index < self.size:
												__magic_name__		:Optional[Any]   =   self.get_prev(__lowerCAmelCase  ) + 1
												if current_left_border == index:
																__magic_name__		:Union[str, Any]   =   value
												else:
																__magic_name__		:List[str]   =   max(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )
												__magic_name__		:Optional[int]   =   self.get_next(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								right -= 1  # Because of right is exclusive
								__magic_name__		:List[Any]   =   0
								while left <= right:
												__magic_name__		:Tuple   =   self.get_prev(__lowerCAmelCase  )
												if left <= current_left:
																__magic_name__		:Any   =   max(__lowerCAmelCase							,       self.tree[right]  )
																__magic_name__		:List[str]   =   current_left
												else:
																__magic_name__		:Dict   =   max(__lowerCAmelCase							,       self.arr[right]  )
																right -= 1
								return result


if __name__ == "__main__":
					import doctest

					doctest.testmod()














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


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


set_seed(42)
SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""sshleifer/student_marian_en_ro_6_1"""
SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""sshleifer/tiny-mbart"""
@require_torch
class     lowerCamelCase_					(   lowerCamelCase			):







				def       A						(    self							,       __lowerCAmelCase=False							,       __lowerCAmelCase=None							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   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							,       )
								__magic_name__		:Any   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history

								if not do_eval:
												return

								__magic_name__		:Any   =   [log for log in logs if '''eval_loss''' in log.keys()]

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

												__magic_name__		:Tuple   =   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       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick()







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""simple docstring"""
								self.run_seqaseq_quick(distributed=__lowerCAmelCase  )







				@require_torch_multi_gpu
				def       A						(    self  ):
								"""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       A						(    self  ):
								"""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       A						(    self  ):
								"""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       A						(    self  ):
								"""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       A						(    self  ):
								"""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       A						(    self  ):
								"""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=__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       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
								__magic_name__		: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},
								}

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







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   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
								__magic_name__		:Optional[int]   =   TrainerState.load_from_json(os.path.join(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
								__magic_name__		:List[str]   =   [log for log in logs if '''eval_loss''' in log.keys()]
								__magic_name__		:Any   =   eval_metrics[0]
								__magic_name__		: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''']							,       __lowerCAmelCase  )

								# test if do_predict saves generations and metrics
								__magic_name__		:List[Any]   =   os.listdir(__lowerCAmelCase  )
								__magic_name__		: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       A						(    self  ):
								"""simple docstring"""
								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(__lowerCAmelCase  ) -> Tuple[int, float]:
												__magic_name__		:str   =   '''--skip_memory_metrics 0'''

												__magic_name__		:Dict   =   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
												__magic_name__		:Optional[Any]   =   TrainerState.load_from_json(Path(__lowerCAmelCase							,       '''trainer_state.json'''  )  ).log_history
												__magic_name__		:int   =   int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0  )
												__magic_name__		:Optional[Any]   =   int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0  )

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

								__magic_name__  ,					__magic_name__  ,					__magic_name__		:int   =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

								__magic_name__		:Tuple   =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								__magic_name__		:Tuple   =   gpu_peak_mem_orig + gpu_alloc_mem_orig
								__magic_name__		:List[Any]   =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								__magic_name__		:Optional[int]   =   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
								__magic_name__		:Optional[Any]   =   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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 3E-3							,       __lowerCAmelCase = "adafactor"							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								__magic_name__		:Dict   =   self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple   =   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()

								__magic_name__		:str   =   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()

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

								__magic_name__		:Optional[int]   =   []
								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:
																__magic_name__		:List[Any]   =   get_gpu_count()
												__magic_name__		:Tuple   =   get_torch_dist_unique_port()
												__magic_name__		:Union[str, 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()
												__magic_name__		: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:
												__magic_name__		:List[Any]   =   ['''run_translation.py'''] + args
												with patch.object(__lowerCAmelCase							,       '''argv'''							,       __lowerCAmelCase  ):
																main()

								return output_dir






