
__SCREAMING_SNAKE_CASE 						="""0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
			if not is_onnx_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_onnx_objects import *  # noqa F403
else:
			from .pipelines import OnnxRuntimeModel

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_pt_objects import *  # noqa F403
else:
			from .models import (
			    AutoencoderKL,
			    ControlNetModel,
			    ModelMixin,
			    PriorTransformer,
			    TaFilmDecoder,
			    TransformeraDModel,
			    UNetaDModel,
			    UNetaDConditionModel,
			    UNetaDModel,
			    UNetaDConditionModel,
			    VQModel,
			)
			from .optimization import (
			    get_constant_schedule,
			    get_constant_schedule_with_warmup,
			    get_cosine_schedule_with_warmup,
			    get_cosine_with_hard_restarts_schedule_with_warmup,
			    get_linear_schedule_with_warmup,
			    get_polynomial_decay_schedule_with_warmup,
			    get_scheduler,
			)
			from .pipelines import (
			    AudioPipelineOutput,
			    ConsistencyModelPipeline,
			    DanceDiffusionPipeline,
			    DDIMPipeline,
			    DDPMPipeline,
			    DiffusionPipeline,
			    DiTPipeline,
			    ImagePipelineOutput,
			    KarrasVePipeline,
			    LDMPipeline,
			    LDMSuperResolutionPipeline,
			    PNDMPipeline,
			    RePaintPipeline,
			    ScoreSdeVePipeline,
			)
			from .schedulers import (
			    CMStochasticIterativeScheduler,
			    DDIMInverseScheduler,
			    DDIMParallelScheduler,
			    DDIMScheduler,
			    DDPMParallelScheduler,
			    DDPMScheduler,
			    DEISMultistepScheduler,
			    DPMSolverMultistepInverseScheduler,
			    DPMSolverMultistepScheduler,
			    DPMSolverSinglestepScheduler,
			    EulerAncestralDiscreteScheduler,
			    EulerDiscreteScheduler,
			    HeunDiscreteScheduler,
			    IPNDMScheduler,
			    KarrasVeScheduler,
			    KDPMaAncestralDiscreteScheduler,
			    KDPMaDiscreteScheduler,
			    PNDMScheduler,
			    RePaintScheduler,
			    SchedulerMixin,
			    ScoreSdeVeScheduler,
			    UnCLIPScheduler,
			    UniPCMultistepScheduler,
			    VQDiffusionScheduler,
			)
			from .training_utils import EMAModel

try:
			if not (is_torch_available() and is_scipy_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
			from .schedulers import LMSDiscreteScheduler

try:
			if not (is_torch_available() and is_torchsde_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
			from .schedulers import DPMSolverSDEScheduler

try:
			if not (is_torch_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipelines import (
			    AltDiffusionImgaImgPipeline,
			    AltDiffusionPipeline,
			    AudioLDMPipeline,
			    CycleDiffusionPipeline,
			    IFImgaImgPipeline,
			    IFImgaImgSuperResolutionPipeline,
			    IFInpaintingPipeline,
			    IFInpaintingSuperResolutionPipeline,
			    IFPipeline,
			    IFSuperResolutionPipeline,
			    ImageTextPipelineOutput,
			    KandinskyImgaImgPipeline,
			    KandinskyInpaintPipeline,
			    KandinskyPipeline,
			    KandinskyPriorPipeline,
			    KandinskyVaaControlnetImgaImgPipeline,
			    KandinskyVaaControlnetPipeline,
			    KandinskyVaaImgaImgPipeline,
			    KandinskyVaaInpaintPipeline,
			    KandinskyVaaPipeline,
			    KandinskyVaaPriorEmbaEmbPipeline,
			    KandinskyVaaPriorPipeline,
			    LDMTextToImagePipeline,
			    PaintByExamplePipeline,
			    SemanticStableDiffusionPipeline,
			    ShapEImgaImgPipeline,
			    ShapEPipeline,
			    StableDiffusionAttendAndExcitePipeline,
			    StableDiffusionControlNetImgaImgPipeline,
			    StableDiffusionControlNetInpaintPipeline,
			    StableDiffusionControlNetPipeline,
			    StableDiffusionDepthaImgPipeline,
			    StableDiffusionDiffEditPipeline,
			    StableDiffusionImageVariationPipeline,
			    StableDiffusionImgaImgPipeline,
			    StableDiffusionInpaintPipeline,
			    StableDiffusionInpaintPipelineLegacy,
			    StableDiffusionInstructPixaPixPipeline,
			    StableDiffusionLatentUpscalePipeline,
			    StableDiffusionLDMaDPipeline,
			    StableDiffusionModelEditingPipeline,
			    StableDiffusionPanoramaPipeline,
			    StableDiffusionParadigmsPipeline,
			    StableDiffusionPipeline,
			    StableDiffusionPipelineSafe,
			    StableDiffusionPixaPixZeroPipeline,
			    StableDiffusionSAGPipeline,
			    StableDiffusionUpscalePipeline,
			    StableUnCLIPImgaImgPipeline,
			    StableUnCLIPPipeline,
			    TextToVideoSDPipeline,
			    TextToVideoZeroPipeline,
			    UnCLIPImageVariationPipeline,
			    UnCLIPPipeline,
			    UniDiffuserModel,
			    UniDiffuserPipeline,
			    UniDiffuserTextDecoder,
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			    VideoToVideoSDPipeline,
			    VQDiffusionPipeline,
			)

try:
			if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
			from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
			if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
			from .pipelines import StableDiffusionKDiffusionPipeline

try:
			if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
			from .pipelines import (
			    OnnxStableDiffusionImgaImgPipeline,
			    OnnxStableDiffusionInpaintPipeline,
			    OnnxStableDiffusionInpaintPipelineLegacy,
			    OnnxStableDiffusionPipeline,
			    OnnxStableDiffusionUpscalePipeline,
			    StableDiffusionOnnxPipeline,
			)

try:
			if not (is_torch_available() and is_librosa_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
			from .pipelines import AudioDiffusionPipeline, Mel

try:
			if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
			from .pipelines import SpectrogramDiffusionPipeline

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_flax_objects import *  # noqa F403
else:
			from .models.controlnet_flax import FlaxControlNetModel
			from .models.modeling_flax_utils import FlaxModelMixin
			from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
			from .models.vae_flax import FlaxAutoencoderKL
			from .pipelines import FlaxDiffusionPipeline
			from .schedulers import (
			    FlaxDDIMScheduler,
			    FlaxDDPMScheduler,
			    FlaxDPMSolverMultistepScheduler,
			    FlaxKarrasVeScheduler,
			    FlaxLMSDiscreteScheduler,
			    FlaxPNDMScheduler,
			    FlaxSchedulerMixin,
			    FlaxScoreSdeVeScheduler,
			)


try:
			if not (is_flax_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
			from .pipelines import (
			    FlaxStableDiffusionControlNetPipeline,
			    FlaxStableDiffusionImgaImgPipeline,
			    FlaxStableDiffusionInpaintPipeline,
			    FlaxStableDiffusionPipeline,
			)

try:
			if not (is_note_seq_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_note_seq_objects import *  # noqa F403
else:
			from .pipelines import MidiProcessor








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


