
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    fname.split(os.path.sep     )[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''  ,    _lowerCAmelCase     ).groups()[0]






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    file_names
											SCREAMING_SNAKE_CASE_   	=    image_transform
											SCREAMING_SNAKE_CASE_   	=    label_to_id


				def __len__(							self:   Any		):
											return len(self.file_names		)





				def __getitem__(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.file_names[idx]
											SCREAMING_SNAKE_CASE_   	=    PIL.Image.open(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    raw_image.convert('''RGB'''		)
											if self.image_transform is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.image_transform(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    extract_label(_lowerCamelCase		)
											if self.label_to_id is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.label_to_id[label]
											return {"image": image, "label": label}






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							# Initialize accelerator
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(
														    cpu=args.cpu  ,    mixed_precision=args.mixed_precision  ,    log_with='''all'''  ,    project_dir=args.project_dir     )
							else:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(cpu=args.cpu  ,    mixed_precision=args.mixed_precision     )

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_   	=    config['''lr''']
							SCREAMING_SNAKE_CASE_   	=    int(config['''num_epochs''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''seed''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''batch_size''']     )
							SCREAMING_SNAKE_CASE_   	=    config['''image_size''']
							if not isinstance(_lowerCAmelCase  ,    (list, tuple)     ):
														SCREAMING_SNAKE_CASE_   	=    (image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps  ,    '''isdigit'''     ):
														if args.checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					SCREAMING_SNAKE_CASE_   	=    int(args.checkpointing_steps     )
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    os.path.split(_lowerCAmelCase     )[-1].split('''.'''     )[0]
														accelerator.init_trackers(_lowerCAmelCase  ,    _lowerCAmelCase     )

							# Grab all the image filenames
							SCREAMING_SNAKE_CASE_   	=    [os.path.join(args.data_dir  ,    _lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith('''.jpg'''     )]

							# Build the label correspondences
							SCREAMING_SNAKE_CASE_   	=    [extract_label(_lowerCAmelCase     ) for fname in file_names]
							SCREAMING_SNAKE_CASE_   	=    list(set(_lowerCAmelCase     )     )
							id_to_label.sort()
							SCREAMING_SNAKE_CASE_   	=    {lbl: i for i, lbl in enumerate(_lowerCAmelCase     )}

							# Set the seed before splitting the data.
							np.random.seed(_lowerCAmelCase     )
							torch.manual_seed(_lowerCAmelCase     )
							torch.cuda.manual_seed_all(_lowerCAmelCase     )

							# Split our filenames between train and validation
							SCREAMING_SNAKE_CASE_   	=    np.random.permutation(len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    int(0.8 * len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    random_perm[:cut]
							SCREAMING_SNAKE_CASE_   	=    random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							SCREAMING_SNAKE_CASE_   	=    Compose([RandomResizedCrop(_lowerCAmelCase  ,    scale=(0.5, 1.0)     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset(
							    [file_names[i] for i in train_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# For evaluation, we use a deterministic Resize
							SCREAMING_SNAKE_CASE_   	=    Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset([file_names[i] for i in eval_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_   	=    create_model('''resnet50d'''  ,    pretrained=_lowerCAmelCase  ,    num_classes=len(_lowerCAmelCase     )     )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							SCREAMING_SNAKE_CASE_   	=    model.to(accelerator.device     )

							# Freezing the base model
							for param in model.parameters():
														SCREAMING_SNAKE_CASE_   	=    False
							for param in model.get_classifier().parameters():
														SCREAMING_SNAKE_CASE_   	=    True

							# We normalize the batches of images to be a bit faster.
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''mean''']     )[None, :, None, None].to(accelerator.device     )
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''std''']     )[None, :, None, None].to(accelerator.device     )

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_   	=    torch.optim.Adam(params=model.parameters()  ,    lr=lr / 2_5     )

							# Instantiate learning rate scheduler
							SCREAMING_SNAKE_CASE_   	=    OneCycleLR(optimizer=_lowerCAmelCase  ,    max_lr=_lowerCAmelCase  ,    epochs=_lowerCAmelCase  ,    steps_per_epoch=len(_lowerCAmelCase     )     )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(
							    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE_   	=    0
							# We also need to keep track of the starting epoch so files are named properly
							SCREAMING_SNAKE_CASE_   	=    0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
																					accelerator.load_state(args.resume_from_checkpoint     )
																					SCREAMING_SNAKE_CASE_   	=    os.path.basename(args.resume_from_checkpoint     )
														else:
																					# Get the most recent checkpoint
																					SCREAMING_SNAKE_CASE_   	=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime     )
																					SCREAMING_SNAKE_CASE_   	=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														SCREAMING_SNAKE_CASE_   	=    os.path.splitext(_lowerCAmelCase     )[0]

														if "epoch" in training_difference:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''epoch_'''  ,    ''''''     )     ) + 1
																					SCREAMING_SNAKE_CASE_   	=    None
														else:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''step_'''  ,    ''''''     )     )
																					SCREAMING_SNAKE_CASE_   	=    resume_step // len(_lowerCAmelCase     )
																					resume_step -= starting_epoch * len(_lowerCAmelCase     )

    # Now we train the model
							for epoch in range(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														model.train()
														if args.with_tracking:
																					SCREAMING_SNAKE_CASE_   	=    0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					SCREAMING_SNAKE_CASE_   	=    accelerator.skip_first_batches(_lowerCAmelCase  ,    _lowerCAmelCase     )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					SCREAMING_SNAKE_CASE_   	=    train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    torch.nn.functional.cross_entropy(_lowerCAmelCase  ,    batch['''label''']     )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(_lowerCAmelCase     )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
																												SCREAMING_SNAKE_CASE_   	=    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																																			accelerator.save_state(_lowerCAmelCase     )
														model.eval()
														SCREAMING_SNAKE_CASE_   	=    0
														SCREAMING_SNAKE_CASE_   	=    0
														for step, batch in enumerate(_lowerCAmelCase     ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    outputs.argmax(dim=-1     )
																					SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather_for_metrics((predictions, batch['''label'''])     )
																					SCREAMING_SNAKE_CASE_   	=    predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														SCREAMING_SNAKE_CASE_   	=    accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}: {1_0_0 * eval_metric:.2f}"     )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        '''accuracy''': 1_0_0 * eval_metric,
																					        '''train_loss''': total_loss.item() / len(_lowerCAmelCase     ),
																					        '''epoch''': epoch,
																					    }  ,    step=_lowerCAmelCase  ,    )
														if checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																					accelerator.save_state(_lowerCAmelCase     )

							if args.with_tracking:
														accelerator.end_training()

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    argparse.ArgumentParser(description='''Simple example of training script.'''     )
							parser.add_argument('''--data_dir'''  ,    required=_lowerCAmelCase  ,    help='''The data folder on disk.'''     )
							parser.add_argument('''--fp16'''  ,    action='''store_true'''  ,    help='''If passed, will use FP16 training.'''     )
							parser.add_argument(
							    '''--mixed_precision'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,    help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''  ,    )
							parser.add_argument('''--cpu'''  ,    action='''store_true'''  ,    help='''If passed, will train on the CPU.'''     )
							parser.add_argument(
							    '''--checkpointing_steps'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,    )
							parser.add_argument(
							    '''--output_dir'''  ,    type=_lowerCAmelCase  ,    default='''.'''  ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,    )
							parser.add_argument(
							    '''--resume_from_checkpoint'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''If the training should continue from a checkpoint folder.'''  ,    )
							parser.add_argument(
							    '''--with_tracking'''  ,    action='''store_true'''  ,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,    )
							parser.add_argument(
							    '''--project_dir'''  ,    type=_lowerCAmelCase  ,    default='''logs'''  ,    help='''Location on where to store experiment tracking logs` and relevent project information'''  ,    )
							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()
							SCREAMING_SNAKE_CASE_   	=    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(_lowerCAmelCase  ,    _lowerCAmelCase     )


if __name__ == "__main__":
			main()








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 						=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 						="""sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE 						="""sshleifer/tiny-mbart"""






@require_torch
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   List[Any]=False		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Optional[int]=True		,					_lowerCamelCase:   Any=True		,					_lowerCamelCase:   Any=True		,					):
											SCREAMING_SNAKE_CASE_   	=    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		,					)
											SCREAMING_SNAKE_CASE_   	=    TrainerState.load_from_json(os.path.join(_lowerCamelCase		,					'''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''']		,					_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:   Optional[int]		):
											self.run_seqaseq_quick()


				@require_torch_multi_gpu
				def     _A							(							self:   Optional[Any]		):
											self.run_seqaseq_quick(distributed=_lowerCamelCase		)


				@require_torch_multi_gpu
				def     _A							(							self:   List[Any]		):
											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:   Optional[int]		):
											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:   Optional[Any]		):
											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:   Union[str, Any]		):
											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:   Optional[Any]		):
											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:   List[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=_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:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											# 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(**_lowerCamelCase		,					extra_args_str=data['''extra_args_str''']		)
											SCREAMING_SNAKE_CASE_   	=    len(re.findall(_lowerCamelCase		,					cl.err		)		)
											self.assertEqual(_lowerCamelCase		,					data['''n_matches''']		)


				@slow
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.run_trainer(
											    eval_steps=2		,					max_len=1_28		,					model_name=_lowerCamelCase		,					learning_rate=3E-4		,					num_train_epochs=10		,					distributed=_lowerCamelCase		,					)

											# Check metrics
											SCREAMING_SNAKE_CASE_   	=    TrainerState.load_from_json(os.path.join(_lowerCamelCase		,					'''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''']		,					_lowerCamelCase		)

											# test if do_predict saves generations and metrics
											SCREAMING_SNAKE_CASE_   	=    os.listdir(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {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:   Optional[int]		):
											from transformers.training_args import OptimizerNames

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

																		SCREAMING_SNAKE_CASE_   	=    self.run_trainer(
																		    max_len=1_28		,					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
																		SCREAMING_SNAKE_CASE_   	=    TrainerState.load_from_json(Path(_lowerCamelCase		,					'''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(
											    _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:   Union[str, Any]		,					_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		,					):
											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(_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()

											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(_lowerCamelCase		)}\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(_lowerCamelCase		,					env=self.get_env()		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    ['''run_translation.py'''] + args
																		with patch.object(_lowerCamelCase		,					'''argv'''		,					_lowerCamelCase		):
																									main()

											return output_dir


