
'''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):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=7       ,							A__=3       ,							A__=30       ,							A__=4_00       ,							A__=True       ,							A__=None       ,							A__=True       ,							A__=[0.5, 0.5, 0.5]       ,							A__=[0.5, 0.5, 0.5]       ,							A__=True       ,							A__=1 / 2_55       ,							A__=True       ,							)    ->			str:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _SCREAMING_SNAKE_CASE      =				size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
          _SCREAMING_SNAKE_CASE      =				parent
          _SCREAMING_SNAKE_CASE      =				batch_size
          _SCREAMING_SNAKE_CASE      =				num_channels
          _SCREAMING_SNAKE_CASE      =				min_resolution
          _SCREAMING_SNAKE_CASE      =				max_resolution
          _SCREAMING_SNAKE_CASE      =				do_resize
          _SCREAMING_SNAKE_CASE      =				size
          _SCREAMING_SNAKE_CASE      =				do_normalize
          _SCREAMING_SNAKE_CASE      =				image_mean
          _SCREAMING_SNAKE_CASE      =				image_std
          _SCREAMING_SNAKE_CASE      =				do_rescale
          _SCREAMING_SNAKE_CASE      =				rescale_factor
          _SCREAMING_SNAKE_CASE      =				do_pad






       def        UpperCamelCase				(							self      )    ->			List[str]:
          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        UpperCamelCase				(							self       ,							A__       ,							A__=False      )    ->			List[str]:
          if not batched:
             _SCREAMING_SNAKE_CASE      =				image_inputs[0]
             if isinstance(A__       ,							Image.Image      ):
                _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				image.size
             else:
                _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				image.shape[1], image.shape[2]
             if w < h:
                _SCREAMING_SNAKE_CASE      =				int(self.size["""shortest_edge"""] * h / w      )
                _SCREAMING_SNAKE_CASE      =				self.size["""shortest_edge"""]
             elif w > h:
                _SCREAMING_SNAKE_CASE      =				self.size["""shortest_edge"""]
                _SCREAMING_SNAKE_CASE      =				int(self.size["""shortest_edge"""] * w / h      )
             else:
                _SCREAMING_SNAKE_CASE      =				self.size["""shortest_edge"""]
                _SCREAMING_SNAKE_CASE      =				self.size["""shortest_edge"""]

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

          return expected_height, expected_width






@require_torch
@require_vision
class    _a				(_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ConditionalDetrImageProcessor if is_vision_available() else None






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				ConditionalDetrImageProcessingTester(self      )






       @property
       def        UpperCamelCase				(							self      )    ->			str:
          return self.image_processor_tester.prepare_image_processor_dict()






       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A__       ,							"""image_mean"""      )      )
          self.assertTrue(hasattr(A__       ,							"""image_std"""      )      )
          self.assertTrue(hasattr(A__       ,							"""do_normalize"""      )      )
          self.assertTrue(hasattr(A__       ,							"""do_resize"""      )      )
          self.assertTrue(hasattr(A__       ,							"""size"""      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class.from_dict(self.image_processor_dict      )
          self.assertEqual(image_processor.size       ,							{"""shortest_edge""": 18, """longest_edge""": 13_33}      )
          self.assertEqual(image_processor.do_pad       ,							A__      )

          _SCREAMING_SNAKE_CASE      =				self.image_processing_class.from_dict(
              self.image_processor_dict       ,							size=42       ,							max_size=84       ,							pad_and_return_pixel_mask=A__      )
          self.assertEqual(image_processor.size       ,							{"""shortest_edge""": 42, """longest_edge""": 84}      )
          self.assertEqual(image_processor.do_pad       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Tuple:
          pass






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          _SCREAMING_SNAKE_CASE      =				prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							Image.Image      )

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

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__      )

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

          # Test batched
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__       ,							batched=A__      )

          _SCREAMING_SNAKE_CASE      =				image_processing(A__       ,							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        UpperCamelCase				(							self      )    ->			Dict:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          _SCREAMING_SNAKE_CASE      =				prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=A__       ,							numpify=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							np.ndarray      )

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

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__      )

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

          # Test batched
          _SCREAMING_SNAKE_CASE      =				image_processing(A__       ,							return_tensors="""pt"""      ).pixel_values

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__       ,							batched=A__      )

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






       def        UpperCamelCase				(							self      )    ->			List[str]:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          _SCREAMING_SNAKE_CASE      =				prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=A__       ,							torchify=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							torch.Tensor      )

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

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__      )

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

          # Test batched
          _SCREAMING_SNAKE_CASE      =				image_processing(A__       ,							return_tensors="""pt"""      ).pixel_values

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.image_processor_tester.get_expected_values(A__       ,							batched=A__      )

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






       @slow
       def        UpperCamelCase				(							self      )    ->			Tuple:
          # prepare image and target
          _SCREAMING_SNAKE_CASE      =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
          with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,							"""r"""      ) as f:
             _SCREAMING_SNAKE_CASE      =				json.loads(f.read()      )

          _SCREAMING_SNAKE_CASE      =				{"""image_id""": 3_97_69, """annotations""": target}

          # encode them
          _SCREAMING_SNAKE_CASE      =				ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""      )
          _SCREAMING_SNAKE_CASE      =				image_processing(images=A__       ,							annotations=A__       ,							return_tensors="""pt"""      )

          # verify pixel values
          _SCREAMING_SNAKE_CASE      =				torch.Size([1, 3, 8_00, 10_66]      )
          self.assertEqual(encoding["""pixel_values"""].shape       ,							A__      )

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

          # verify area
          _SCREAMING_SNAKE_CASE      =				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"""]       ,							A__      )      )
          # verify boxes
          _SCREAMING_SNAKE_CASE      =				torch.Size([6, 4]      )
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							A__       ,							atol=1E-3      )      )
          # verify image_id
          _SCREAMING_SNAKE_CASE      =				torch.tensor([3_97_69]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							A__      )      )
          # verify is_crowd
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							A__      )      )
          # verify class_labels
          _SCREAMING_SNAKE_CASE      =				torch.tensor([75, 75, 63, 65, 17, 17]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							A__      )      )
          # verify orig_size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([4_80, 6_40]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							A__      )      )
          # verify size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([8_00, 10_66]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							A__      )      )






       @slow
       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          # prepare image, target and masks_path
          _SCREAMING_SNAKE_CASE      =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
          with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,							"""r"""      ) as f:
             _SCREAMING_SNAKE_CASE      =				json.loads(f.read()      )

          _SCREAMING_SNAKE_CASE      =				{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

          _SCREAMING_SNAKE_CASE      =				pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""      )

          # encode them
          _SCREAMING_SNAKE_CASE      =				ConditionalDetrImageProcessor(format="""coco_panoptic"""      )
          _SCREAMING_SNAKE_CASE      =				image_processing(images=A__       ,							annotations=A__       ,							masks_path=A__       ,							return_tensors="""pt"""      )

          # verify pixel values
          _SCREAMING_SNAKE_CASE      =				torch.Size([1, 3, 8_00, 10_66]      )
          self.assertEqual(encoding["""pixel_values"""].shape       ,							A__      )

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

          # verify area
          _SCREAMING_SNAKE_CASE      =				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"""]       ,							A__      )      )
          # verify boxes
          _SCREAMING_SNAKE_CASE      =				torch.Size([6, 4]      )
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							A__       ,							atol=1E-3      )      )
          # verify image_id
          _SCREAMING_SNAKE_CASE      =				torch.tensor([3_97_69]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							A__      )      )
          # verify is_crowd
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							A__      )      )
          # verify class_labels
          _SCREAMING_SNAKE_CASE      =				torch.tensor([17, 17, 63, 75, 75, 93]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							A__      )      )
          # verify masks
          _SCREAMING_SNAKE_CASE      =				82_28_73
          self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,							A__      )
          # verify orig_size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([4_80, 6_40]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							A__      )      )
          # verify size
          _SCREAMING_SNAKE_CASE      =				torch.tensor([8_00, 10_66]      )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							A__      )      )








'''simple docstring'''



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


UpperCamelCase__      :		Tuple       							=  os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
     from run_translation import main  # noqa


set_seed(42)
UpperCamelCase__      :		Union[str, Any]       							=  "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__      :		str       							=  "sshleifer/tiny-mbart"

@require_torch
class    _a				(_lowerCamelCase):





       """simple docstring"""




       def        UpperCamelCase				(							self       ,							A__=False       ,							A__=None       ,							A__=True       ,							A__=True       ,							A__=True       ,							A__=True       ,							)    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=1       ,							max_len=12       ,							model_name=A__       ,							num_train_epochs=1       ,							distributed=A__       ,							extra_args_str=A__       ,							predict_with_generate=A__       ,							do_train=A__       ,							do_eval=A__       ,							do_predict=A__       ,							)
          _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(os.path.join(A__       ,							"""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"""]       ,							A__      )
             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      )    ->			Optional[Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @require_torch_multi_gpu
       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          self.run_seqaseq_quick(distributed=A__      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			Any:
          self.run_seqaseq_quick(distributed=A__       ,							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      )    ->			Tuple:
          self.run_seqaseq_quick(distributed=A__       ,							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      )    ->			str:
          self.run_seqaseq_quick(distributed=A__       ,							extra_args_str="""--sharded_ddp zero_dp_2"""       ,							predict_with_generate=A__      )






       @unittest.skip("""Requires an update of the env running those tests"""      )
       @require_torch_multi_gpu
       @require_fairscale
       def        UpperCamelCase				(							self      )    ->			List[str]:
          self.run_seqaseq_quick(
              distributed=A__       ,							extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""       ,							predict_with_generate=A__      )






       @require_apex
       @require_torch_gpu
       def        UpperCamelCase				(							self      )    ->			Optional[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=A__       ,							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=A__       ,							extra_args_str="""--fp16 --fp16_backend=apex"""      )






       @parameterized.expand(["""base""", """low""", """high""", """mixed"""]      )
       @require_torch_multi_gpu
       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          # 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(**A__       ,							extra_args_str=data["""extra_args_str"""]      )
          _SCREAMING_SNAKE_CASE      =				len(re.findall(A__       ,							cl.err      )      )
          self.assertEqual(A__       ,							data["""n_matches"""]      )






       @slow
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.run_trainer(
              eval_steps=2       ,							max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=10       ,							distributed=A__       ,							)

          # Check metrics
          _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(os.path.join(A__       ,							"""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"""]       ,							A__      )

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






       @slow
       @require_bitsandbytes
       def        UpperCamelCase				(							self      )    ->			Dict:
          from transformers.training_args import OptimizerNames

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

             _SCREAMING_SNAKE_CASE      =				self.run_trainer(
                 max_len=1_28       ,							model_name=A__       ,							learning_rate=3E-4       ,							num_train_epochs=1       ,							optim=A__       ,							distributed=A__       ,							extra_args_str=A__       ,							do_eval=A__       ,							do_predict=A__       ,							n_gpus_to_use=1       ,							)

             # Check metrics
             _SCREAMING_SNAKE_CASE      =				TrainerState.load_from_json(Path(A__       ,							"""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(
              A__       ,							A__       ,							"""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(
              A__       ,							A__       ,							"""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(
              A__       ,							A__       ,							F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = 3E-3       ,							A__ = "adafactor"       ,							A__ = False       ,							A__ = None       ,							A__ = 0       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = True       ,							A__ = None       ,							)    ->			Dict:
          _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(A__      )}\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(A__      )}\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(A__      )}\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(A__       ,							env=self.get_env()      )
          else:
             _SCREAMING_SNAKE_CASE      =				["""run_translation.py"""] + args
             with patch.object(A__       ,							"""argv"""       ,							A__      ):
                main()

          return output_dir






