From 596de9f9e4048210edb1a101c236dd08bbeec1a0 Mon Sep 17 00:00:00 2001
From: CodSpeed Bot <no-reply@codspeed.io>
Date: Thu, 10 Sep 2026 22:51:26 +0000
Subject: [PATCH] ci: add CodSpeed benchmarks for core forecasting pipeline

---
 .github/workflows/codspeed.yaml     |  51 +++
 README.md                           |   1 +
 benchmarks/__init__.py              |   1 +
 benchmarks/_models.py               |  86 ++++
 benchmarks/conftest.py              |  55 +++
 benchmarks/test_bench_core_utils.py |  78 ++++
 benchmarks/test_bench_dataset.py    |  63 +++
 benchmarks/test_bench_pipelines.py  |  58 +++
 pyproject.toml                      |   1 +
 uv.lock                             | 621 +++++++++++++++-------------
 10 files changed, 724 insertions(+), 291 deletions(-)
 create mode 100644 .github/workflows/codspeed.yaml
 create mode 100644 benchmarks/__init__.py
 create mode 100644 benchmarks/_models.py
 create mode 100644 benchmarks/conftest.py
 create mode 100644 benchmarks/test_bench_core_utils.py
 create mode 100644 benchmarks/test_bench_dataset.py
 create mode 100644 benchmarks/test_bench_pipelines.py

diff --git a/.github/workflows/codspeed.yaml b/.github/workflows/codspeed.yaml
new file mode 100644
index 0000000..3e33a47
--- /dev/null
+++ b/.github/workflows/codspeed.yaml
@@ -0,0 +1,51 @@
+name: CodSpeed
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  # `workflow_dispatch` allows CodSpeed to trigger backtest
+  # performance analysis in order to generate initial data.
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  id-token: write # for OpenID Connect authentication with CodSpeed
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  benchmarks:
+    name: Run benchmarks
+    runs-on: ubuntu-latest
+    env:
+      UV_PYTHON: "3.11"
+
+    steps:
+      - name: Clone repo
+        uses: actions/checkout@v4
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+
+      - name: Free disk space in action runner
+        uses: endersonmenezes/free-disk-space@v3
+        with:
+          remove_android: true
+          remove_dotnet: true
+          remove_haskell: true
+          rm_cmd: "rmz"
+          rmz_version: "3.1.1"
+
+      - name: Install dependencies
+        run: uv sync --group dev
+
+      - name: Run benchmarks
+        uses: CodSpeedHQ/action@v5
+        with:
+          mode: simulation
+          run: uv run pytest benchmarks/ --codspeed
diff --git a/README.md b/README.md
index 2fc156b..f079be0 100644
--- a/README.md
+++ b/README.md
@@ -10,6 +10,7 @@
   <a href="https://pypi.python.org/pypi/foundationforecast"><img src="https://img.shields.io/pypi/v/foundationforecast.svg" alt="PyPI"></a>
   <a href="https://github.com/TimeCopilot/foundationforecast"><img src="https://img.shields.io/pypi/pyversions/foundationforecast.svg" alt="versions"></a>
   <a href="https://github.com/TimeCopilot/foundationforecast/blob/main/LICENSE"><img src="https://img.shields.io/github/license/TimeCopilot/foundationforecast" alt="license"></a>
+  <a href="https://app.codspeed.io/TimeCopilot/foundationforecast?utm_source=badge"><img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed"/></a>
   <a href="https://discord.gg/7GEdHR6Pfg"><img src="https://img.shields.io/discord/1387291858513821776?label=discord" alt="Join Discord"></a>
 </div>
 
diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py
new file mode 100644
index 0000000..e9763df
--- /dev/null
+++ b/benchmarks/__init__.py
@@ -0,0 +1 @@
+"""CodSpeed performance benchmarks for foundationforecast."""
diff --git a/benchmarks/_models.py b/benchmarks/_models.py
new file mode 100644
index 0000000..463ef5a
--- /dev/null
+++ b/benchmarks/_models.py
@@ -0,0 +1,86 @@
+"""Lightweight forecasters used to benchmark the FoundationForecast pipeline.
+
+These models are deliberately trivial: the goal is to measure the cost of the
+library's own data plumbing (validation, splitting, merging, quantile handling)
+without downloading or running any foundation model weights.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+from utilsforecast.processing import make_future_dataframe
+
+from foundationforecast.core.forecaster import Forecaster, QuantileConverter
+
+
+class ConstantModel(Forecaster):
+    """Return a constant point forecast, plus quantiles when requested."""
+
+    def __init__(self, alias: str = "Constant", value: float = 1.0):
+        self.alias = alias
+        self.value = value
+
+    def forecast(
+        self,
+        df: pd.DataFrame,
+        h: int,
+        freq: str | None = None,
+        level: list[int | float] | None = None,
+        quantiles: list[float] | None = None,
+    ) -> pd.DataFrame:
+        freq = self._maybe_infer_freq(df, freq)
+        qc = QuantileConverter(level=level, quantiles=quantiles)
+        last_times = df.groupby("unique_id")["ds"].max()
+        fcst = make_future_dataframe(
+            uids=last_times.index.tolist(),
+            last_times=last_times,
+            h=h,
+            freq=freq,
+        )
+        fcst[self.alias] = self.value
+        if qc.quantiles:
+            for q in qc.quantiles:
+                fcst[f"{self.alias}-q-{int(q * 100)}"] = fcst[self.alias] + q
+            fcst = qc.maybe_convert_quantiles_to_level(fcst, models=[self.alias])
+        return fcst
+
+
+class SeasonalNaiveModel(Forecaster):
+    """Repeat the last seasonal period, mimicking a real model's output shape."""
+
+    alias = "SeasonalNaive"
+
+    def __init__(self, season_length: int | None = None):
+        self.season_length = season_length
+
+    def forecast(
+        self,
+        df: pd.DataFrame,
+        h: int,
+        freq: str | None = None,
+        level: list[int | float] | None = None,
+        quantiles: list[float] | None = None,
+    ) -> pd.DataFrame:
+        freq = self._maybe_infer_freq(df, freq)
+        season_length = self._maybe_get_seasonality(freq)
+        qc = QuantileConverter(level=level, quantiles=quantiles)
+        results = []
+        for uid, group in df.groupby("unique_id"):
+            y = group["y"].to_numpy()
+            effective_length = min(season_length, len(y))
+            future = make_future_dataframe(
+                uids=[uid],
+                last_times=pd.Series([group["ds"].max()], index=[uid]),
+                h=h,
+                freq=freq,
+            )
+            seasonal_values = y[-effective_length:]
+            future[self.alias] = seasonal_values[np.arange(h) % effective_length]
+            results.append(future)
+        fcst = pd.concat(results, ignore_index=True)
+        if qc.quantiles:
+            for q in qc.quantiles:
+                fcst[f"{self.alias}-q-{int(q * 100)}"] = fcst[self.alias] * (1 + q)
+            fcst = qc.maybe_convert_quantiles_to_level(fcst, models=[self.alias])
+        return fcst
diff --git a/benchmarks/conftest.py b/benchmarks/conftest.py
new file mode 100644
index 0000000..54ca11f
--- /dev/null
+++ b/benchmarks/conftest.py
@@ -0,0 +1,55 @@
+"""Shared, deterministic data fixtures for the CodSpeed benchmarks."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from utilsforecast.data import generate_series
+
+
+def make_panel(
+    n_series: int,
+    length: int,
+    freq: str = "D",
+) -> pd.DataFrame:
+    """Generate a deterministic panel with `n_series` series of `length` rows.
+
+    `generate_series` is seeded by default, so the data is stable across runs.
+    """
+    df = generate_series(
+        n_series=n_series,
+        freq=freq,
+        min_length=length,
+        max_length=length,
+    )
+    df["unique_id"] = df["unique_id"].astype(str)
+    return df.reset_index(drop=True)
+
+
+@pytest.fixture(scope="session")
+def small_panel() -> pd.DataFrame:
+    """20 daily series of 200 observations."""
+    return make_panel(n_series=20, length=200)
+
+
+@pytest.fixture(scope="session")
+def large_panel() -> pd.DataFrame:
+    """200 daily series of 500 observations."""
+    return make_panel(n_series=200, length=500)
+
+
+@pytest.fixture(scope="session")
+def unsorted_panel(large_panel: pd.DataFrame) -> pd.DataFrame:
+    """A shuffled copy of `large_panel`, forcing the sorting code paths."""
+    rng = np.random.default_rng(42)
+    idxs = rng.permutation(len(large_panel))
+    return large_panel.iloc[idxs].reset_index(drop=True)
+
+
+@pytest.fixture(scope="session")
+def string_ds_panel(large_panel: pd.DataFrame) -> pd.DataFrame:
+    """`large_panel` with `ds` stored as ISO-8601 strings."""
+    df = large_panel.copy()
+    df["ds"] = df["ds"].dt.strftime("%Y-%m-%d")
+    return df
diff --git a/benchmarks/test_bench_core_utils.py b/benchmarks/test_bench_core_utils.py
new file mode 100644
index 0000000..9437c34
--- /dev/null
+++ b/benchmarks/test_bench_core_utils.py
@@ -0,0 +1,78 @@
+"""Benchmarks for the pure-python helpers used on every forecasting call."""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+
+from foundationforecast.core.forecaster import (
+    Forecaster,
+    QuantileConverter,
+    get_seasonality,
+    maybe_convert_col_to_datetime,
+    maybe_infer_freq,
+)
+from foundationforecast.core.gluonts_forecaster import (
+    maybe_convert_col_to_float32,
+)
+
+
+def test_validate_input(benchmark, large_panel: pd.DataFrame):
+    benchmark(Forecaster.validate_input, large_panel, 12)
+
+
+def test_maybe_infer_freq(benchmark, large_panel: pd.DataFrame):
+    freq = benchmark(maybe_infer_freq, large_panel, None)
+    assert freq == "D"
+
+
+def test_maybe_convert_col_to_datetime_from_string(
+    benchmark,
+    string_ds_panel: pd.DataFrame,
+):
+    out = benchmark(maybe_convert_col_to_datetime, string_ds_panel, "ds")
+    assert pd.api.types.is_datetime64_any_dtype(out["ds"])
+
+
+def test_maybe_convert_col_to_float32(benchmark, large_panel: pd.DataFrame):
+    out = benchmark(maybe_convert_col_to_float32, large_panel, "y")
+    assert out["y"].dtype == "float32"
+
+
+@pytest.mark.parametrize("freq", ["D", "h", "MS"])
+def test_get_seasonality(benchmark, freq: str):
+    benchmark(get_seasonality, freq)
+
+
+def test_quantile_converter_from_level(benchmark):
+    qc = benchmark(QuantileConverter, [80, 90, 95], None)
+    assert qc.quantiles is not None
+
+
+def test_quantile_converter_level_to_quantiles(
+    benchmark,
+    large_panel: pd.DataFrame,
+):
+    """Levels -> quantile columns, as done when users pass `quantiles`."""
+    model = "Model"
+    df = large_panel.rename(columns={"y": model})
+    qc = QuantileConverter(quantiles=[0.1, 0.25, 0.5, 0.75, 0.9])
+    for lv in [50, 80]:
+        df[f"{model}-lo-{lv}"] = df[model] - lv / 100
+        df[f"{model}-hi-{lv}"] = df[model] + lv / 100
+    out = benchmark(qc.maybe_convert_level_to_quantiles, df, [model])
+    assert f"{model}-q-90" in out.columns
+
+
+def test_quantile_converter_quantiles_to_level(
+    benchmark,
+    large_panel: pd.DataFrame,
+):
+    """Quantile columns -> lo/hi levels, as done when users pass `level`."""
+    model = "Model"
+    df = large_panel.rename(columns={"y": model})
+    qc = QuantileConverter(level=[80, 90])
+    for q in qc.quantiles or []:
+        df[f"{model}-q-{int(q * 100)}"] = df[model] + q
+    out = benchmark(qc.maybe_convert_quantiles_to_level, df, [model])
+    assert f"{model}-lo-90" in out.columns
diff --git a/benchmarks/test_bench_dataset.py b/benchmarks/test_bench_dataset.py
new file mode 100644
index 0000000..9000419
--- /dev/null
+++ b/benchmarks/test_bench_dataset.py
@@ -0,0 +1,63 @@
+"""Benchmarks for the tensor-side data preparation used by every model."""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+import torch
+
+from foundationforecast.core.forecaster import _DataProcessor
+from foundationforecast.core.utils import TimeSeriesDataset
+
+
+@pytest.mark.parametrize("batch_size", [16, 128])
+def test_time_series_dataset_from_df(
+    benchmark,
+    large_panel: pd.DataFrame,
+    batch_size: int,
+):
+    dataset = benchmark(TimeSeriesDataset.from_df, large_panel, batch_size)
+    assert len(dataset) > 0
+
+
+def test_time_series_dataset_iteration(benchmark, large_panel: pd.DataFrame):
+    dataset = TimeSeriesDataset.from_df(large_panel, batch_size=16)
+
+    def consume() -> int:
+        return sum(len(batch) for batch in dataset)
+
+    assert benchmark(consume) == len(large_panel["unique_id"].unique())
+
+
+def test_time_series_dataset_make_future_dataframe(
+    benchmark,
+    large_panel: pd.DataFrame,
+):
+    dataset = TimeSeriesDataset.from_df(large_panel, batch_size=32)
+    future = benchmark(dataset.make_future_dataframe, 24, "D")
+    assert len(future) == 24 * len(large_panel["unique_id"].unique())
+
+
+def _variable_length_context(
+    n_series: int = 128,
+    max_len: int = 512,
+) -> list[torch.Tensor]:
+    generator = torch.Generator().manual_seed(0)
+    return [
+        torch.rand(max_len - (i % 64), generator=generator, dtype=torch.float32)
+        for i in range(n_series)
+    ]
+
+
+def test_data_processor_left_pad_and_stack(benchmark):
+    processor = _DataProcessor(dtype=torch.float32, device=torch.device("cpu"))
+    context = _variable_length_context()
+    out = benchmark(processor._prepare_and_validate_context, context)
+    assert out.shape[0] == len(context)
+
+
+def test_data_processor_impute_missing(benchmark):
+    processor = _DataProcessor(dtype=torch.float32, device=torch.device("cpu"))
+    batch = processor._prepare_and_validate_context(_variable_length_context())
+    out = benchmark(processor._maybe_impute_missing, batch)
+    assert not torch.isnan(out).any()
diff --git a/benchmarks/test_bench_pipelines.py b/benchmarks/test_bench_pipelines.py
new file mode 100644
index 0000000..bea3408
--- /dev/null
+++ b/benchmarks/test_bench_pipelines.py
@@ -0,0 +1,58 @@
+"""End-to-end benchmarks of the forecast / cross-validation / anomaly APIs.
+
+The models used here are trivial by design (see `_models.py`): what is measured
+is the library's own orchestration cost - validation, frequency inference,
+backtest splitting, merging and residual statistics.
+"""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+
+from ._models import ConstantModel, SeasonalNaiveModel
+from foundationforecast import FoundationForecast
+
+
+def test_forecast_single_model(benchmark, large_panel: pd.DataFrame):
+    ff = FoundationForecast(models=[SeasonalNaiveModel()])
+    fcst = benchmark(ff.forecast, large_panel, 12, "D")
+    assert len(fcst) == 12 * large_panel["unique_id"].nunique()
+
+
+def test_forecast_with_levels(benchmark, large_panel: pd.DataFrame):
+    ff = FoundationForecast(models=[SeasonalNaiveModel()])
+    fcst = benchmark(ff.forecast, large_panel, 12, "D", [80, 90])
+    assert "SeasonalNaive-lo-90" in fcst.columns
+
+
+def test_forecast_multi_model_merge(benchmark, large_panel: pd.DataFrame):
+    ff = FoundationForecast(
+        models=[ConstantModel(alias=f"Constant{i}", value=float(i)) for i in range(4)]
+    )
+    fcst = benchmark(ff.forecast, large_panel, 12, "D")
+    assert "Constant3" in fcst.columns
+
+
+def test_forecast_infer_freq_and_sort(benchmark, unsorted_panel: pd.DataFrame):
+    """Forecast on shuffled input, exercising freq inference and sorting."""
+    ff = FoundationForecast(models=[ConstantModel()])
+    fcst = benchmark(ff.forecast, unsorted_panel, 12)
+    assert len(fcst) == 12 * unsorted_panel["unique_id"].nunique()
+
+
+@pytest.mark.parametrize("n_windows", [1, 4])
+def test_cross_validation(
+    benchmark,
+    small_panel: pd.DataFrame,
+    n_windows: int,
+):
+    ff = FoundationForecast(models=[SeasonalNaiveModel()])
+    cv_df = benchmark(ff.cross_validation, small_panel, 12, "D", n_windows)
+    assert len(cv_df) == 12 * n_windows * small_panel["unique_id"].nunique()
+
+
+def test_detect_anomalies(benchmark, small_panel: pd.DataFrame):
+    ff = FoundationForecast(models=[SeasonalNaiveModel()])
+    anomalies = benchmark(ff.detect_anomalies, small_panel, 12, "D", 3)
+    assert "SeasonalNaive-anomaly" in anomalies.columns
diff --git a/pyproject.toml b/pyproject.toml
index c9337e3..3ed0b1a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,6 +6,7 @@ requires = ["hatchling"]
 dev = [
   "mktestdocs>=0.2.5",
   "pre-commit",
+  "pytest-codspeed>=4.0",
   "pytest-cov>=6.0",
   "pytest-mock>=3.15.1",
   "pytest-rerunfailures>=15.1",
diff --git a/uv.lock b/uv.lock
index 266def1..2c16fe6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -7,7 +7,7 @@ resolution-markers = [
   "python_full_version == '3.14.*'",
   "python_full_version >= '3.15'",
 ]
-revision = 2
+revision = 3
 version = 1
 
 [[package]]
@@ -497,7 +497,7 @@ wheels = [
 
 [[package]]
 dependencies = [
-  {marker = "implementation_name != 'PyPy'", name = "pycparser"},
+  {name = "pycparser"},
 ]
 name = "cffi"
 sdist = {hash = "sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be", size = 530807, upload-time = "2026-08-03T21:21:18.939Z", url = "https://files.pythonhosted.org/packages/9e/ef/008a1939e372c06329a3fce4279c02f328488f3526744906eeec3da7ad5f/cffi-2.1.1.tar.gz"}
@@ -733,7 +733,7 @@ wheels = [
 
 [[package]]
 dependencies = [
-  {marker = "python_full_version < '3.11'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"},
+  {name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"},
 ]
 name = "contourpy"
 resolution-markers = [
@@ -803,7 +803,7 @@ wheels = [
 
 [[package]]
 dependencies = [
-  {marker = "python_full_version >= '3.11' and python_full_version < '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"},
+  {marker = "python_full_version < '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"},
   {marker = "python_full_version >= '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "2.5.2"},
 ]
 name = "contourpy"
@@ -1026,7 +1026,7 @@ toml = [
 
 [[package]]
 dependencies = [
-  {marker = "python_full_version < '3.15'", name = "cuda-pathfinder"},
+  {name = "cuda-pathfinder"},
 ]
 name = "cuda-bindings"
 source = {registry = "https://pypi.org/simple"}
@@ -1064,43 +1064,43 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cublas"},
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-nvrtc"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cublas"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-nvrtc"},
 ]
 cudart = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-runtime"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-runtime"},
 ]
 cufft = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cufft"},
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-nvjitlink"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cufft"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-nvjitlink"},
 ]
 cufile = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')", name = "nvidia-cufile"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cufile"},
 ]
 cupti = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cuda-cupti"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-cuda-cupti"},
 ]
 curand = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-curand"},
+  {marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'", name = "nvidia-curand"},
 ]
 cusolver = [
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cublas"},
-  {marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')", name = "nvidia-cusolver"},
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-  {marker = "python_full_version >= '3.11'", name = "ninja"},
-  {marker = "python_full_version >= '3.11'", name = "torch"},
+  {marker = "sys_platform == 'linux'", name = "triton"},
+  {marker = "sys_platform == 'win32'", name = "triton-windows"},
+  {name = "einops"},
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   {name = "pre-commit"},
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   {name = "pytest-mock"},
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   {name = "pytest", specifier = ">=8.0"},
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