Remove duplicated test classes (#14122)

Remove duplicated classes

tests/tests_pytorch/callbacks/progress/test_rich_progress_bar.py

@@ -21,8 +21,7 @@
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ProgressBarBase, RichProgressBar
 from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBarTheme
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
-from tests_pytorch.helpers.datasets import RandomIterableDataset
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from tests_pytorch.helpers.runif import RunIf
 
 

tests/tests_pytorch/callbacks/test_stochastic_weight_avg.py

@@ -26,10 +26,9 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import StochasticWeightAveraging
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from pytorch_lightning.strategies import DDPSpawnStrategy, Strategy
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests_pytorch.helpers.datasets import RandomIterableDataset
 from tests_pytorch.helpers.runif import RunIf
 
 

tests/tests_pytorch/helpers/datasets.py

@@ -19,7 +19,7 @@
 from typing import Optional, Sequence, Tuple
 
 import torch
-from torch.utils.data import Dataset, IterableDataset
+from torch.utils.data import Dataset
 
 
 class MNIST(Dataset):
@@ -212,40 +212,3 @@ def __getitem__(self, idx):
 
     def __len__(self):
         return len(self.y)
-
-
-class RandomDictDataset(Dataset):
-    def __init__(self, size: int, length: int):
-        self.len = length
-        self.data = torch.randn(length, size)
-
-    def __getitem__(self, index):
-        a = self.data[index]
-        b = a + 2
-        return {"a": a, "b": b}
-
-    def __len__(self):
-        return self.len
-
-
-class RandomIterableDataset(IterableDataset):
-    def __init__(self, size: int, count: int):
-        self.count = count
-        self.size = size
-
-    def __iter__(self):
-        for _ in range(self.count):
-            yield torch.randn(self.size)
-
-
-class RandomIterableDatasetWithLen(IterableDataset):
-    def __init__(self, size: int, count: int):
-        self.count = count
-        self.size = size
-
-    def __iter__(self):
-        for _ in range(len(self)):
-            yield torch.randn(self.size)
-
-    def __len__(self):
-        return self.count

tests/tests_pytorch/strategies/test_deepspeed_strategy.py

@@ -28,13 +28,12 @@
 
 from pytorch_lightning import LightningDataModule, LightningModule, Trainer
 from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from pytorch_lightning.plugins import DeepSpeedPrecisionPlugin
 from pytorch_lightning.strategies import DeepSpeedStrategy
 from pytorch_lightning.strategies.deepspeed import _DEEPSPEED_AVAILABLE, LightningDeepSpeedModule
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests_pytorch.helpers.datamodules import ClassifDataModule
-from tests_pytorch.helpers.datasets import RandomIterableDataset
 from tests_pytorch.helpers.runif import RunIf
 
 if _DEEPSPEED_AVAILABLE:

tests/tests_pytorch/trainer/flags/test_val_check_interval.py

@@ -16,10 +16,9 @@
 import pytest
 from torch.utils.data import DataLoader
 
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from pytorch_lightning.trainer.trainer import Trainer
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests_pytorch.helpers.datasets import RandomIterableDataset
 
 
 @pytest.mark.parametrize("max_epochs", [1, 2, 3])

tests/tests_pytorch/trainer/logging_/test_train_loop_logging.py

@@ -28,9 +28,8 @@
 from pytorch_lightning import callbacks, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint, TQDMProgressBar
 from pytorch_lightning.core.module import LightningModule
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomDictDataset
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests_pytorch.helpers.datasets import RandomDictDataset
 from tests_pytorch.helpers.runif import RunIf
 
 

tests/tests_pytorch/trainer/properties/test_estimated_stepping_batches.py

@@ -22,11 +22,10 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks.gradient_accumulation_scheduler import GradientAccumulationScheduler
-from pytorch_lightning.demos.boring_classes import BoringModel
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomIterableDataset
 from pytorch_lightning.strategies.ipu import IPUStrategy
 from pytorch_lightning.utilities import device_parser
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests_pytorch.helpers.datasets import RandomIterableDataset
 from tests_pytorch.helpers.runif import RunIf
 
 

tests/tests_pytorch/trainer/test_dataloaders.py

@@ -25,12 +25,16 @@
 
 from pytorch_lightning import Callback, seed_everything, Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import (
+    BoringModel,
+    RandomDataset,
+    RandomIterableDataset,
+    RandomIterableDatasetWithLen,
+)
 from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.utilities.data import _auto_add_worker_init_fn, has_iterable_dataset, has_len_all_ranks
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests_pytorch.helpers.dataloaders import CustomInfDataloader, CustomNotImplementedErrorDataloader
-from tests_pytorch.helpers.datasets import RandomIterableDataset, RandomIterableDatasetWithLen
 from tests_pytorch.helpers.runif import RunIf
 
 

tests/tests_pytorch/trainer/test_trainer.py

@@ -41,7 +41,12 @@
 from pytorch_lightning.callbacks.fault_tolerance import _FaultToleranceCheckpoint
 from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter
 from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml, save_hparams_to_tags_csv
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import (
+    BoringModel,
+    RandomDataset,
+    RandomIterableDataset,
+    RandomIterableDatasetWithLen,
+)
 from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper, UnrepeatedDistributedSampler
 from pytorch_lightning.strategies import (
@@ -60,7 +65,6 @@
 from pytorch_lightning.utilities.imports import _OMEGACONF_AVAILABLE, _TORCH_GREATER_EQUAL_1_12
 from pytorch_lightning.utilities.seed import seed_everything
 from tests_pytorch.helpers.datamodules import ClassifDataModule
-from tests_pytorch.helpers.datasets import RandomIterableDataset, RandomIterableDatasetWithLen
 from tests_pytorch.helpers.runif import RunIf
 from tests_pytorch.helpers.simple_models import ClassificationModel
 

tests/tests_pytorch/utilities/test_data.py

@@ -6,7 +6,7 @@
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 
 from pytorch_lightning import Trainer
-from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
+from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper
 from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.utilities.data import (
@@ -23,7 +23,6 @@
     warning_cache,
 )
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests_pytorch.helpers.datasets import RandomIterableDataset
 from tests_pytorch.helpers.utils import no_warning_call