
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
			import transformers

def 			a   (_lowerCAmelCase     ):
							random.seed(lowerCamelCase__     )
							np.random.seed(lowerCamelCase__     )
							torch.manual_seed(lowerCamelCase__     )
							torch.cuda.manual_seed_all(lowerCamelCase__     )
							# ^^ safe to call this function even if cuda is not available






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[str] = 0.99_99		,					_lowerCamelCase:   Any = 0.0		,					_lowerCamelCase:   Optional[int] = 0		,					_lowerCamelCase:   Optional[Any] = False		,					_lowerCamelCase:   int = 1.0		,					_lowerCamelCase:   Any = 2 / 3		,					_lowerCamelCase:   Optional[int] = None		,					_lowerCamelCase:   str = None		,					**_lowerCamelCase:   Optional[Any]		,					):

											if isinstance(UpperCamelCase_		,					torch.nn.Module		):
																		SCREAMING_SNAKE_CASE_   	=    (
																		    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
																		    "Please pass the parameters of the module instead."
																		)
																		deprecate(
																		    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''		,					'''1.0.0'''		,					UpperCamelCase_		,					standard_warn=UpperCamelCase_		,					)
																		SCREAMING_SNAKE_CASE_   	=    parameters.parameters()

																		# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																		SCREAMING_SNAKE_CASE_   	=    True

											if kwargs.get('''max_value'''		,					UpperCamelCase_		) is not None:
																		SCREAMING_SNAKE_CASE_   	=    "The `max_value` argument is deprecated. Please use `decay` instead."
																		deprecate('''max_value'''		,					'''1.0.0'''		,					UpperCamelCase_		,					standard_warn=UpperCamelCase_		)
																		SCREAMING_SNAKE_CASE_   	=    kwargs["max_value"]

											if kwargs.get('''min_value'''		,					UpperCamelCase_		) is not None:
																		SCREAMING_SNAKE_CASE_   	=    "The `min_value` argument is deprecated. Please use `min_decay` instead."
																		deprecate('''min_value'''		,					'''1.0.0'''		,					UpperCamelCase_		,					standard_warn=UpperCamelCase_		)
																		SCREAMING_SNAKE_CASE_   	=    kwargs["min_value"]

											SCREAMING_SNAKE_CASE_   	=    list(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    [p.clone().detach() for p in parameters]

											if kwargs.get('''device'''		,					UpperCamelCase_		) is not None:
																		SCREAMING_SNAKE_CASE_   	=    "The `device` argument is deprecated. Please use `to` instead."
																		deprecate('''device'''		,					'''1.0.0'''		,					UpperCamelCase_		,					standard_warn=UpperCamelCase_		)
																		self.to(device=kwargs['''device''']		)

											SCREAMING_SNAKE_CASE_   	=    None

											SCREAMING_SNAKE_CASE_   	=    decay
											SCREAMING_SNAKE_CASE_   	=    min_decay
											SCREAMING_SNAKE_CASE_   	=    update_after_step
											SCREAMING_SNAKE_CASE_   	=    use_ema_warmup
											SCREAMING_SNAKE_CASE_   	=    inv_gamma
											SCREAMING_SNAKE_CASE_   	=    power
											SCREAMING_SNAKE_CASE_   	=    0
											SCREAMING_SNAKE_CASE_   	=    None  # set in `step()`

											SCREAMING_SNAKE_CASE_   	=    model_cls
											SCREAMING_SNAKE_CASE_   	=    model_config


				@classmethod
				def     _A							(							cls:   int		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    model_cls.load_config(UpperCamelCase_		,					return_unused_kwargs=UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    model_cls.from_pretrained(UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    cls(model.parameters()		,					model_cls=UpperCamelCase_		,					model_config=model.config		)

											ema_model.load_state_dict(UpperCamelCase_		)
											return ema_model


				def     _A							(							self:   int		,					_lowerCamelCase:   Any		):
											if self.model_cls is None:
																		raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''		)

											if self.model_config is None:
																		raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''		)

											SCREAMING_SNAKE_CASE_   	=    self.model_cls.from_config(self.model_config		)
											SCREAMING_SNAKE_CASE_   	=    self.state_dict()
											state_dict.pop('''shadow_params'''		,					UpperCamelCase_		)

											model.register_to_config(**UpperCamelCase_		)
											self.copy_to(model.parameters()		)
											model.save_pretrained(UpperCamelCase_		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   Any		):
											SCREAMING_SNAKE_CASE_   	=    max(0		,					optimization_step - self.update_after_step - 1		)

											if step <= 0:
																		return 0.0

											if self.use_ema_warmup:
																		SCREAMING_SNAKE_CASE_   	=    1 - (1 + step / self.inv_gamma) ** -self.power
											else:
																		SCREAMING_SNAKE_CASE_   	=    (1 + step) / (10 + step)

											SCREAMING_SNAKE_CASE_   	=    min(UpperCamelCase_		,					self.decay		)
											# make sure decay is not smaller than min_decay
											SCREAMING_SNAKE_CASE_   	=    max(UpperCamelCase_		,					self.min_decay		)
											return cur_decay_value


				@torch.no_grad()
				def     _A							(							self:   int		,					_lowerCamelCase:   Any		):
											if isinstance(UpperCamelCase_		,					torch.nn.Module		):
																		SCREAMING_SNAKE_CASE_   	=    (
																		    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
																		    "Please pass the parameters of the module instead."
																		)
																		deprecate(
																		    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''		,					'''1.0.0'''		,					UpperCamelCase_		,					standard_warn=UpperCamelCase_		,					)
																		SCREAMING_SNAKE_CASE_   	=    parameters.parameters()

											SCREAMING_SNAKE_CASE_   	=    list(UpperCamelCase_		)

											self.optimization_step += 1

											# Compute the decay factor for the exponential moving average.
											SCREAMING_SNAKE_CASE_   	=    self.get_decay(self.optimization_step		)
											SCREAMING_SNAKE_CASE_   	=    decay
											SCREAMING_SNAKE_CASE_   	=    1 - decay

											SCREAMING_SNAKE_CASE_   	=    contextlib.nullcontext
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																		import deepspeed

											for s_param, param in zip(self.shadow_params		,					UpperCamelCase_		):
																		if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																									SCREAMING_SNAKE_CASE_   	=    deepspeed.zero.GatheredParameters(UpperCamelCase_		,					modifier_rank=UpperCamelCase_		)

																		with context_manager():
																									if param.requires_grad:
																																s_param.sub_(one_minus_decay * (s_param - param)		)
																									else:
																																s_param.copy_(UpperCamelCase_		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    list(UpperCamelCase_		)
											for s_param, param in zip(self.shadow_params		,					UpperCamelCase_		):
																		param.data.copy_(s_param.to(param.device		).data		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Optional[Any]=None		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    [
											    p.to(device=UpperCamelCase_		,					dtype=UpperCamelCase_		) if p.is_floating_point() else p.to(device=UpperCamelCase_		)
											    for p in self.shadow_params
											]


				def     _A							(							self:   Tuple		):
											return {
											    "decay": self.decay,
											    "min_decay": self.min_decay,
											    "optimization_step": self.optimization_step,
											    "update_after_step": self.update_after_step,
											    "use_ema_warmup": self.use_ema_warmup,
											    "inv_gamma": self.inv_gamma,
											    "power": self.power,
											    "shadow_params": self.shadow_params,
											}


				def     _A							(							self:   Dict		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    [param.detach().cpu().clone() for param in parameters]


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[Any]		):
											if self.temp_stored_params is None:
																		raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''		)
											for c_param, param in zip(self.temp_stored_params		,					UpperCamelCase_		):
																		param.data.copy_(c_param.data		)

											# Better memory-wise.
											SCREAMING_SNAKE_CASE_   	=    None





				def     _A							(							self:   Any		,					_lowerCamelCase:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    copy.deepcopy(UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''decay'''		,					self.decay		)
											if self.decay < 0.0 or self.decay > 1.0:
																		raise ValueError('''Decay must be between 0 and 1'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''min_decay'''		,					self.min_decay		)
											if not isinstance(self.min_decay		,					UpperCamelCase_		):
																		raise ValueError('''Invalid min_decay'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''optimization_step'''		,					self.optimization_step		)
											if not isinstance(self.optimization_step		,					UpperCamelCase_		):
																		raise ValueError('''Invalid optimization_step'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''update_after_step'''		,					self.update_after_step		)
											if not isinstance(self.update_after_step		,					UpperCamelCase_		):
																		raise ValueError('''Invalid update_after_step'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''use_ema_warmup'''		,					self.use_ema_warmup		)
											if not isinstance(self.use_ema_warmup		,					UpperCamelCase_		):
																		raise ValueError('''Invalid use_ema_warmup'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''inv_gamma'''		,					self.inv_gamma		)
											if not isinstance(self.inv_gamma		,					(float, int)		):
																		raise ValueError('''Invalid inv_gamma'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''power'''		,					self.power		)
											if not isinstance(self.power		,					(float, int)		):
																		raise ValueError('''Invalid power'''		)

											SCREAMING_SNAKE_CASE_   	=    state_dict.get('''shadow_params'''		,					UpperCamelCase_		)
											if shadow_params is not None:
																		SCREAMING_SNAKE_CASE_   	=    shadow_params
																		if not isinstance(self.shadow_params		,					UpperCamelCase_		):
																									raise ValueError('''shadow_params must be a list'''		)
																		if not all(isinstance(UpperCamelCase_		,					torch.Tensor		) for p in self.shadow_params		):
																									raise ValueError('''shadow_params must all be Tensors'''		)








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


