




'''simple docstring'''







import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ConditionalDetrImageProcessor


class 			_a  (	unittest.TestCase ):
       def __init__(	self						:	int   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Dict=7   ,				lowercase						:	Union[str, Any]=3   ,				lowercase						:	int=30   ,				lowercase						:	List[str]=400   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	Any=[0.5, 0.5, 0.5]   ,				lowercase						:	int=[0.5, 0.5, 0.5]   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Tuple=1 / 255   ,				lowercase						:	List[str]=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		min_resolution
           UpperCAmelCase			       =		max_resolution
           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean
           UpperCAmelCase			       =		image_std
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_pad
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }







       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	Dict   ,				lowercase						:	Optional[Any]=False				):



           '''simple docstring'''

           if not batched:
               UpperCAmelCase			       =		image_inputs[0]
               if isinstance(lowercase   ,				Image.Image				):
                   UpperCAmelCase       ,     UpperCAmelCase			       =		image.size
               else:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		image.shape[1], image.shape[2]
               if w < h:
                   UpperCAmelCase			       =		int(self.size['''shortest_edge'''] * h / w				)
                   UpperCAmelCase			       =		self.size['''shortest_edge''']
               elif w > h:
                   UpperCAmelCase			       =		self.size['''shortest_edge''']
                   UpperCAmelCase			       =		int(self.size['''shortest_edge'''] * w / h				)
               else:
                   UpperCAmelCase			       =		self.size['''shortest_edge''']
                   UpperCAmelCase			       =		self.size['''shortest_edge''']

           else:
               UpperCAmelCase			       =		[]
               for image in image_inputs:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		self.get_expected_values([image]				)
                   expected_values.append((expected_height, expected_width)				)
               UpperCAmelCase			       =		max(lowercase   ,				key=lambda lowercase				: item[0]				)[0]
               UpperCAmelCase			       =		max(lowercase   ,				key=lambda lowercase				: item[1]				)[1]

           return expected_height, expected_width






@require_torch
@require_vision
class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     List[Any]   				=					ConditionalDetrImageProcessor if is_vision_available() else None
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		ConditionalDetrImageProcessingTester(self				)
       @property
       def     A      (	self						:	int				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(lowercase   ,				'''image_mean'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''image_std'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''do_normalize'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''do_resize'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''size'''				)				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
           self.assertEqual(image_processor.do_pad   ,				lowercase				)

           UpperCAmelCase			       =		self.image_processing_class.from_dict(
               self.image_processor_dict   ,				size=42   ,				max_size=84   ,				pad_and_return_pixel_mask=lowercase				)
           self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 42, '''longest_edge''': 84}				)
           self.assertEqual(image_processor.do_pad   ,				lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				Image.Image				)

           # Test not batched input
           UpperCAmelCase			       =		image_processing(image_inputs[0]   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase				)

           self.assertEqual(
               encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

           # Test batched
           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase   ,				batched=lowercase				)

           UpperCAmelCase			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values
           self.assertEqual(
               encoded_images.shape   ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase   ,				numpify=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				np.ndarray				)

           # Test not batched input
           UpperCAmelCase			       =		image_processing(image_inputs[0]   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase				)

           self.assertEqual(
               encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

           # Test batched
           UpperCAmelCase			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase   ,				batched=lowercase				)

           self.assertEqual(
               encoded_images.shape   ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase   ,				torchify=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				torch.Tensor				)

           # Test not batched input
           UpperCAmelCase			       =		image_processing(image_inputs[0]   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase				)

           self.assertEqual(
               encoded_images.shape   ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,				)

           # Test batched
           UpperCAmelCase			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase   ,				batched=lowercase				)

           self.assertEqual(
               encoded_images.shape   ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,				)
       @slow
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,				'''r'''				) as f:
               UpperCAmelCase			       =		json.loads(f.read()				)

           UpperCAmelCase			       =		{'''image_id''': 39_769, '''annotations''': target}

           # encode them
           UpperCAmelCase			       =		ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''				)
           UpperCAmelCase			       =		image_processing(images=lowercase   ,				annotations=lowercase   ,				return_tensors='''pt'''				)

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor([0.2796, 0.3138, 0.3481]				)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,				lowercase   ,				atol=1E-4				)				)

           # verify area
           UpperCAmelCase			       =		torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,				lowercase				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowercase				)
           UpperCAmelCase			       =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowercase   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowercase				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowercase				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([75, 75, 63, 65, 17, 17]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowercase				)				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowercase				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowercase				)				)







       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,				'''r'''				) as f:
               UpperCAmelCase			       =		json.loads(f.read()				)

           UpperCAmelCase			       =		{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

           UpperCAmelCase			       =		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

           # encode them
           UpperCAmelCase			       =		ConditionalDetrImageProcessor(format='''coco_panoptic'''				)
           UpperCAmelCase			       =		image_processing(images=lowercase   ,				annotations=lowercase   ,				masks_path=lowercase   ,				return_tensors='''pt'''				)

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor([0.2796, 0.3138, 0.3481]				)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,				lowercase   ,				atol=1E-4				)				)

           # verify area
           UpperCAmelCase			       =		torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,				lowercase				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowercase				)
           UpperCAmelCase			       =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowercase   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowercase				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowercase				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([17, 17, 63, 75, 75, 93]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowercase				)				)
           # verify masks
           UpperCAmelCase			       =		822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,				lowercase				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowercase				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowercase				)				)






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__   :			Optional[int]												=    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__   :			Any												=    "sshleifer/student_marian_en_ro_6_1"
SCREAMING_SNAKE_CASE__   :			Tuple												=    "sshleifer/tiny-mbart"

@require_torch
class    lowerCAmelCase__     (	__lowercase			):






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple=False							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=True							,			)     ->							Optional[int]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=1							,			max_len=12							,			model_name=SCREAMING_SNAKE_CASE__							,			num_train_epochs=1							,			distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str=SCREAMING_SNAKE_CASE__							,			predict_with_generate=SCREAMING_SNAKE_CASE__							,			do_train=SCREAMING_SNAKE_CASE__							,			do_eval=SCREAMING_SNAKE_CASE__							,			do_predict=SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history

						if not do_eval:
										return

						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]

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

										__lowerCamelCase    					=	eval_metrics[-1]
										assert isinstance(last_step_stats['''eval_bleu''']							,			SCREAMING_SNAKE_CASE__  )
										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]  )     ->							int:
						self.run_seqaseq_quick()






		@require_torch_multi_gpu
		def  __A       (		self		:							int  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@require_torch_multi_gpu
		def  __A       (		self		:							Optional[Any]  )     ->							Tuple:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Dict  )     ->							Tuple:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			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[int]  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			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		:							Tuple  )     ->							Any:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp zero_dp_2'''							,			predict_with_generate=SCREAMING_SNAKE_CASE__  )






		@unittest.skip('''Requires an update of the env running those tests'''  )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A       (		self		:							Dict  )     ->							Tuple:
						self.run_seqaseq_quick(
						    distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''							,			predict_with_generate=SCREAMING_SNAKE_CASE__  )






		@require_apex
		@require_torch_gpu
		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						# 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=SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			extra_args_str='''--fp16 --fp16_backend=apex'''  )






		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']  )
		@require_torch_multi_gpu
		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Optional[Any]:
						# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
						__lowerCamelCase    					=	{
						    # 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},
						}

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






		@slow
		def  __A       (		self		:							Any  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=2							,			max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=10							,			distributed=SCREAMING_SNAKE_CASE__							,			)

						# Check metrics
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]
						__lowerCamelCase    					=	eval_metrics[0]
						__lowerCamelCase    					=	eval_metrics[-1]

						assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
						assert isinstance(last_step_stats['''eval_bleu''']							,			SCREAMING_SNAKE_CASE__  )

						# test if do_predict saves generations and metrics
						__lowerCamelCase    					=	os.listdir(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	{os.path.basename(SCREAMING_SNAKE_CASE__  ) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents






		@slow
		@require_bitsandbytes
		def  __A       (		self		:							Optional[int]  )     ->							str:
						from transformers.training_args import OptimizerNames

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

										__lowerCamelCase    					=	self.run_trainer(
										    max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=1							,			optim=SCREAMING_SNAKE_CASE__							,			distributed=SCREAMING_SNAKE_CASE__							,			extra_args_str=SCREAMING_SNAKE_CASE__							,			do_eval=SCREAMING_SNAKE_CASE__							,			do_predict=SCREAMING_SNAKE_CASE__							,			n_gpus_to_use=1							,			)

										# Check metrics
										__lowerCamelCase    					=	TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20  )
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20  )

										__lowerCamelCase    					=	logs[0]['''train_loss''']
										return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

						__lowerCamelCase    					=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						__lowerCamelCase    					=	gpu_peak_mem_orig + gpu_alloc_mem_orig
						__lowerCamelCase    					=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	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(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''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(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			'''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(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''  )







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							float = 3e-3							,			SCREAMING_SNAKE_CASE__		:							str = "adafactor"							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							int = 0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							int = None							,			)     ->							List[Any]:
						__lowerCamelCase    					=	self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
						__lowerCamelCase    					=	self.get_auto_remove_tmp_dir()
						__lowerCamelCase    					=	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(SCREAMING_SNAKE_CASE__  )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(SCREAMING_SNAKE_CASE__  )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

						__lowerCamelCase    					=	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(SCREAMING_SNAKE_CASE__  )}
        '''.split()

						__lowerCamelCase    					=	'''
            --do_predict
        '''.split()

						__lowerCamelCase    					=	[]
						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:
														__lowerCamelCase    					=	get_gpu_count()
										__lowerCamelCase    					=	get_torch_dist_unique_port()
										__lowerCamelCase    					=	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()
										__lowerCamelCase    					=	[sys.executable] + distributed_args + args
										# keep for quick debug
										# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
										execute_subprocess_async(SCREAMING_SNAKE_CASE__							,			env=self.get_env()  )
						else:
										__lowerCamelCase    					=	['''run_translation.py'''] + args
										with patch.object(SCREAMING_SNAKE_CASE__							,			'''argv'''							,			SCREAMING_SNAKE_CASE__  ):
														main()

						return output_dir



