automatic import of python-scale-nucleusopeneuler20.03

3 files changed, 719 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..8ee0031 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/scale-nucleus-0.15.4.tar.gz
diff --git a/python-scale-nucleus.spec b/python-scale-nucleus.spec
new file mode 100644
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--- /dev/null
+++ b/python-scale-nucleus.spec
@@ -0,0 +1,717 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-scale-nucleus
+Version:	0.15.4
+Release:	1
+Summary:	The official Python client library for Nucleus, the Data Platform for AI
+License:	MIT
+URL:		https://scale.com/nucleus
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/5b/b5/429360399e1411ebcc7f854d49d9a94a02c801cbe701cec7d438a83d6d16/scale-nucleus-0.15.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-requests
+Requires:	python3-tqdm
+Requires:	python3-dataclasses
+Requires:	python3-aiohttp
+Requires:	python3-nest-asyncio
+Requires:	python3-pydantic
+Requires:	python3-numpy
+Requires:	python3-numpy
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-click
+Requires:	python3-rich
+Requires:	python3-shellingham
+Requires:	python3-scikit-learn
+Requires:	python3-Shapely
+Requires:	python3-rasterio
+Requires:	python3-Pillow
+Requires:	python3-scale-launch
+Requires:	python3-astroid
+Requires:	python3-questionary
+Requires:	python3-dateutil
+
+%description
+# Nucleus
+
+https://dashboard.scale.com/nucleus
+
+Aggregate metrics in ML are not good enough. To improve production ML, you need to understand their qualitative failure modes, fix them by gathering more data, and curate diverse scenarios.
+
+Scale Nucleus helps you:
+
+- Visualize your data
+- Curate interesting slices within your dataset
+- Review and manage annotations
+- Measure and debug your model performance
+
+Nucleus is a new way—the right way—to develop ML models, helping us move away from the concept of one dataset and towards a paradigm of collections of scenarios.
+
+## Installation
+
+`$ pip install scale-nucleus`
+
+## CLI installation
+
+We recommend installing the CLI via `pipx` (https://pypa.github.io/pipx/installation/). This makes sure that
+the CLI does not interfere with you system packages and is accessible from your favorite terminal.
+
+For MacOS:
+
+```bash
+brew install pipx
+pipx ensurepath
+pipx install scale-nucleus
+# Optional installation of shell completion (for bash, zsh or fish)
+nu install-completions
+```
+
+Otherwise, install via pip (requires pip 19.0 or later):
+
+```bash
+python3 -m pip install --user pipx
+python3 -m pipx ensurepath
+python3 -m pipx install scale-nucleus
+# Optional installation of shell completion (for bash, zsh or fish)
+nu install-completions
+```
+
+## Common issues/FAQ
+
+### Outdated Client
+
+Nucleus is iterating rapidly and as a result we do not always perfectly preserve backwards compatibility with older versions of the client. If you run into any unexpected error, it's a good idea to upgrade your version of the client by running
+
+```
+pip install --upgrade scale-nucleus
+```
+
+## Usage
+
+For the most up to date documentation, reference: https://dashboard.scale.com/nucleus/docs/api?language=python.
+
+## For Developers
+
+Clone from github and install as editable
+
+```
+git clone git@github.com:scaleapi/nucleus-python-client.git
+cd nucleus-python-client
+pip3 install poetry
+poetry install
+```
+
+Please install the pre-commit hooks by running the following command:
+
+```python
+poetry run pre-commit install
+```
+
+When releasing a new version please add release notes to the changelog in `CHANGELOG.md`.
+
+**Best practices for testing:**
+(1). Please run pytest from the root directory of the repo, i.e.
+
+```
+poetry run pytest tests/test_dataset.py
+```
+
+(2) To skip slow integration tests that have to wait for an async job to start.
+
+```
+poetry run pytest -m "not integration"
+```
+
+## Pydantic Models
+
+Prefer using [Pydantic](https://pydantic-docs.helpmanual.io/usage/models/) models rather than creating raw dictionaries
+or dataclasses to send or receive over the wire as JSONs. Pydantic is created with data validation in mind and provides very clear error
+messages when it encounters a problem with the payload.
+
+The Pydantic model(s) should mirror the payload to send. To represent a JSON payload that looks like this:
+
+```json
+{
+  "example_json_with_info": {
+    "metadata": {
+      "frame": 0
+    },
+    "reference_id": "frame0",
+    "url": "s3://example/scale_nucleus/2021/lidar/0038711321865000.json",
+    "type": "pointcloud"
+  },
+  "example_image_with_info": {
+    "metadata": {
+      "author": "Picasso"
+    },
+    "reference_id": "frame0",
+    "url": "s3://bucket/0038711321865000.jpg",
+    "type": "image"
+  }
+}
+```
+
+Could be represented as the following structure. Note that the field names map to the JSON keys and the usage of field
+validators (`@validator`).
+
+```python
+import os.path
+from pydantic import BaseModel, validator
+from typing import Literal
+
+
+class JsonWithInfo(BaseModel):
+    metadata: dict  # any dict is valid
+    reference_id: str
+    url: str
+    type: Literal["pointcloud", "recipe"]
+
+    @validator("url")
+    def has_json_extension(cls, v):
+        if not v.endswith(".json"):
+            raise ValueError(f"Expected '.json' extension got {v}")
+        return v
+
+
+class ImageWithInfo(BaseModel):
+    metadata: dict  # any dict is valid
+    reference_id: str
+    url: str
+    type: Literal["image", "mask"]
+
+    @validator("url")
+    def has_valid_extension(cls, v):
+        valid_extensions = {".jpg", ".jpeg", ".png", ".tiff"}
+        _, extension = os.path.splitext(v)
+        if extension not in valid_extensions:
+            raise ValueError(f"Expected extension in {valid_extensions} got {v}")
+        return v
+
+
+class ExampleNestedModel(BaseModel):
+    example_json_with_info: JsonWithInfo
+    example_image_with_info: ImageWithInfo
+
+# Usage:
+import requests
+payload = requests.get("/example")
+parsed_model = ExampleNestedModel.parse_obj(payload.json())
+requests.post("example/post_to", json=parsed_model.dict())
+```
+
+### Migrating to Pydantic
+
+- When migrating an interface from a dictionary use `nucleus.pydantic_base.DictCompatibleModel`. That allows you to get
+  the benefits of Pydantic but maintaints backwards compatibility with a Python dictionary by delegating `__getitem__` to
+  fields.
+- When migrating a frozen dataclass use `nucleus.pydantic_base.ImmutableModel`. That is a base class set up to be
+  immutable after initialization.
+
+**Updating documentation:**
+We use [Sphinx](https://www.sphinx-doc.org/en/master/) to autogenerate our API Reference from docstrings.
+
+To test your local docstring changes, run the following commands from the repository's root directory:
+
+```
+poetry shell
+cd docs
+sphinx-autobuild . ./_build/html --watch ../nucleus
+```
+
+`sphinx-autobuild` will spin up a server on localhost (port 8000 by default) that will watch for and automatically rebuild a version of the API reference based on your local docstring changes.
+
+## Custom Metrics using Shapely in scale-validate
+
+Certain metrics use `Shapely` and `rasterio` which is added as optional dependencies.
+
+```bash
+pip install scale-nucleus[metrics]
+```
+
+Note that you might need to install a local GEOS package since Shapely doesn't provide binaries bundled with GEOS for every platform.
+
+```bash
+#Mac OS
+brew install geos
+# Ubuntu/Debian flavors
+apt-get install libgeos-dev
+```
+
+To develop it locally use
+
+`poetry install --extras metrics`
+
+
+%package -n python3-scale-nucleus
+Summary:	The official Python client library for Nucleus, the Data Platform for AI
+Provides:	python-scale-nucleus
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-scale-nucleus
+# Nucleus
+
+https://dashboard.scale.com/nucleus
+
+Aggregate metrics in ML are not good enough. To improve production ML, you need to understand their qualitative failure modes, fix them by gathering more data, and curate diverse scenarios.
+
+Scale Nucleus helps you:
+
+- Visualize your data
+- Curate interesting slices within your dataset
+- Review and manage annotations
+- Measure and debug your model performance
+
+Nucleus is a new way—the right way—to develop ML models, helping us move away from the concept of one dataset and towards a paradigm of collections of scenarios.
+
+## Installation
+
+`$ pip install scale-nucleus`
+
+## CLI installation
+
+We recommend installing the CLI via `pipx` (https://pypa.github.io/pipx/installation/). This makes sure that
+the CLI does not interfere with you system packages and is accessible from your favorite terminal.
+
+For MacOS:
+
+```bash
+brew install pipx
+pipx ensurepath
+pipx install scale-nucleus
+# Optional installation of shell completion (for bash, zsh or fish)
+nu install-completions
+```
+
+Otherwise, install via pip (requires pip 19.0 or later):
+
+```bash
+python3 -m pip install --user pipx
+python3 -m pipx ensurepath
+python3 -m pipx install scale-nucleus
+# Optional installation of shell completion (for bash, zsh or fish)
+nu install-completions
+```
+
+## Common issues/FAQ
+
+### Outdated Client
+
+Nucleus is iterating rapidly and as a result we do not always perfectly preserve backwards compatibility with older versions of the client. If you run into any unexpected error, it's a good idea to upgrade your version of the client by running
+
+```
+pip install --upgrade scale-nucleus
+```
+
+## Usage
+
+For the most up to date documentation, reference: https://dashboard.scale.com/nucleus/docs/api?language=python.
+
+## For Developers
+
+Clone from github and install as editable
+
+```
+git clone git@github.com:scaleapi/nucleus-python-client.git
+cd nucleus-python-client
+pip3 install poetry
+poetry install
+```
+
+Please install the pre-commit hooks by running the following command:
+
+```python
+poetry run pre-commit install
+```
+
+When releasing a new version please add release notes to the changelog in `CHANGELOG.md`.
+
+**Best practices for testing:**
+(1). Please run pytest from the root directory of the repo, i.e.
+
+```
+poetry run pytest tests/test_dataset.py
+```
+
+(2) To skip slow integration tests that have to wait for an async job to start.
+
+```
+poetry run pytest -m "not integration"
+```
+
+## Pydantic Models
+
+Prefer using [Pydantic](https://pydantic-docs.helpmanual.io/usage/models/) models rather than creating raw dictionaries
+or dataclasses to send or receive over the wire as JSONs. Pydantic is created with data validation in mind and provides very clear error
+messages when it encounters a problem with the payload.
+
+The Pydantic model(s) should mirror the payload to send. To represent a JSON payload that looks like this:
+
+```json
+{
+  "example_json_with_info": {
+    "metadata": {
+      "frame": 0
+    },
+    "reference_id": "frame0",
+    "url": "s3://example/scale_nucleus/2021/lidar/0038711321865000.json",
+    "type": "pointcloud"
+  },
+  "example_image_with_info": {
+    "metadata": {
+      "author": "Picasso"
+    },
+    "reference_id": "frame0",
+    "url": "s3://bucket/0038711321865000.jpg",
+    "type": "image"
+  }
+}
+```
+
+Could be represented as the following structure. Note that the field names map to the JSON keys and the usage of field
+validators (`@validator`).
+
+```python
+import os.path
+from pydantic import BaseModel, validator
+from typing import Literal
+
+
+class JsonWithInfo(BaseModel):
+    metadata: dict  # any dict is valid
+    reference_id: str
+    url: str
+    type: Literal["pointcloud", "recipe"]
+
+    @validator("url")
+    def has_json_extension(cls, v):
+        if not v.endswith(".json"):
+            raise ValueError(f"Expected '.json' extension got {v}")
+        return v
+
+
+class ImageWithInfo(BaseModel):
+    metadata: dict  # any dict is valid
+    reference_id: str
+    url: str
+    type: Literal["image", "mask"]
+
+    @validator("url")
+    def has_valid_extension(cls, v):
+        valid_extensions = {".jpg", ".jpeg", ".png", ".tiff"}
+        _, extension = os.path.splitext(v)
+        if extension not in valid_extensions:
+            raise ValueError(f"Expected extension in {valid_extensions} got {v}")
+        return v
+
+
+class ExampleNestedModel(BaseModel):
+    example_json_with_info: JsonWithInfo
+    example_image_with_info: ImageWithInfo
+
+# Usage:
+import requests
+payload = requests.get("/example")
+parsed_model = ExampleNestedModel.parse_obj(payload.json())
+requests.post("example/post_to", json=parsed_model.dict())
+```
+
+### Migrating to Pydantic
+
+- When migrating an interface from a dictionary use `nucleus.pydantic_base.DictCompatibleModel`. That allows you to get
+  the benefits of Pydantic but maintaints backwards compatibility with a Python dictionary by delegating `__getitem__` to
+  fields.
+- When migrating a frozen dataclass use `nucleus.pydantic_base.ImmutableModel`. That is a base class set up to be
+  immutable after initialization.
+
+**Updating documentation:**
+We use [Sphinx](https://www.sphinx-doc.org/en/master/) to autogenerate our API Reference from docstrings.
+
+To test your local docstring changes, run the following commands from the repository's root directory:
+
+```
+poetry shell
+cd docs
+sphinx-autobuild . ./_build/html --watch ../nucleus
+```
+
+`sphinx-autobuild` will spin up a server on localhost (port 8000 by default) that will watch for and automatically rebuild a version of the API reference based on your local docstring changes.
+
+## Custom Metrics using Shapely in scale-validate
+
+Certain metrics use `Shapely` and `rasterio` which is added as optional dependencies.
+
+```bash
+pip install scale-nucleus[metrics]
+```
+
+Note that you might need to install a local GEOS package since Shapely doesn't provide binaries bundled with GEOS for every platform.
+
+```bash
+#Mac OS
+brew install geos
+# Ubuntu/Debian flavors
+apt-get install libgeos-dev
+```
+
+To develop it locally use
+
+`poetry install --extras metrics`
+
+
+%package help
+Summary:	Development documents and examples for scale-nucleus
+Provides:	python3-scale-nucleus-doc
+%description help
+# Nucleus
+
+https://dashboard.scale.com/nucleus
+
+Aggregate metrics in ML are not good enough. To improve production ML, you need to understand their qualitative failure modes, fix them by gathering more data, and curate diverse scenarios.
+
+Scale Nucleus helps you:
+
+- Visualize your data
+- Curate interesting slices within your dataset
+- Review and manage annotations
+- Measure and debug your model performance
+
+Nucleus is a new way—the right way—to develop ML models, helping us move away from the concept of one dataset and towards a paradigm of collections of scenarios.
+
+## Installation
+
+`$ pip install scale-nucleus`
+
+## CLI installation
+
+We recommend installing the CLI via `pipx` (https://pypa.github.io/pipx/installation/). This makes sure that
+the CLI does not interfere with you system packages and is accessible from your favorite terminal.
+
+For MacOS:
+
+```bash
+brew install pipx
+pipx ensurepath
+pipx install scale-nucleus
+# Optional installation of shell completion (for bash, zsh or fish)
+nu install-completions
+```
+
+Otherwise, install via pip (requires pip 19.0 or later):
+
+```bash
+python3 -m pip install --user pipx
+python3 -m pipx ensurepath
+python3 -m pipx install scale-nucleus
+# Optional installation of shell completion (for bash, zsh or fish)
+nu install-completions
+```
+
+## Common issues/FAQ
+
+### Outdated Client
+
+Nucleus is iterating rapidly and as a result we do not always perfectly preserve backwards compatibility with older versions of the client. If you run into any unexpected error, it's a good idea to upgrade your version of the client by running
+
+```
+pip install --upgrade scale-nucleus
+```
+
+## Usage
+
+For the most up to date documentation, reference: https://dashboard.scale.com/nucleus/docs/api?language=python.
+
+## For Developers
+
+Clone from github and install as editable
+
+```
+git clone git@github.com:scaleapi/nucleus-python-client.git
+cd nucleus-python-client
+pip3 install poetry
+poetry install
+```
+
+Please install the pre-commit hooks by running the following command:
+
+```python
+poetry run pre-commit install
+```
+
+When releasing a new version please add release notes to the changelog in `CHANGELOG.md`.
+
+**Best practices for testing:**
+(1). Please run pytest from the root directory of the repo, i.e.
+
+```
+poetry run pytest tests/test_dataset.py
+```
+
+(2) To skip slow integration tests that have to wait for an async job to start.
+
+```
+poetry run pytest -m "not integration"
+```
+
+## Pydantic Models
+
+Prefer using [Pydantic](https://pydantic-docs.helpmanual.io/usage/models/) models rather than creating raw dictionaries
+or dataclasses to send or receive over the wire as JSONs. Pydantic is created with data validation in mind and provides very clear error
+messages when it encounters a problem with the payload.
+
+The Pydantic model(s) should mirror the payload to send. To represent a JSON payload that looks like this:
+
+```json
+{
+  "example_json_with_info": {
+    "metadata": {
+      "frame": 0
+    },
+    "reference_id": "frame0",
+    "url": "s3://example/scale_nucleus/2021/lidar/0038711321865000.json",
+    "type": "pointcloud"
+  },
+  "example_image_with_info": {
+    "metadata": {
+      "author": "Picasso"
+    },
+    "reference_id": "frame0",
+    "url": "s3://bucket/0038711321865000.jpg",
+    "type": "image"
+  }
+}
+```
+
+Could be represented as the following structure. Note that the field names map to the JSON keys and the usage of field
+validators (`@validator`).
+
+```python
+import os.path
+from pydantic import BaseModel, validator
+from typing import Literal
+
+
+class JsonWithInfo(BaseModel):
+    metadata: dict  # any dict is valid
+    reference_id: str
+    url: str
+    type: Literal["pointcloud", "recipe"]
+
+    @validator("url")
+    def has_json_extension(cls, v):
+        if not v.endswith(".json"):
+            raise ValueError(f"Expected '.json' extension got {v}")
+        return v
+
+
+class ImageWithInfo(BaseModel):
+    metadata: dict  # any dict is valid
+    reference_id: str
+    url: str
+    type: Literal["image", "mask"]
+
+    @validator("url")
+    def has_valid_extension(cls, v):
+        valid_extensions = {".jpg", ".jpeg", ".png", ".tiff"}
+        _, extension = os.path.splitext(v)
+        if extension not in valid_extensions:
+            raise ValueError(f"Expected extension in {valid_extensions} got {v}")
+        return v
+
+
+class ExampleNestedModel(BaseModel):
+    example_json_with_info: JsonWithInfo
+    example_image_with_info: ImageWithInfo
+
+# Usage:
+import requests
+payload = requests.get("/example")
+parsed_model = ExampleNestedModel.parse_obj(payload.json())
+requests.post("example/post_to", json=parsed_model.dict())
+```
+
+### Migrating to Pydantic
+
+- When migrating an interface from a dictionary use `nucleus.pydantic_base.DictCompatibleModel`. That allows you to get
+  the benefits of Pydantic but maintaints backwards compatibility with a Python dictionary by delegating `__getitem__` to
+  fields.
+- When migrating a frozen dataclass use `nucleus.pydantic_base.ImmutableModel`. That is a base class set up to be
+  immutable after initialization.
+
+**Updating documentation:**
+We use [Sphinx](https://www.sphinx-doc.org/en/master/) to autogenerate our API Reference from docstrings.
+
+To test your local docstring changes, run the following commands from the repository's root directory:
+
+```
+poetry shell
+cd docs
+sphinx-autobuild . ./_build/html --watch ../nucleus
+```
+
+`sphinx-autobuild` will spin up a server on localhost (port 8000 by default) that will watch for and automatically rebuild a version of the API reference based on your local docstring changes.
+
+## Custom Metrics using Shapely in scale-validate
+
+Certain metrics use `Shapely` and `rasterio` which is added as optional dependencies.
+
+```bash
+pip install scale-nucleus[metrics]
+```
+
+Note that you might need to install a local GEOS package since Shapely doesn't provide binaries bundled with GEOS for every platform.
+
+```bash
+#Mac OS
+brew install geos
+# Ubuntu/Debian flavors
+apt-get install libgeos-dev
+```
+
+To develop it locally use
+
+`poetry install --extras metrics`
+
+
+%prep
+%autosetup -n scale-nucleus-0.15.4
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-scale-nucleus -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.15.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..a4c63e2
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e52bea74d87f807ae59f9db6138d6aec  scale-nucleus-0.15.4.tar.gz