Repository: whittle-org/whittle (language: python)

Issue:
[Typing] Use `from __futures__ import annotations`
This code here:

```python
def configure_pad_token(
    tokenizer: "PreTrainedTokenizerBase",
    model_config: Optional["PretrainedConfig"] = None,
) -> "PreTrainedTokenizerBase":
```

Could be updated to not use `"PreTrainedTokenizerBase"` as a string by including `from __futures__ import annotations` as an import.


```python
def configure_pad_token(
    tokenizer: PreTrainedTokenizerBase,
    model_config: PretrainedConfig | None = None,
) -> PreTrainedTokenizerBase:
```

You can configure `ruff` to do this import automatically.

https://github.com/automl/amltk/blob/f3b96e4a95c1bc4115c3a7d7a2b255794b615731/pyproject.toml#L236-L243

This also removes the need to do `from typing import Optional, Dict, Tuple, List` and instead types can be replaced with `dict[str, whatever]`, `tuple[one, two]`, `list[a]` and `int | None`. Pretty sure `ruff` can also automatically fix these with the right setting


Discussion / hints:
that's great, thank you!

Fix patch:
diff --git a/.github/workflows/run-examples.yml b/.github/workflows/run-examples.yml
index 5b9ff6d..345ca27 100644
--- a/.github/workflows/run-examples.yml
+++ b/.github/workflows/run-examples.yml
@@ -10,7 +10,7 @@ jobs:
     strategy:
       matrix:
         # Run in all these versions of Python
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: [ "3.9", "3.10", "3.11" ]
     steps:
       - uses: actions/checkout@v4
       - name: Install poetry
@@ -24,6 +24,4 @@ jobs:
         run: poetry install
       - name: Run sinc example
         run: |
-          poetry run python examples/sinc/sinc_nas.py
-          export PYTHONPATH=.
-          poetry run python examples/sinc/search.py
\ No newline at end of file
+          poetry run python examples/sinc/sinc_nas.py
\ No newline at end of file
diff --git a/examples/sinc/estimate_efficiency.py b/examples/sinc/estimate_efficiency.py
index c0f337f..df6d71e 100644
--- a/examples/sinc/estimate_efficiency.py
+++ b/examples/sinc/estimate_efficiency.py
@@ -1,2 +1,5 @@
+from __future__ import annotations
+
+
 def compute_mac_linear_layer(in_features, out_features):
     return in_features * out_features
diff --git a/examples/sinc/model.py b/examples/sinc/model.py
index c3eb8dd..7e2537b 100644
--- a/examples/sinc/model.py
+++ b/examples/sinc/model.py
@@ -1,14 +1,14 @@
+from __future__ import annotations
+
 import torch
 import torch.nn as nn
 
-from syne_tune.config_space import randint
-
 from whittle.modules import Linear
 
 
 class MLP(nn.Module):
     def __init__(self, input_dim, hidden_dim=200, device="cuda"):
-        super(MLP, self).__init__()
+        super().__init__()
 
         self.hidden_dim = hidden_dim
         self.input_layer = Linear(input_dim, hidden_dim)
diff --git a/examples/sinc/search.py b/examples/sinc/search.py
index 62b77f7..cb4c949 100644
--- a/examples/sinc/search.py
+++ b/examples/sinc/search.py
@@ -1,18 +1,18 @@
-import numpy as np
-import torch
-import matplotlib.pyplot as plt
+from __future__ import annotations
 
 from argparse import ArgumentParser
 from pathlib import Path
-from torch.utils.data import DataLoader
 
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
 from syne_tune.config_space import randint
-from whittle.search import multi_objective_search
-from examples.sinc.estimate_efficiency import compute_mac_linear_layer
-
-from sinc_nas import validate, f
-from model import MLP
+from torch.utils.data import DataLoader
 
+from .model import MLP
+from .estimate_efficiency import compute_mac_linear_layer
+from .sinc_nas import f, validate
+from whittle.search import multi_objective_search
 
 if __name__ == "__main__":
     parser = ArgumentParser()
diff --git a/examples/sinc/sinc_nas.py b/examples/sinc/sinc_nas.py
index c8439f7..096ffcb 100644
--- a/examples/sinc/sinc_nas.py
+++ b/examples/sinc/sinc_nas.py
@@ -1,19 +1,20 @@
+from __future__ import annotations
+
+import json
 import os
+from argparse import ArgumentParser
+from pathlib import Path
 
 import numpy as np
-import json
 import torch
 import torch.nn as nn
-
-from argparse import ArgumentParser
-from pathlib import Path
-from torch.utils.data import DataLoader
 from syne_tune.config_space import randint
 from syne_tune.report import Reporter
-from whittle.training_strategies import SandwichStrategy
-from whittle.sampling.random_sampler import RandomSampler
+from torch.utils.data import DataLoader
 
-from model import MLP
+from .model import MLP
+from whittle.sampling.random_sampler import RandomSampler
+from whittle.training_strategies import SandwichStrategy
 
 report = Reporter()
 
diff --git a/poetry.lock b/poetry.lock
index 9db1d93..6a94b4c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -53,92 +53,104 @@ test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "py
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
 testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.3.4"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.3.4-py3-none-any.whl", hash = "sha256:40a16ceffcf1fc9e142fd488123b2e218abc4188cf12ac20c67200e1579baa42"},
+    {file = "aiohappyeyeballs-2.3.4.tar.gz", hash = "sha256:7e1ae8399c320a8adec76f6c919ed5ceae6edd4c3672f4d9eae2b27e37c80ff6"},
+]
+
 [[package]]
 name = "aiohttp"
-version = "3.9.5"
+version = "3.10.0"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
 async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
@@ -147,7 +159,7 @@ multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -272,17 +284,17 @@ scipy = "*"
 
 [[package]]
 name = "boto3"
-version = "1.34.147"
+version = "1.34.152"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "boto3-1.34.152-py3-none-any.whl", hash = "sha256:92726a5be7083fd62585f8de251251ec7e53f4c7ee69c9c3168873fe979ec511"},
+    {file = "boto3-1.34.152.tar.gz", hash = "sha256:d34d7efe608b98cc10cfb43983bd2c511eb32efd5780ef72b171a3e3325462ff"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.147,<1.35.0"
+botocore = ">=1.34.152,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -291,13 +303,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.147"
+version = "1.34.152"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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@@ -734,26 +746,6 @@ files = [
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-trio = ["trio (>=0.23)"]
-wmi = ["wmi (>=1.5.1)"]
-
 [[package]]
 name = "docstring-parser"
 version = "0.16"
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-
 [[package]]
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 version = "0.4.2"
@@ -831,45 +808,23 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
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+version = "0.112.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
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 python-versions = ">=3.8"
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 [[package]]
 name = "filelock"
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 [package.extras]
-protobuf = ["grpcio-tools (>=1.65.1)"]
+protobuf = ["grpcio-tools (>=1.65.2)"]
 
 [[package]]
 name = "h11"
@@ -1257,54 +1212,6 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 trio = ["trio (>=0.22.0,<0.26.0)"]
 
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-
-[package.extras]
-test = ["Cython (>=0.29.24,<0.30.0)"]
-
 [[package]]
 name = "httpx"
 version = "0.27.0"
@@ -1331,13 +1238,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.24.2"
+version = "0.24.5"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2023,30 +1930,6 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
-[[package]]
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-version = "3.0.0"
-description = "Python port of markdown-it. Markdown parsing, done right!"
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-
-[package.extras]
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-code-style = ["pre-commit (>=3.0,<4.0)"]
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-linkify = ["linkify-it-py (>=1,<3)"]
-plugins = ["mdit-py-plugins"]
-profiling = ["gprof2dot"]
-rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
-testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
-
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -2186,17 +2069,6 @@ chardet = ">=3.0.4,<6"
 [package.extras]
 test = ["Faker (>=1.0.2)", "pytest (>=6.0.1)", "pytest-md-report (>=0.1)"]
 
-[[package]]
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-description = "Markdown URL utilities"
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-python-versions = ">=3.7"
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-
 [[package]]
 name = "mkl"
 version = "2021.4.0"
@@ -2628,14 +2500,13 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.5.82"
+version = "12.6.20"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -2925,13 +2796,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
 
 [[package]]
 name = "pre-commit"
-version = "3.7.1"
+version = "3.8.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
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 [[package]]
@@ -3591,24 +3420,6 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
-[[package]]
-name = "rich"
-version = "13.7.1"
-description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-optional = false
-python-versions = ">=3.7.0"
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-[package.dependencies]
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-pygments = ">=2.13.0,<3.0.0"
-
-[package.extras]
-jupyter = ["ipywidgets (>=7.5.1,<9)"]
-
 [[package]]
 name = "rouge-score"
 version = "0.1.2"
@@ -3625,6 +3436,33 @@ nltk = "*"
 numpy = "*"
 six = ">=1.14.0"
 
+[[package]]
+name = "ruff"
+version = "0.5.5"
+description = "An extremely fast Python linter and code formatter, written in Rust."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "s3transfer"
 version = "0.10.2"
@@ -3938,13 +3776,13 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "71.1.0"
+version = "72.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -3952,17 +3790,6 @@ core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.te
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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-[[package]]
-name = "shellingham"
-version = "1.5.4"
-description = "Tool to Detect Surrounding Shell"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -4471,13 +4298,13 @@ dev = ["twine"]
 
 [[package]]
 name = "transformers"
-version = "4.43.2"
+version = "4.43.3"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -4581,23 +4408,6 @@ pytz = {version = ">=2018.9", optional = true, markers = "extra == \"datetime\""
 datetime = ["packaging", "python-dateutil (>=2.8.0,<3.0.0)", "pytz (>=2018.9)"]
 test = ["packaging", "pytest (>=6.0.1)", "python-dateutil (>=2.8.0,<3.0.0)", "pytz (>=2018.9)", "tcolorpy"]
 
-[[package]]
-name = "typer"
-version = "0.12.3"
-description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
-optional = false
-python-versions = ">=3.7"
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-shellingham = ">=1.3.0"
-typing-extensions = ">=3.7.4.3"
-
 [[package]]
 name = "typeshed-client"
 version = "2.7.0"
@@ -4670,74 +4480,23 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "uvicorn"
-version = "0.30.3"
+version = "0.30.4"
 description = "The lightning-fast ASGI server."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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-watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
-websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
 
 [package.extras]
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-description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
-optional = false
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-    {file = "websockets-12.0.tar.gz", hash = "sha256:81df9cbcbb6c260de1e007e58c011bfebe2dafc8435107b0537f393dd38c8b1b"},
-]
-
 [[package]]
 name = "werkzeug"
 version = "3.0.3"
@@ -5382,4 +4973,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9, <3.12"
-content-hash = "6bcc8bfef33bdcf88efbebef0d747e4551d66b7db86926712548bbf2aff02e0b"
+content-hash = "9c63d1fb927da0ca7d43b0f199f0cfde45b8e690c996b028c4d103bccb3e408c"
diff --git a/pyproject.toml b/pyproject.toml
index b3bc408..e8083ef 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ torchvision = "^0.18.1"
 [tool.poetry.dev-dependencies]
 pre-commit = "^3.7.1"
 pytest = "^8.2.2"
-
+ruff = "^0.5.5"
 
 [build-system]
 requires = ["poetry-core"]
@@ -68,7 +68,7 @@ target-version = "py39"
 # Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
 # Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
 # McCabe complexity (`C901`) by default.
-select = ["E4", "E7", "E9", "F"]
+select = ["E4", "E7", "E9", "F", "UP"]
 ignore = []
 
 # Allow fix for all enabled rules (when `--fix`) is provided.
@@ -97,3 +97,11 @@ line-ending = "auto"
 # This is currently disabled by default, but it is planned for this
 # to be opt-out in the future.
 docstring-code-format = false
+
+[tool.ruff.lint.isort]
+known-first-party = ["whittle", "examples", "sinc"]
+no-lines-before = ["future"]
+required-imports = ["from __future__ import annotations"]
+combine-as-imports = true
+extra-standard-library = ["typing_extensions"]
+force-wrap-aliases = true
diff --git a/whittle/eval/utils.py b/whittle/eval/utils.py
index 72fd7b4..8e0efc4 100644
--- a/whittle/eval/utils.py
+++ b/whittle/eval/utils.py
@@ -1,11 +1,14 @@
+from __future__ import annotations
+
 import json
 import os
 from pathlib import Path
 from pprint import pprint
-from typing import Optional, Union
+
 import torch
-from whittle.models.gpt import GPT
+
 from whittle.eval.whittle_llms import WhittleLM
+from whittle.models.gpt import GPT
 
 
 def prepare_results(results, save_filepath, print_results=True):
@@ -22,17 +25,17 @@ def prepare_results(results, save_filepath, print_results=True):
 
 def convert_and_evaluate(
     model: GPT,
-    tasks: Optional[str] = None,
+    tasks: str | None = None,
     out_dir=None,
     force_conversion: bool = False,
-    num_fewshot: Optional[int] = None,
-    batch_size: Union[int, str] = 1,
-    device: Optional[str] = None,
-    dtype: Optional[Union[str, torch.dtype]] = None,
-    limit: Optional[float] = None,
+    num_fewshot: int | None = None,
+    batch_size: int | str = 1,
+    device: str | None = None,
+    dtype: str | torch.dtype | None = None,
+    limit: float | None = None,
     seed: int = 1234,
-    save_filepath: Optional[Path] = None,
-    access_token: Optional[str] = None,
+    save_filepath: Path | None = None,
+    access_token: str | None = None,
 ) -> None:
     """Evaluate a model with the LM Evaluation Harness.
 
diff --git a/whittle/eval/whittle_llms.py b/whittle/eval/whittle_llms.py
index 6d785c1..2ee01bc 100644
--- a/whittle/eval/whittle_llms.py
+++ b/whittle/eval/whittle_llms.py
@@ -1,18 +1,15 @@
+from __future__ import annotations
+
 import copy
-from typing import Dict, List, Literal, Optional, Tuple, Union
-from tqdm import tqdm
+from typing import Literal
+
 import torch
 import torch.nn.functional as F
 import transformers
 from accelerate import (
     find_executable_batch_size,
 )
-
-from transformers.models.auto.modeling_auto import (
-    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
-    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
-)
-
+from litgpt.generate.base import generate
 from lm_eval import utils
 from lm_eval.api.instance import Instance
 from lm_eval.api.model import TemplateLM
@@ -22,9 +19,14 @@
     clear_torch_cache,
     pad_and_concat,
 )
-from litgpt.generate.base import generate
+from tqdm import tqdm
 from transformers import PreTrainedTokenizerBase
 from transformers.configuration_utils import PretrainedConfig
+from transformers.models.auto.modeling_auto import (
+    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
+    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+)
+
 from whittle.models.gpt import GPT
 
 eval_logger = utils.eval_logger
@@ -56,9 +58,9 @@ def _get_accelerate_args(
 
 
 def configure_pad_token(
-    tokenizer: "PreTrainedTokenizerBase",
-    model_config: Optional["PretrainedConfig"] = None,
-) -> "PreTrainedTokenizerBase":
+    tokenizer: PreTrainedTokenizerBase,
+    model_config: PretrainedConfig | None = None,
+) -> PreTrainedTokenizerBase:
     """
     This function checks if the (Hugging Face) tokenizer has a padding token and sets it if not present.
     Some tokenizers require special handling.
@@ -112,39 +114,36 @@ class WhittleLM(TemplateLM):
     def __init__(
         self,
         pretrained: GPT,
-        backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
+        backend: Literal["default", "causal", "seq2seq"] | None = "default",
         # override whether the model should be treated as decoder-only (causal) or encoder-decoder (seq2seq)
-        revision: Optional[str] = "main",
+        revision: str | None = "main",
         subfolder=None,
-        tokenizer: Optional[
-            Union[
-                str,
-                transformers.PreTrainedTokenizer,
-                transformers.PreTrainedTokenizerFast,
-            ]
-        ] = None,
-        truncation: Optional[bool] = False,
+        tokenizer: str
+        | transformers.PreTrainedTokenizer
+        | transformers.PreTrainedTokenizerFast
+        | None = None,
+        truncation: bool | None = False,
         logits_cache: bool = True,
         max_length=None,
         device="cuda",
         dtype="auto",
         batch_size=1,
         max_batch_size=64,
-        trust_remote_code: Optional[bool] = False,
-        use_fast_tokenizer: Optional[bool] = True,
-        add_bos_token: Optional[bool] = False,
-        prefix_token_id: Optional[int] = None,
+        trust_remote_code: bool | None = False,
+        use_fast_tokenizer: bool | None = True,
+        add_bos_token: bool | None = False,
+        prefix_token_id: int | None = None,
         # arguments used for splitting a model across GPUs naively.
         # only used if `parallelize=True`.
-        parallelize: Optional[bool] = False,
+        parallelize: bool | None = False,
         device_map_option="auto",
         max_memory_per_gpu=None,
         max_cpu_memory=None,
         offload_folder="./offload",
         # PEFT, delta weights and quantization options
-        peft: Optional[str] = None,
-        delta: Optional[str] = None,
-        autogptq: Optional[Union[bool, str]] = False,
+        peft: str | None = None,
+        delta: str | None = None,
+        autogptq: bool | str | None = False,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -210,7 +209,7 @@ def __init__(
         self.peft = peft
         self.revision = revision
         self.batch_schedule: float = 1
-        self.batch_sizes: Dict = {}
+        self.batch_sizes: dict = {}
         self.max_batch_size = max_batch_size
 
         if str(batch_size).startswith("auto"):
@@ -306,8 +305,8 @@ def chat_template(self) -> str:
 
     def _get_backend(
         self,
-        config: Union[transformers.PretrainedConfig, transformers.AutoConfig],
-        backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
+        config: transformers.PretrainedConfig | transformers.AutoConfig,
+        backend: Literal["default", "causal", "seq2seq"] | None = "default",
         trust_remote_code=False,
     ) -> None:
         """
@@ -358,17 +357,14 @@ def _get_backend(
 
     def _create_tokenizer(
         self,
-        pretrained: Union[str, transformers.PreTrainedModel],
-        tokenizer: Optional[
-            Union[
-                str,
-                transformers.PreTrainedTokenizer,
-                transformers.PreTrainedTokenizerFast,
-            ]
-        ],
-        revision: Optional[str] = "main",
-        trust_remote_code: Optional[bool] = False,
-        use_fast_tokenizer: Optional[bool] = True,
+        pretrained: str | transformers.PreTrainedModel,
+        tokenizer: str
+        | transformers.PreTrainedTokenizer
+        | transformers.PreTrainedTokenizerFast
+        | None,
+        revision: str | None = "main",
+        trust_remote_code: bool | None = False,
+        use_fast_tokenizer: bool | None = True,
     ) -> None:
         """
         Helper method during initialization.
@@ -464,7 +460,7 @@ def forward_batch(batch_size):
 
     def tok_encode(
         self, string: str, left_truncate_len=None, add_special_tokens=None
-    ) -> List[int]:
+    ) -> list[int]:
         """ """
         # default for None - empty dict, use predefined tokenizer param
         # used for all models except for CausalLM or predefined value
@@ -494,7 +490,7 @@ def tok_batch_encode(
         padding_side="left",
         left_truncate_len=None,
         truncation=False,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         # encode a batch of strings. converts to tensors and pads automatically, unlike tok_encode.
         old_padding_side = self.tokenizer.padding_side
         self.tokenizer.padding_side = padding_side
@@ -611,8 +607,8 @@ def _select_cont_toks(
         return logits
 
     def loglikelihood_rolling(
-        self, requests: List[Instance], disable_tqdm: bool = False
-    ) -> List[float]:
+        self, requests: list[Instance], disable_tqdm: bool = False
+    ) -> list[float]:
         loglikelihoods = []
 
         adaptive_batch_size = None
@@ -689,14 +685,14 @@ def _batch_scheduler(self, pos, n_reordered_requests):
 
     def _loglikelihood_tokens(
         self,
-        requests: List[Tuple[Tuple[str, str], List[int], List[int]]],
+        requests: list[tuple[tuple[str, str], list[int], list[int]]],
         disable_tqdm=False,
         override_bs=None,
     ):
         # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
         res = []
 
-        def _collate(req: Tuple[Tuple[str, str], List[int], List[int]]):
+        def _collate(req: tuple[tuple[str, str], list[int], list[int]]):
             """Defines the key for the sorted method"""
             # the negative sign on len(toks) sorts descending - this has a few advantages:
             # - time estimates will always be over not underestimates, which is more useful for planning
@@ -708,7 +704,7 @@ def _collate(req: Tuple[Tuple[str, str], List[int], List[int]]):
             toks = req[1] + req[2]
             return -len(toks), tuple(toks)
 
-        def _lookup_one_token_cont(req: Tuple[Tuple[str, str], List[int], List[int]]):
+        def _lookup_one_token_cont(req: tuple[tuple[str, str], list[int], list[int]]):
             """Defines the key to group and lookup one-token continuations"""
             # Use with group_by="contexts" (optional)"
             # allows for the creation of a lookup, so we can reuse logits in case of one-token continuations.
@@ -919,12 +915,12 @@ def set_sub_network(
         )
 
     def generate_until(
-        self, requests: List[Instance], disable_tqdm: bool = False
-    ) -> List[str]:
+        self, requests: list[Instance], disable_tqdm: bool = False
+    ) -> list[str]:
         res = []
 
         # print(requests)
-        def _collate(req: Tuple[str, dict]):
+        def _collate(req: tuple[str, dict]):
             """Defines the key for the sorted method"""
             # the negative sign on len(toks) sorts descending - this has a few advantages:
             # - time estimates will always be over not underestimates, which is more useful for planning
@@ -1053,7 +1049,7 @@ def _collate(req: Tuple[str, dict]):
 
         return res
 
-    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+    def apply_chat_template(self, chat_history: list[dict[str, str]]) -> str:
         """
         Method to apply a chat template to a list of chat history between user and model.
         """
diff --git a/whittle/extract_subnetworks.py b/whittle/extract_subnetworks.py
index efaa0a2..0a7fbbc 100644
--- a/whittle/extract_subnetworks.py
+++ b/whittle/extract_subnetworks.py
@@ -1,14 +1,16 @@
+from __future__ import annotations
+
 from collections import OrderedDict
+from copy import deepcopy
 
 import torch
 import torch.nn as nn
-from copy import deepcopy
 
 
 class IdentityAttention(nn.Module):
     def __init__(self, model_type=None):
         self.model_type = model_type
-        super(IdentityAttention, self).__init__()
+        super().__init__()
 
     def forward(self, *input, **kwargs):
         if self.model_type == "distilbert-base-cased":
@@ -20,7 +22,7 @@ def forward(self, *input, **kwargs):
 class IdentityFFN(nn.Module):
     def __init__(self, model_type=None):
         self.model_type = model_type
-        super(IdentityFFN, self).__init__()
+        super().__init__()
 
     def forward(self, hidden_states):
         return hidden_states
@@ -29,7 +31,7 @@ def forward(self, hidden_states):
 class IdentityOutput(nn.Module):
     def __init__(self, model_type=None):
         self.model_type = model_type
-        super(IdentityOutput, self).__init__()
+        super().__init__()
 
     def forward(self, hidden_states, input_tensor):
         return hidden_states
@@ -148,6 +150,7 @@ def get_final_bert_model(original_model, new_model_config):
 if __name__ == "__main__":
     from transformers import AutoModelForSequenceClassification, AutoTokenizer
     from transformers.models.bert.modeling_bert import BertConfig
+
     from benchmarks.plm_pruning.mask import mask_bert
 
     model_type = "bert-base-uncased"
diff --git a/whittle/loss/__init__.py b/whittle/loss/__init__.py
index d755380..65aa841 100644
--- a/whittle/loss/__init__.py
+++ b/whittle/loss/__init__.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from .kd_loss import DistillLoss
 
 __all__ = ["DistillLoss"]
diff --git a/whittle/loss/kd_loss.py b/whittle/loss/kd_loss.py
index 422ae1c..82f22b0 100644
--- a/whittle/loss/kd_loss.py
+++ b/whittle/loss/kd_loss.py
@@ -1,5 +1,7 @@
-from torch import nn
+from __future__ import annotations
+
 import torch.nn.functional as F
+from torch import nn
 
 
 class DistillLoss(nn.Module):
diff --git a/whittle/metrics/magnitude.py b/whittle/metrics/magnitude.py
index ede3afe..1419b8d 100644
--- a/whittle/metrics/magnitude.py
+++ b/whittle/metrics/magnitude.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import torch
 
 
diff --git a/whittle/metrics/parameters.py b/whittle/metrics/parameters.py
index b68b93a..852162f 100644
--- a/whittle/metrics/parameters.py
+++ b/whittle/metrics/parameters.py
@@ -1,3 +1,4 @@
+from __future__ import annotations
 import logging
 
 import torch.nn as nn
diff --git a/whittle/models/gpt/__init__.py b/whittle/models/gpt/__init__.py
index 53468b2..6ec172a 100644
--- a/whittle/models/gpt/__init__.py
+++ b/whittle/models/gpt/__init__.py
@@ -1,12 +1,13 @@
 # Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
+from __future__ import annotations
 
-import re
 import logging
+import re
 
-from whittle.models.gpt.model import GPT
+from lightning_utilities.core.imports import RequirementCache
 from litgpt import Config
 
-from lightning_utilities.core.imports import RequirementCache
+from whittle.models.gpt.model import GPT
 
 _LIGHTNING_AVAILABLE = RequirementCache("lightning>=2.2.0.dev0")
 if not bool(_LIGHTNING_AVAILABLE):
diff --git a/whittle/models/gpt/blocks/__init__.py b/whittle/models/gpt/blocks/__init__.py
index 89f8c67..e92dcb5 100644
--- a/whittle/models/gpt/blocks/__init__.py
+++ b/whittle/models/gpt/blocks/__init__.py
@@ -1,5 +1,7 @@
+from __future__ import annotations
+
 from .causal_self_attention import CausalSelfAttention
-from .mlp import GptNeoxMLP, LLaMAMLP, GemmaMLP
+from .mlp import GemmaMLP, GptNeoxMLP, LLaMAMLP
 from .transformer_block import Block
 
 __all__ = ["CausalSelfAttention", "GptNeoxMLP", "LLaMAMLP", "GemmaMLP", "Block"]
diff --git a/whittle/models/gpt/blocks/causal_self_attention.py b/whittle/models/gpt/blocks/causal_self_attention.py
index 5ba3c55..e9a3d87 100644
--- a/whittle/models/gpt/blocks/causal_self_attention.py
+++ b/whittle/models/gpt/blocks/causal_self_attention.py
@@ -1,9 +1,12 @@
+from __future__ import annotations
+
+import math
+
 import torch
 import torch.nn as nn
 from litgpt import Config
-from typing import Optional
-import math
 from litgpt.model import KVCache, apply_rope
+
 from whittle.modules import Linear
 
 
@@ -19,7 +22,7 @@ def __init__(self, config: Config) -> None:
             config.head_size * config.n_head, config.n_embd, bias=config.bias
         )
         # disabled by default
-        self.kv_cache: Optional[KVCache] = None
+        self.kv_cache: KVCache | None = None
         self.config = config
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -102,8 +105,8 @@ def forward(
         x: torch.Tensor,
         cos: torch.Tensor,
         sin: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        input_pos: Optional[torch.Tensor] = None,
+        mask: torch.Tensor | None = None,
+        input_pos: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert (
             self.sub_network_n_embd is not None
@@ -176,7 +179,7 @@ def scaled_dot_product_attention(
         q: torch.Tensor,
         k: torch.Tensor,
         v: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
+        mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         scale = 1.0 / math.sqrt(self.sub_network_head_size)
         y = torch.nn.functional.scaled_dot_product_attention(
@@ -188,10 +191,10 @@ def build_kv_cache(
         self,
         batch_size: int,
         max_seq_length: int,
-        rope_cache_length: Optional[int] = None,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ) -> "KVCache":
+        rope_cache_length: int | None = None,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ) -> KVCache:
         heads = 1 if self.config.n_query_groups == 1 else self.config.n_head
         v_shape = (batch_size, heads, max_seq_length, self.config.head_size)
         if rope_cache_length is None:
diff --git a/whittle/models/gpt/blocks/mlp.py b/whittle/models/gpt/blocks/mlp.py
index 979254f..1aaf5f7 100644
--- a/whittle/models/gpt/blocks/mlp.py
+++ b/whittle/models/gpt/blocks/mlp.py
@@ -1,8 +1,10 @@
-from typing import Optional
+from __future__ import annotations
+
 
-import torch
 import litgpt
+import torch
 from litgpt import Config
+
 from whittle.modules import Linear
 
 
@@ -55,8 +57,8 @@ def __init__(self, config: Config) -> None:
         self.proj = Linear(config.intermediate_size, config.n_embd, bias=config.bias)
         self.in_features = config.n_embd
         self.intermediate_size = config.intermediate_size
-        self.sub_network_n_embd: Optional[int] = None
-        self.sub_network_intermediate_size: Optional[int] = None
+        self.sub_network_n_embd: int | None = None
+        self.sub_network_intermediate_size: int | None = None
         self.config = config
 
     def set_sub_network(
diff --git a/whittle/models/gpt/blocks/transformer_block.py b/whittle/models/gpt/blocks/transformer_block.py
index 52eeddc..d8a860c 100644
--- a/whittle/models/gpt/blocks/transformer_block.py
+++ b/whittle/models/gpt/blocks/transformer_block.py
@@ -1,11 +1,13 @@
-from typing import Optional, Union
+from __future__ import annotations
+
 
 import litgpt
 from litgpt import Config
+
 from whittle.models.gpt.blocks.causal_self_attention import CausalSelfAttention
-from whittle.modules.rmsnorm import RMSNorm
+from whittle.models.gpt.blocks.mlp import GemmaMLP, GptNeoxMLP, LLaMAMLP
 from whittle.modules.layernorm import LayerNorm
-from whittle.models.gpt.blocks.mlp import GptNeoxMLP, LLaMAMLP, GemmaMLP
+from whittle.modules.rmsnorm import RMSNorm
 
 
 class Block(litgpt.model.Block):
@@ -20,7 +22,7 @@ def __init__(self, config: Config) -> None:
 
         self.norm_1 = self.norm_class()(config.n_embd, eps=config.norm_eps)
         self.attn = CausalSelfAttention(config)
-        self.norm_2: Optional[Union[LayerNorm, RMSNorm]] = (
+        self.norm_2: LayerNorm | RMSNorm | None = (
             None
             if config.shared_attention_norm
             else self.norm_class()(config.n_embd, eps=config.norm_eps)
diff --git a/whittle/models/gpt/extract.py b/whittle/models/gpt/extract.py
index deb80c0..1dd7706 100644
--- a/whittle/models/gpt/extract.py
+++ b/whittle/models/gpt/extract.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from collections import OrderedDict
 
 from whittle.models.gpt import GPT
diff --git a/whittle/models/gpt/model.py b/whittle/models/gpt/model.py
index bf6c5fd..67a8738 100644
--- a/whittle/models/gpt/model.py
+++ b/whittle/models/gpt/model.py
@@ -4,19 +4,21 @@
 https://github.com/EleutherAI/gpt-neox/tree/main/megatron/model.
 """
 
-from typing import Any, Optional, Tuple
+from __future__ import annotations
+
+from typing import Any
 from typing_extensions import Self
 
 import torch
 import torch.nn as nn
-
 from litgpt import Config
 from litgpt.model import build_rope_cache
+
 from whittle.models.gpt.blocks import Block
 from whittle.modules.embedding import Embedding
+from whittle.modules.layernorm import LayerNorm
 from whittle.modules.linear import Linear
 from whittle.modules.rmsnorm import RMSNorm
-from whittle.modules.layernorm import LayerNorm
 
 
 class GPT(nn.Module):
@@ -37,7 +39,7 @@ def __init__(self, config: Config) -> None:
         )
         self.max_layer = config.n_layer
         self.max_seq_length = self.config.block_size
-        self.mask_cache: Optional[torch.Tensor] = None
+        self.mask_cache: torch.Tensor | None = None
 
         # Set current sub-network to super-network
         self.sub_network_n_embd = self.config.n_embd
@@ -104,8 +106,8 @@ def tie_weights(self) -> None:
             self.transformer.wte.weight = self.lm_head.weight
 
     def rope_cache(
-        self, device: Optional[torch.device] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self, device: torch.device | None = None
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         return build_rope_cache(
             seq_len=self.max_seq_length,
             n_elem=self.config.rope_n_elem,
@@ -191,7 +193,7 @@ def process_rope_cache(self, cos, sin, input_pos, T):
         return cos, sin, mask
 
     def forward(
-        self, idx: torch.Tensor, input_pos: Optional[torch.Tensor] = None
+        self, idx: torch.Tensor, input_pos: torch.Tensor | None = None
     ) -> torch.Tensor:
         T = idx.size(1)
         if self.max_seq_length < T:
@@ -256,9 +258,9 @@ def from_name(cls, name: str, **kwargs: Any) -> Self:
     def set_kv_cache(
         self,
         batch_size: int,
-        rope_cache_length: Optional[int] = None,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
+        rope_cache_length: int | None = None,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
     ) -> None:
         if rope_cache_length is None:
             rope_cache_length = self.cos.size(-1)
@@ -282,7 +284,7 @@ def clear_kv_cache(self) -> None:
 
 
 def build_mask_cache(
-    max_seq_length: int, device: Optional[torch.device] = None
+    max_seq_length: int, device: torch.device | None = None
 ) -> torch.Tensor:
     ones = torch.ones((max_seq_length, max_seq_length), device=device, dtype=torch.bool)
     return torch.tril(ones).unsqueeze(0).unsqueeze(0)
diff --git a/whittle/models/gpt/utils.py b/whittle/models/gpt/utils.py
index 36668ca..c05e851 100644
--- a/whittle/models/gpt/utils.py
+++ b/whittle/models/gpt/utils.py
@@ -2,37 +2,33 @@
 
 """Utility functions for training and inference."""
 
+from __future__ import annotations
+
 import math
 import pickle
+import random
 import sys
 from io import BytesIO
 from pathlib import Path
 from typing import (
     TYPE_CHECKING,
     Any,
-    Dict,
-    Iterable,
-    List,
-    Mapping,
-    Optional,
     TypeVar,
-    Union,
-    Iterator,
 )
+from collections.abc import Iterable, Iterator, Mapping
+from typing_extensions import Self
 
 import lightning as L
+import numpy as np
 import torch
 import torch.nn as nn
-import random
 import torch.utils._device
 from lightning.fabric.strategies import FSDPStrategy
 from lightning.fabric.utilities.load import _lazy_load as lazy_load
 from torch.serialization import normalize_storage_type
-from typing_extensions import Self
-import numpy as np
 
 if TYPE_CHECKING:
-    from gpt.model import GPT
+    from whittle.models.gpt.model import GPT
 
 
 def find_multiple(n: int, k: int) -> int:
@@ -42,7 +38,7 @@ def find_multiple(n: int, k: int) -> int:
     return n + k - (n % k)
 
 
-def num_parameters(module: nn.Module, requires_grad: Optional[bool] = None) -> int:
+def num_parameters(module: nn.Module, requires_grad: bool | None = None) -> int:
     total = 0
     for p in module.parameters():
         if requires_grad is None or p.requires_grad == requires_grad:
@@ -259,7 +255,7 @@ def __exit__(self, type, value, traceback):
 
 
 def chunked_cross_entropy(
-    logits: Union[torch.Tensor, List[torch.Tensor]],
+    logits: torch.Tensor | list[torch.Tensor],
     targets: torch.Tensor,
     chunk_size: int = 128,
     ignore_index: int = -1,
@@ -318,7 +314,7 @@ def chunked_cross_entropy(
     return torch.cat(loss_chunks).sum() / max(1, non_masked_elems)
 
 
-def map_old_state_dict_weights(state_dict: Dict, mapping: Mapping, prefix: str) -> Dict:
+def map_old_state_dict_weights(state_dict: dict, mapping: Mapping, prefix: str) -> dict:
     for checkpoint_name, attribute_name in mapping.items():
         full_checkpoint_name = prefix + checkpoint_name
         if full_checkpoint_name in state_dict:
@@ -369,7 +365,7 @@ def flops_per_param(
     return flops_per_seq + attn_flops_per_seq
 
 
-def estimate_flops(model: "GPT", training: bool) -> int:
+def estimate_flops(model: GPT, training: bool) -> int:
     """Measures estimated FLOPs for MFU.
 
     Refs:
@@ -413,7 +409,7 @@ class CycleIterator:
     def __init__(self, iterable: Iterable) -> None:
         self.iterable = iterable
         self.epoch = 0
-        self._iterator: Optional[Iterator] = None
+        self._iterator: Iterator | None = None
 
     def __next__(self) -> Any:
         if self._iterator is None:
diff --git a/whittle/modules/__init__.py b/whittle/modules/__init__.py
index c8168b3..3ef0ffb 100644
--- a/whittle/modules/__init__.py
+++ b/whittle/modules/__init__.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from .linear import Linear
 
 __all__ = ["Linear"]
diff --git a/whittle/modules/embedding.py b/whittle/modules/embedding.py
index 6d84ceb..8da39d5 100644
--- a/whittle/modules/embedding.py
+++ b/whittle/modules/embedding.py
@@ -1,16 +1,17 @@
+from __future__ import annotations
+
+
 import torch
 import torch.nn.functional as F
 
-from typing import Optional
-
 
 class Embedding(torch.nn.Embedding):
     def __init__(
         self,
         num_embeddings: int,
         embedding_dim: int,
-        padding_idx: Optional[int] = None,
-        max_norm: Optional[float] = None,
+        padding_idx: int | None = None,
+        max_norm: float | None = None,
         norm_type: float = 2.0,
         scale_grad_by_freq: bool = False,
         sparse: bool = False,
@@ -30,7 +31,7 @@ def __init__(
         )
 
         # the embedding dimensionality of the current sub-network
-        self.sub_network_embedding_dim: Optional[int] = embedding_dim
+        self.sub_network_embedding_dim: int | None = embedding_dim
         self.random_indices = torch.arange(self.sub_network_embedding_dim)
 
     def set_sub_network(
diff --git a/whittle/modules/layernorm.py b/whittle/modules/layernorm.py
index 1e0bdf1..7c254d6 100644
--- a/whittle/modules/layernorm.py
+++ b/whittle/modules/layernorm.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import torch
 import torch.nn.functional as F
 
diff --git a/whittle/modules/linear.py b/whittle/modules/linear.py
index 9a5faca..2bc1e68 100644
--- a/whittle/modules/linear.py
+++ b/whittle/modules/linear.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
diff --git a/whittle/modules/rmsnorm.py b/whittle/modules/rmsnorm.py
index 42b3a17..2f68e0c 100644
--- a/whittle/modules/rmsnorm.py
+++ b/whittle/modules/rmsnorm.py
@@ -1,4 +1,5 @@
-from typing import Optional
+from __future__ import annotations
+
 
 import torch
 
@@ -23,7 +24,7 @@ def __init__(
         self.eps = eps
         self.dim = dim
         self.add_unit_offset = add_unit_offset
-        self.sub_network_in_features: Optional[int] = in_features
+        self.sub_network_in_features: int | None = in_features
         self.random_indices = torch.arange(self.in_features)
 
     def set_sub_network(
diff --git a/whittle/sampling/__init__.py b/whittle/sampling/__init__.py
index 2d2998b..c350194 100644
--- a/whittle/sampling/__init__.py
+++ b/whittle/sampling/__init__.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from .random_sampler import RandomSampler
 
 __all__ = ["RandomSampler"]
diff --git a/whittle/sampling/random_sampler.py b/whittle/sampling/random_sampler.py
index 44ea832..0d006dd 100644
--- a/whittle/sampling/random_sampler.py
+++ b/whittle/sampling/random_sampler.py
@@ -1,12 +1,12 @@
-from typing import Dict, Optional
+from __future__ import annotations
 
-import numpy as np
 
-from syne_tune.config_space import Domain, Categorical
+import numpy as np
+from syne_tune.config_space import Categorical, Domain
 
 
 class RandomSampler:
-    def __init__(self, config_space: Dict, seed: Optional[int] = None):
+    def __init__(self, config_space: dict, seed: int | None = None):
         self.config_space = config_space
         self.rng = np.random.RandomState(seed)
 
diff --git a/whittle/search/__init__.py b/whittle/search/__init__.py
index 6adec5d..3ed6c43 100644
--- a/whittle/search/__init__.py
+++ b/whittle/search/__init__.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from .search import multi_objective_search
 
 __all__ = ["multi_objective_search"]
diff --git a/whittle/search/ask_tell_scheduler.py b/whittle/search/ask_tell_scheduler.py
index 0bd2e46..64777b7 100644
--- a/whittle/search/ask_tell_scheduler.py
+++ b/whittle/search/ask_tell_scheduler.py
@@ -1,14 +1,15 @@
-from typing import Dict
+from __future__ import annotations
+
 import datetime
 
-from syne_tune.backend.trial_status import Trial, Status, TrialResult
+from syne_tune.backend.trial_status import Status, Trial, TrialResult
 from syne_tune.optimizer.scheduler import TrialScheduler
 
 
 class AskTellScheduler:
     bscheduler: TrialScheduler
     trial_counter: int
-    completed_experiments: Dict[int, TrialResult]
+    completed_experiments: dict[int, TrialResult]
 
     def __init__(self, base_scheduler: TrialScheduler):
         self.bscheduler = base_scheduler
@@ -29,7 +30,7 @@ def ask(self) -> Trial:
         self.trial_counter += 1
         return trial
 
-    def tell(self, trial: Trial, experiment_result: Dict[str, float]):
+    def tell(self, trial: Trial, experiment_result: dict[str, float]):
         """
         Feed experiment results back to the Scheduler
 
diff --git a/whittle/search/baselines.py b/whittle/search/baselines.py
index 8204754..d1d51f6 100644
--- a/whittle/search/baselines.py
+++ b/whittle/search/baselines.py
@@ -1,15 +1,16 @@
+from __future__ import annotations
+
 from dataclasses import dataclass
-from typing import Dict
 
-from syne_tune.config_space import Domain, Categorical
+from syne_tune.config_space import Categorical, Domain
 from syne_tune.optimizer.baselines import (
-    RandomSearch,
     MOREA,
     NSGA2,
     MORandomScalarizationBayesOpt,
-    # EHVI
+    RandomSearch,
 )
 
+# EHVI
 from syne_tune.optimizer.schedulers.multiobjective.linear_scalarizer import (
     LinearScalarizedScheduler,
 )
@@ -63,7 +64,7 @@ def get_mid_point(config_space):
 
 @dataclass
 class MethodArguments:
-    config_space: Dict
+    config_space: dict
     metrics: list
     mode: list
     random_seed: int
diff --git a/whittle/search/local_search.py b/whittle/search/local_search.py
index 916482f..a3e479f 100644
--- a/whittle/search/local_search.py
+++ b/whittle/search/local_search.py
@@ -1,13 +1,14 @@
-import numpy as np
+from __future__ import annotations
+
 import logging
 from copy import deepcopy
-from typing import Optional, List, Dict, Any, Union
 from dataclasses import dataclass
+from typing import Any
 
+import numpy as np
+from syne_tune.config_space import Domain
 from syne_tune.optimizer.schedulers import FIFOScheduler
 from syne_tune.optimizer.schedulers.searchers import StochasticSearcher
-from syne_tune.config_space import Domain
-
 
 logger = logging.getLogger(__name__)
 
@@ -45,15 +46,15 @@ class LS(FIFOScheduler):
 
     def __init__(
         self,
-        config_space: Dict[str, Any],
-        metric: List[str],
-        mode: Union[List[str], str] = "min",
-        start_point: Optional[Dict[str, Any]] = None,
-        random_seed: Optional[int] = None,
-        points_to_evaluate: Optional[List[dict]] = None,
+        config_space: dict[str, Any],
+        metric: list[str],
+        mode: list[str] | str = "min",
+        start_point: dict[str, Any] | None = None,
+        random_seed: int | None = None,
+        points_to_evaluate: list[dict] | None = None,
         **kwargs,
     ):
-        super(LS, self).__init__(
+        super().__init__(
             config_space=config_space,
             metric=metric,
             mode=mode,
@@ -76,10 +77,10 @@ class LocalSearch(StochasticSearcher):
     def __init__(
         self,
         config_space,
-        metric: Union[List[str], str],
-        points_to_evaluate: Optional[List[dict]] = None,
-        start_point: Optional[Dict] = None,
-        mode: Union[List[str], str] = "min",
+        metric: list[str] | str,
+        points_to_evaluate: list[dict] | None = None,
+        start_point: dict | None = None,
+        mode: list[str] | str = "min",
         **kwargs,
     ):
         if start_point is None:
@@ -90,22 +91,22 @@ def __init__(
         else:
             self.start_point = start_point
 
-        self._pareto_front: List[PopulationElement] = []
+        self._pareto_front: list[PopulationElement] = []
 
         if points_to_evaluate is None:
             points_to_evaluate = [self.start_point]
         else:
             points_to_evaluate.append(self.start_point)
 
-        super(LocalSearch, self).__init__(
+        super().__init__(
             config_space,
             metric,
             mode=mode,
             points_to_evaluate=points_to_evaluate,
             **kwargs,
         )
-        if isinstance(self._mode, List):
-            self._metric_op: Dict[str, Any] = {
+        if isinstance(self._mode, list):
+            self._metric_op: dict[str, Any] = {
                 metric: 1 if mode == "min" else -1
                 for metric, mode in zip(metric, self._mode)
             }
@@ -153,7 +154,7 @@ def is_efficient(self, costs):
     def dominates(self, incumbent, neighbour):
         return np.all(neighbour <= incumbent) * np.any(neighbour < incumbent)
 
-    def get_config(self, **kwargs) -> Optional[dict]:
+    def get_config(self, **kwargs) -> dict | None:
         config = self._next_initial_config()
         if config is not None:
             return config
@@ -167,13 +168,13 @@ def get_config(self, **kwargs) -> Optional[dict]:
 
         return config
 
-    def _metric_dict(self, reported_results: Dict) -> Dict:
+    def _metric_dict(self, reported_results: dict) -> dict:
         return {
             metric: reported_results[metric] * self._metric_op[metric]
             for metric in self._metric
         }
 
-    def _update(self, trial_id: int, config: Dict[str, Any], result: Dict[str, Any]):
+    def _update(self, trial_id: int, config: dict[str, Any], result: dict[str, Any]):
         # assume that the new point is in the Pareto Front
         element = PopulationElement(
             trial_id=trial_id, config=config, result=self._metric_dict(result)
@@ -207,16 +208,15 @@ def configure_scheduler(self, scheduler):
         ), "This searcher requires TrialSchedulerWithSearcher scheduler"
         super().configure_scheduler(scheduler)
 
-    def clone_from_state(self, state: Dict[str, Any]):
+    def clone_from_state(self, state: dict[str, Any]):
         raise NotImplementedError
 
 
 if __name__ == "__main__":
-    from transformers import AutoConfig
-
     from nas_fine_tuning.sampling import SmallSearchSpace
-    from syne_tune.tuner import Trial
     from syne_tune.config_space import Categorical
+    from syne_tune.tuner import Trial
+    from transformers import AutoConfig
 
     config = AutoConfig.from_pretrained("bert-base-cased")
     ss = SmallSearchSpace(config)
diff --git a/whittle/search/multi_objective.py b/whittle/search/multi_objective.py
index e19c375..6311064 100644
--- a/whittle/search/multi_objective.py
+++ b/whittle/search/multi_objective.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 
 
@@ -7,7 +9,7 @@ def get_pareto_optimal(costs: np.ndarray):
     :param costs: (n_points, m_cost_values) array
     :return: (n_points, 1) indicator if point is on pareto front or not.
     """
-    assert type(costs) == np.ndarray
+    assert isinstance(costs, np.ndarray)
     assert costs.ndim == 2
 
     # first assume all points are pareto optimal
diff --git a/whittle/search/search.py b/whittle/search/search.py
index 62fc632..e9fe095 100644
--- a/whittle/search/search.py
+++ b/whittle/search/search.py
@@ -1,10 +1,11 @@
+from __future__ import annotations
+
 import time
-from typing import Optional
 
 import numpy as np
 
-from whittle.search.baselines import MethodArguments, methods
 from whittle.search.ask_tell_scheduler import AskTellScheduler
+from whittle.search.baselines import MethodArguments, methods
 from whittle.search.multi_objective import get_pareto_optimal
 
 
@@ -13,8 +14,8 @@ def multi_objective_search(
     search_space: dict,
     search_strategy: str = "random_search",
     num_samples: int = 100,
-    objective_kwargs: Optional[dict] = None,
-    seed: Optional[int] = None,
+    objective_kwargs: dict | None = None,
+    seed: int | None = None,
 ):
     """
     Search for the Pareto optimal sub-networks.
diff --git a/whittle/training_strategies/__init__.py b/whittle/training_strategies/__init__.py
index 8c9662a..e1bc88a 100644
--- a/whittle/training_strategies/__init__.py
+++ b/whittle/training_strategies/__init__.py
@@ -1,8 +1,10 @@
-from .sandwich import SandwichStrategy
+from __future__ import annotations
+
+from .ats import ATS
 from .random import RandomStrategy
-from .standard import StandardStrategy
 from .random_linear import RandomLinearStrategy
-from .ats import ATS
+from .sandwich import SandwichStrategy
+from .standard import StandardStrategy
 
 __all__ = [
     "SandwichStrategy",
diff --git a/whittle/training_strategies/ats.py b/whittle/training_strategies/ats.py
index 9e584b4..fb0a573 100644
--- a/whittle/training_strategies/ats.py
+++ b/whittle/training_strategies/ats.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from whittle.training_strategies.base_strategy import BaseTrainingStrategy
 
 
diff --git a/whittle/training_strategies/base_strategy.py b/whittle/training_strategies/base_strategy.py
index 57885da..272c922 100644
--- a/whittle/training_strategies/base_strategy.py
+++ b/whittle/training_strategies/base_strategy.py
@@ -1,8 +1,12 @@
-from whittle.sampling.random_sampler import RandomSampler
-from typing import Callable, Optional
-from whittle.loss import DistillLoss
+from __future__ import annotations
+
+from typing import Callable
+
 import torch
 
+from whittle.loss import DistillLoss
+from whittle.sampling.random_sampler import RandomSampler
+
 
 class BaseTrainingStrategy:
     """
@@ -15,7 +19,7 @@ def __init__(
         self,
         sampler: RandomSampler,
         loss_function: Callable,
-        kd_loss: Optional[DistillLoss] = None,
+        kd_loss: DistillLoss | None = None,
         device: str = "cuda",
         **kwargs,
     ):
diff --git a/whittle/training_strategies/random.py b/whittle/training_strategies/random.py
index 4c711c5..dab213d 100644
--- a/whittle/training_strategies/random.py
+++ b/whittle/training_strategies/random.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from whittle.training_strategies.base_strategy import BaseTrainingStrategy
 
 
diff --git a/whittle/training_strategies/random_linear.py b/whittle/training_strategies/random_linear.py
index 40a1b48..be5da5f 100644
--- a/whittle/training_strategies/random_linear.py
+++ b/whittle/training_strategies/random_linear.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 
 from whittle.training_strategies.base_strategy import BaseTrainingStrategy
diff --git a/whittle/training_strategies/sandwich.py b/whittle/training_strategies/sandwich.py
index b61b7f2..81730f4 100644
--- a/whittle/training_strategies/sandwich.py
+++ b/whittle/training_strategies/sandwich.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from whittle.training_strategies.base_strategy import BaseTrainingStrategy
 
 
diff --git a/whittle/training_strategies/standard.py b/whittle/training_strategies/standard.py
index 5bd810a..88488a2 100644
--- a/whittle/training_strategies/standard.py
+++ b/whittle/training_strategies/standard.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from whittle.training_strategies.base_strategy import BaseTrainingStrategy
 
 


Test patch:
diff --git a/test/test_api.py b/test/test_api.py
index 41cb1f9..cf97253 100644
--- a/test/test_api.py
+++ b/test/test_api.py
@@ -1,19 +1,21 @@
+from __future__ import annotations
+
 import json
+import pathlib
 import sys
+
+import litgpt.eval.evaluate as module
 import numpy as np
-import pathlib
 import pytest
 import torch
 from litgpt import Config
 from litgpt.scripts.download import download_from_hub
-import litgpt.eval.evaluate as module
 from lm_eval import tasks
 from lm_eval.api.instance import Instance
 
-from whittle.models.gpt import GPT
-
-from whittle.eval.whittle_llms import WhittleLM
 from whittle.eval.utils import convert_and_evaluate
+from whittle.eval.whittle_llms import WhittleLM
+from whittle.models.gpt import GPT
 
 
 def copy_subnetwork_weights(sub_network, super_network):
@@ -303,7 +305,7 @@ def test_evaluate(self, checkpoint_dir, out_dir):
             tasks="logiqa",
             batch_size=1,  # Test for non-positive integer
         )
-        with open(str(out_dir / "results.json"), "r") as f:
+        with open(str(out_dir / "results.json")) as f:
             results = json.load(f)
         acc_api = results["results"]["logiqa"]["acc,none"]
         stderr_api = results["results"]["logiqa"]["acc_stderr,none"]
@@ -318,7 +320,7 @@ def test_evaluate(self, checkpoint_dir, out_dir):
             force_conversion=True,
             batch_size=1,  # Test for non-positive integer
         )
-        with open(str(out_dir / "results.json"), "r") as f:
+        with open(str(out_dir / "results.json")) as f:
             results = json.load(f)
         acc_lit = results["results"]["logiqa"]["acc,none"]
         stderr_lit = results["results"]["logiqa"]["acc_stderr,none"]
@@ -363,7 +365,7 @@ def test_compare_litgpt(self, checkpoint_dir, checkpoint_dir_14m, out_dir):
             tasks="logiqa",
             batch_size=1,  # Test for non-positive integer
         )
-        with open(str(out_dir / "results.json"), "r") as f:
+        with open(str(out_dir / "results.json")) as f:
             results = json.load(f)
         acc_api = results["results"]["logiqa"]["acc,none"]
         stderr_api = results["results"]["logiqa"]["acc_stderr,none"]
@@ -378,7 +380,7 @@ def test_compare_litgpt(self, checkpoint_dir, checkpoint_dir_14m, out_dir):
             force_conversion=True,
             batch_size=1,  # Test for non-positive integer
         )
-        with open(str(out_dir / "results.json"), "r") as f:
+        with open(str(out_dir / "results.json")) as f:
             results = json.load(f)
         acc_lit = results["results"]["logiqa"]["acc,none"]
         stderr_lit = results["results"]["logiqa"]["acc_stderr,none"]
diff --git a/test/test_attention.py b/test/test_attention.py
index bef4b67..25fd6a8 100644
--- a/test/test_attention.py
+++ b/test/test_attention.py
@@ -1,9 +1,15 @@
+from __future__ import annotations
+
+import pytest
 import torch
-from whittle.models.gpt.blocks import CausalSelfAttention
-from litgpt.model import CausalSelfAttention as LitCausalSelfAttention
 from litgpt import Config
-from litgpt.model import build_mask_cache, build_rope_cache
-import pytest
+from litgpt.model import (
+    CausalSelfAttention as LitCausalSelfAttention,
+    build_mask_cache,
+    build_rope_cache,
+)
+
+from whittle.models.gpt.blocks import CausalSelfAttention
 
 attention_configs = {
     "mha_fix_head_size": {
diff --git a/test/test_checkpoint_loading.py b/test/test_checkpoint_loading.py
index f7ab224..3527a31 100644
--- a/test/test_checkpoint_loading.py
+++ b/test/test_checkpoint_loading.py
@@ -1,10 +1,13 @@
-import pytest
-import torch
+from __future__ import annotations
+
 import pathlib
 
+import pytest
+import torch
 from litgpt import Config
 from litgpt.model import GPT as LitGPT
 from litgpt.scripts.download import download_from_hub
+
 from whittle.models.gpt.model import GPT as whittleGPT
 
 
diff --git a/test/test_embedding.py b/test/test_embedding.py
index 0110210..6735bbe 100644
--- a/test/test_embedding.py
+++ b/test/test_embedding.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import torch
 
 from whittle.modules.embedding import Embedding
diff --git a/test/test_extract.py b/test/test_extract.py
index a74db81..4ae9fba 100644
--- a/test/test_extract.py
+++ b/test/test_extract.py
@@ -1,5 +1,6 @@
-import torch
+from __future__ import annotations
 
+import torch
 from litgpt.config import Config
 
 from whittle.models.gpt import GPT
diff --git a/test/test_linear.py b/test/test_linear.py
index 79b1290..70e323c 100644
--- a/test/test_linear.py
+++ b/test/test_linear.py
@@ -1,4 +1,7 @@
+from __future__ import annotations
+
 import torch
+
 from whittle.modules.linear import Linear
 
 
diff --git a/test/test_lmhead.py b/test/test_lmhead.py
index cd214ae..af3bc50 100644
--- a/test/test_lmhead.py
+++ b/test/test_lmhead.py
@@ -1,4 +1,7 @@
+from __future__ import annotations
+
 import torch
+
 from whittle.modules.linear import Linear
 
 
diff --git a/test/test_mlp.py b/test/test_mlp.py
index 0cf4dec..e8cc7af 100644
--- a/test/test_mlp.py
+++ b/test/test_mlp.py
@@ -1,11 +1,14 @@
+from __future__ import annotations
+
 import torch
-from whittle.models.gpt.blocks import GptNeoxMLP, LLaMAMLP, GemmaMLP
+from litgpt import Config
 from litgpt.model import (
+    GemmaMLP as LitGemmaMLP,
     GptNeoxMLP as LitGptNeoxMLP,
     LLaMAMLP as LitLLaMAMLP,
-    GemmaMLP as LitGemmaMLP,
 )
-from litgpt import Config
+
+from whittle.models.gpt.blocks import GemmaMLP, GptNeoxMLP, LLaMAMLP
 
 
 def test_GptNeoxMLP():
diff --git a/test/test_model.py b/test/test_model.py
index 174370e..d740a93 100644
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -1,9 +1,12 @@
-from whittle.models.gpt import GPT
-from litgpt import Config
-import torch
+from __future__ import annotations
+
 import pytest
+import torch
+from litgpt import Config
 from litgpt.model import GPT as LitGPT
 
+from whittle.models.gpt import GPT
+
 
 @pytest.mark.parametrize("sample_random_indices", [False, True])
 def test_gpt(sample_random_indices):
diff --git a/test/test_norms.py b/test/test_norms.py
index 0aa8610..a17f12e 100644
--- a/test/test_norms.py
+++ b/test/test_norms.py
@@ -1,5 +1,6 @@
-import torch
+from __future__ import annotations
 
+import torch
 from litgpt.model import RMSNorm
 
 from whittle.modules.layernorm import LayerNorm as LayerNormSuper
diff --git a/test/test_parameters.py b/test/test_parameters.py
index f10457e..773f82c 100644
--- a/test/test_parameters.py
+++ b/test/test_parameters.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import pytest
 
 from litgpt.config import Config
diff --git a/test/test_search.py b/test/test_search.py
index 35c81b8..a80662c 100644
--- a/test/test_search.py
+++ b/test/test_search.py
@@ -1,7 +1,9 @@
-import pytest
-import numpy as np
+from __future__ import annotations
 
+import numpy as np
+import pytest
 from syne_tune.config_space import randint
+
 from whittle.search import multi_objective_search
 from whittle.search.baselines import methods
 
diff --git a/test/test_training_strategies.py b/test/test_training_strategies.py
index e2a88cc..298a3ca 100644
--- a/test/test_training_strategies.py
+++ b/test/test_training_strategies.py
@@ -1,21 +1,22 @@
+from __future__ import annotations
+
 import pytest
 import torch.nn as nn
 import torch.nn.functional
+from litgpt import Config
+from syne_tune.config_space import choice, randint
 
-from syne_tune.config_space import randint, choice
 from whittle.loss import DistillLoss
+from whittle.models.gpt import GPT
+from whittle.modules.linear import Linear
+from whittle.sampling.random_sampler import RandomSampler
 from whittle.training_strategies import (
-    SandwichStrategy,
+    ATS,
+    RandomLinearStrategy,
     RandomStrategy,
+    SandwichStrategy,
     StandardStrategy,
-    RandomLinearStrategy,
-    ATS,
 )
-from litgpt import Config
-from whittle.models.gpt import GPT
-from whittle.sampling.random_sampler import RandomSampler
-from whittle.modules.linear import Linear
-
 
 methods = [
     SandwichStrategy,
@@ -41,7 +42,7 @@
 
 class MLP(nn.Module):
     def __init__(self, input_dim):
-        super(MLP, self).__init__()
+        super().__init__()
         self.input_dim = input_dim
         self.hidden_dim = 64
         self.input = Linear(input_dim, self.hidden_dim)
diff --git a/test/test_transformer_block.py b/test/test_transformer_block.py
index 07abdfa..9d2e395 100644
--- a/test/test_transformer_block.py
+++ b/test/test_transformer_block.py
@@ -1,8 +1,14 @@
+from __future__ import annotations
+
 import torch
 from litgpt import Config
+from litgpt.model import (
+    Block as LitBlock,
+    build_mask_cache,
+    build_rope_cache,
+)
+
 from whittle.models.gpt.blocks import Block
-from litgpt.model import Block as LitBlock
-from litgpt.model import build_mask_cache, build_rope_cache
 
 
 def test_block():