
'''simple docstring'''





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMAEForPreTraining, ViTMAEModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class        UpperCamelCase__     :






 def __init__(	self     :List[Any]		, _A     :Union[str, Any]		, _A     :str=13		, _A     :int=30		, _A     :Dict=2		, _A     :List[Any]=3		, _A     :Union[str, Any]=True		, _A     :Optional[Any]=True		, _A     :Optional[int]=32		, _A     :Any=5		, _A     :int=4		, _A     :int=37		, _A     :int="gelu"		, _A     :str=0.1		, _A     :Optional[Any]=0.1		, _A     :str=10		, _A     :str=0.02		, _A     :List[Any]=3		, _A     :Tuple=0.6		, _A     :Tuple=None		, ) ->	int:







       '''simple docstring'''



       __A						=	parent
       __A						=	batch_size
       __A						=	image_size
       __A						=	patch_size
       __A						=	num_channels
       __A						=	is_training
       __A						=	use_labels
       __A						=	hidden_size
       __A						=	num_hidden_layers
       __A						=	num_attention_heads
       __A						=	intermediate_size
       __A						=	hidden_act
       __A						=	hidden_dropout_prob
       __A						=	attention_probs_dropout_prob
       __A						=	type_sequence_label_size
       __A						=	initializer_range
       __A						=	mask_ratio
       __A						=	scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       __A						=	(image_size // patch_size) ** 2
       __A						=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)






 def 		lowercase_  (	self     :Optional[int]	) ->	Union[str, Any]:







       '''simple docstring'''



       __A						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

       __A						=	None
       if self.use_labels:
             __A						=	ids_tensor([self.batch_size]		, self.type_sequence_label_size	)

       __A						=	self.get_config()

       return config, pixel_values, labels






 def 		lowercase_  (	self     :List[Any]	) ->	str:







       '''simple docstring'''



       return ViTMAEConfig(
           image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, is_decoder=lowerCAmelCase_		, initializer_range=self.initializer_range		, mask_ratio=self.mask_ratio		, )






 def 		lowercase_  (	self     :Any		, _A     :List[str]		, _A     :int		, _A     :List[Any]	) ->	Any:







       '''simple docstring'''



       __A						=	ViTMAEModel(config=lowerCAmelCase_	)
       model.to(lowerCAmelCase_	)
       model.eval()
       __A						=	model(lowerCAmelCase_	)
       self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)	)






 def 		lowercase_  (	self     :Optional[Any]		, _A     :int		, _A     :Union[str, Any]		, _A     :int	) ->	List[Any]:







       '''simple docstring'''



       __A						=	ViTMAEForPreTraining(lowerCAmelCase_	)
       model.to(lowerCAmelCase_	)
       model.eval()
       __A						=	model(lowerCAmelCase_	)
       __A						=	(self.image_size // self.patch_size) ** 2
       __A						=	self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, num_patches, expected_num_channels)	)

       # test greyscale images
       __A						=	1
       __A						=	ViTMAEForPreTraining(lowerCAmelCase_	)
       model.to(lowerCAmelCase_	)
       model.eval()
       __A						=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
       __A						=	model(lowerCAmelCase_	)
       __A						=	self.patch_size**2
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, num_patches, expected_num_channels)	)






 def 		lowercase_  (	self     :str	) ->	Dict:







       '''simple docstring'''



       __A						=	self.prepare_config_and_inputs()
       __A		,					__A		,					__A						=	config_and_inputs
       __A						=	{'pixel_values': pixel_values}
       return config, inputs_dict


@require_torch
class        UpperCamelCase__     (			__lowerCAmelCase					,   __lowerCAmelCase					,   unittest.TestCase):
 UpperCAmelCase__			:			Union[str, Any]   =    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 UpperCAmelCase__			:			List[Any]   =    {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

 UpperCAmelCase__			:			List[Any]   =    False
 UpperCAmelCase__			:			Tuple   =    False
 UpperCAmelCase__			:			int   =    False
 UpperCAmelCase__			:			Any   =    False






 def 		lowercase_  (	self     :Dict	) ->	str:







       '''simple docstring'''



       __A						=	ViTMAEModelTester(self	)
       __A						=	ConfigTester(self		, config_class=lowerCAmelCase_		, has_text_modality=lowerCAmelCase_		, hidden_size=37	)






 def 		lowercase_  (	self     :Optional[int]	) ->	str:







       '''simple docstring'''



       self.config_tester.run_common_tests()






 @unittest.skip(reason='ViTMAE does not use inputs_embeds'	)
 def 		lowercase_  (	self     :List[Any]	) ->	Optional[Any]:







       '''simple docstring'''



       pass






 def 		lowercase_  (	self     :Any	) ->	str:







       '''simple docstring'''



       __A		,					__A						=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A						=	model_class(lowerCAmelCase_	)
             self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)	)
             __A						=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(lowerCAmelCase_		, nn.Linear	)	)






 def 		lowercase_  (	self     :Optional[int]	) ->	Union[str, Any]:







       '''simple docstring'''



       __A		,					__A						=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A						=	model_class(lowerCAmelCase_	)
             __A						=	inspect.signature(model.forward	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __A						=	[*signature.parameters.keys()]

             __A						=	['pixel_values']
             self.assertListEqual(arg_names[:1]		, lowerCAmelCase_	)






 def 		lowercase_  (	self     :Dict	) ->	Union[str, Any]:







       '''simple docstring'''



       __A						=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_	)






 def 		lowercase_  (	self     :Tuple	) ->	Dict:







       '''simple docstring'''



       __A						=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_	)






 def 		lowercase_  (	self     :List[str]		, _A     :int		, _A     :Tuple		, _A     :Optional[Any]	) ->	Any:







       '''simple docstring'''



       np.random.seed(2	)

       __A						=	int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
       __A						=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
       __A						=	torch.from_numpy(lowerCAmelCase_	)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       __A						=	pt_noise

       super().check_pt_tf_models(lowerCAmelCase_		, lowerCAmelCase_		, lowerCAmelCase_	)






 def 		lowercase_  (	self     :List[Any]	) ->	Any:







       '''simple docstring'''



       __A		,					__A						=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A						=	model_class(lowerCAmelCase_	)
             model.to(lowerCAmelCase_	)
             model.eval()
             # make random mask reproducible
             torch.manual_seed(2	)
             with torch.no_grad():
                   __A						=	model(**self._prepare_for_class(lowerCAmelCase_		, lowerCAmelCase_	)	)

             __A						=	outputs[0].cpu().numpy()
             __A						=	0

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(lowerCAmelCase_	)
                   __A						=	model_class.from_pretrained(lowerCAmelCase_	)
                   model.to(lowerCAmelCase_	)
                   # make random mask reproducible
                   torch.manual_seed(2	)
                   with torch.no_grad():
                         __A						=	model(**self._prepare_for_class(lowerCAmelCase_		, lowerCAmelCase_	)	)

                   # Make sure we don't have nans
                   __A						=	after_outputs[0].cpu().numpy()
                   __A						=	0
                   __A						=	np.amax(np.abs(out_a - out_a	)	)
                   self.assertLessEqual(lowerCAmelCase_		, 1E-5	)






 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
 def 		lowercase_  (	self     :List[Any]	) ->	Tuple:







       '''simple docstring'''



       pass






 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
 def 		lowercase_  (	self     :Union[str, Any]	) ->	str:







       '''simple docstring'''



       pass






 @unittest.skip(
     reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
 def 		lowercase_  (	self     :Union[str, Any]	) ->	str:







       '''simple docstring'''



       pass






 @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'	)
 def 		lowercase_  (	self     :Optional[Any]	) ->	Dict:







       '''simple docstring'''



       pass






 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
 def 		lowercase_  (	self     :List[str]	) ->	str:







       '''simple docstring'''



       pass






 @slow
 def 		lowercase_  (	self     :str	) ->	Optional[Any]:







       '''simple docstring'''



       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A						=	ViTMAEModel.from_pretrained(lowerCAmelCase_	)
             self.assertIsNotNone(lowerCAmelCase_	)





def 				snake_case							(						)->	List[str]:







      """simple docstring"""
      __A						=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image


@require_torch
@require_vision
class        UpperCamelCase__     (			unittest.TestCase):






 @cached_property
 def 		lowercase_  (	self     :List[Any]	) ->	int:







       '''simple docstring'''



       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None






 @slow
 def 		lowercase_  (	self     :int	) ->	Any:







       '''simple docstring'''



       np.random.seed(2	)

       __A						=	ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	).to(lowerCAmelCase_	)

       __A						=	self.default_image_processor
       __A						=	prepare_img()
       __A						=	image_processor(images=lowerCAmelCase_		, return_tensors='pt'	).to(lowerCAmelCase_	)

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       __A						=	ViTMAEConfig()
       __A						=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
       __A						=	np.random.uniform(size=(1, num_patches)	)

       # forward pass
       with torch.no_grad():
             __A						=	model(**lowerCAmelCase_		, noise=torch.from_numpy(lowerCAmelCase_	).to(device=lowerCAmelCase_	)	)

       # verify the logits
       __A						=	torch.Size((1, 196, 768)	)
       self.assertEqual(outputs.logits.shape		, lowerCAmelCase_	)

       __A						=	torch.tensor(
           [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]	)

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		, expected_slice.to(lowerCAmelCase_	)		, atol=1E-4	)	)







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 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				)  ->					int:
        _A        =  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_		,				)
        _A        =  TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,				"""trainer_state.json"""							)							).log_history

        if not do_eval:
               return

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

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

               _A        =  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      UpperCAmelCase							(					self							)  ->					Optional[int]:
        self.run_seqaseq_quick()



 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_							)



 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					str:
        self.run_seqaseq_quick(distributed=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      UpperCAmelCase							(					self							)  ->					Dict:
        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      UpperCAmelCase							(					self							)  ->					Optional[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      UpperCAmelCase							(					self							)  ->					Tuple:
        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      UpperCAmelCase							(					self							)  ->					int:
        # 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      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					int:
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        _A        =  {
            # 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},
        }

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



 @slow
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  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
        _A        =  TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,				"""trainer_state.json"""							)							).log_history
        _A        =  [log for log in logs if """eval_loss""" in log.keys()]
        _A        =  eval_metrics[0]
        _A        =  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
        _A        =  os.listdir(lowerCAmelCase_							)
        _A        =  {os.path.basename(lowerCAmelCase_							) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents



 @slow
 @require_bitsandbytes
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        from transformers.training_args import OptimizerNames

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

               _A        =  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
               _A        =  TrainerState.load_from_json(Path(lowerCAmelCase_		,				"""trainer_state.json"""							)							).log_history
               _A        =  int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20							)
               _A        =  int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20							)

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

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

        _A        =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        _A        =  gpu_peak_mem_orig + gpu_alloc_mem_orig
        _A        =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        _A        =  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
        _A        =  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      UpperCAmelCase							(					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		,				)  ->					str:
        _A        =  self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
        _A        =  self.get_auto_remove_tmp_dir()
        _A        =  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()

        _A        =  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()

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

        _A        =  []
        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:
                      _A        =  get_gpu_count()
               _A        =  get_torch_dist_unique_port()
               _A        =  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()
               _A        =  [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:
               _A        =  ["""run_translation.py"""] + args
               with patch.object(lowerCAmelCase_		,				"""argv"""		,				lowerCAmelCase_							):
                      main()

        return output_dir




