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@@ -0,0 +1 @@
+/ai2-tango-1.2.1.tar.gz
diff --git a/python-ai2-tango.spec b/python-ai2-tango.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-ai2-tango
+Version:	1.2.1
+Release:	1
+Summary:	A library for choreographing your machine learning research.
+License:	Apache
+URL:		https://github.com/allenai/tango
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f0/0d/12f9f4b125aeff7c4dc54e25c671afaaf63748a74109a460c524493d730f/ai2-tango-1.2.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-cached-path
+Requires:	python3-rjsonnet
+Requires:	python3-GitPython
+Requires:	python3-PyYAML
+Requires:	python3-dill
+Requires:	python3-base58
+Requires:	python3-xxhash
+Requires:	python3-filelock
+Requires:	python3-click
+Requires:	python3-click-help-colors
+Requires:	python3-rich
+Requires:	python3-tqdm
+Requires:	python3-more-itertools
+Requires:	python3-sqlitedict
+Requires:	python3-glob2
+Requires:	python3-petname
+Requires:	python3-pytz
+Requires:	python3-torch
+Requires:	python3-numpy
+Requires:	python3-datasets
+Requires:	python3-wandb
+Requires:	python3-retry
+Requires:	python3-transformers
+Requires:	python3-sentencepiece
+Requires:	python3-fairscale
+Requires:	python3-beaker-py
+Requires:	python3-google-cloud-storage
+Requires:	python3-google-cloud-datastore
+Requires:	python3-sacremoses
+Requires:	python3-jax
+Requires:	python3-flax
+Requires:	python3-optax
+Requires:	python3-tensorflow-cpu
+Requires:	python3-beaker-py
+Requires:	python3-datasets
+Requires:	python3-flake8
+Requires:	python3-mypy
+Requires:	python3-types-PyYAML
+Requires:	python3-types-setuptools
+Requires:	python3-types-pytz
+Requires:	python3-types-retry
+Requires:	python3-black
+Requires:	python3-isort
+Requires:	python3-pytest
+Requires:	python3-pytest-sphinx
+Requires:	python3-flaky
+Requires:	python3-pytest-cov
+Requires:	python3-coverage
+Requires:	python3-codecov
+Requires:	python3-twine
+Requires:	python3-setuptools
+Requires:	python3-wheel
+Requires:	python3-Sphinx
+Requires:	python3-furo
+Requires:	python3-myst-parser
+Requires:	python3-sphinx-copybutton
+Requires:	python3-sphinx-autobuild
+Requires:	python3-sphinx-autodoc-typehints
+Requires:	python3-packaging
+Requires:	python3-torchmetrics
+Requires:	python3-torch
+Requires:	python3-numpy
+Requires:	python3-fairscale
+Requires:	python3-datasets
+Requires:	python3-jax
+Requires:	python3-flax
+Requires:	python3-optax
+Requires:	python3-tensorflow-cpu
+Requires:	python3-google-cloud-storage
+Requires:	python3-google-cloud-datastore
+Requires:	python3-torch
+Requires:	python3-numpy
+Requires:	python3-torch
+Requires:	python3-numpy
+Requires:	python3-datasets
+Requires:	python3-transformers
+Requires:	python3-sentencepiece
+Requires:	python3-sacremoses
+Requires:	python3-wandb
+Requires:	python3-retry
+
+%description
+<div align="center">
+<br>
+<img src="https://raw.githubusercontent.com/allenai/tango/main/docs/source/_static/tango_final_horizontal.png" width="600"/>
+<br>
+<br>
+<p>
+<!-- start tagline -->
+AI2 Tango replaces messy directories and spreadsheets full of file versions by organizing experiments into discrete steps that can be cached and reused throughout the lifetime of a research project.
+<!-- end tagline -->
+</p>
+<hr/>
+<a href="https://github.com/allenai/tango/actions">
+    <img alt="CI" src="https://github.com/allenai/tango/workflows/CI/badge.svg?event=push&branch=main">
+</a>
+<a href="https://pypi.org/project/ai2-tango/">
+    <img alt="PyPI" src="https://img.shields.io/pypi/v/ai2-tango">
+</a>
+<a href="https://ai2-tango.readthedocs.io/en/latest/?badge=latest">
+    <img src="https://readthedocs.org/projects/ai2-tango/badge/?version=latest" alt="Documentation Status" />
+</a>
+<a href="https://github.com/allenai/tango/blob/main/LICENSE">
+    <img alt="License" src="https://img.shields.io/github/license/allenai/tango.svg?color=blue&cachedrop">
+</a>
+<br/>
+</div>
+
+## Quick links
+
+- [Documentation](https://ai2-tango.readthedocs.io/)
+- [PyPI Package](https://pypi.org/project/ai2-tango/)
+- [Contributing](https://github.com/allenai/tango/blob/main/CONTRIBUTING.md)
+- [License](https://github.com/allenai/tango/blob/main/LICENSE)
+
+## In this README
+
+- [Quick start](#quick-start)
+- [Installation](#installation)
+  - [Installing with PIP](#installing-with-pip)
+  - [Installing with Conda](#installing-with-conda)
+  - [Installing from source](#installing-from-source)
+  - [Checking your installation](#checking-your-installation)
+  - [Docker image](#docker-image)
+- [FAQ](#faq)
+- [Team](#team)
+- [License](#license)
+
+## Quick start
+
+Create a Tango step:
+
+```python
+# hello.py
+
+from tango import step
+
+@step()
+def hello(name: str) -> str:
+    message = f"Hello, {name}!"
+    print(message)
+    return message
+```
+
+And create a corresponding experiment configuration file:
+
+```jsonnet
+// hello.jsonnet
+
+{
+  steps: {
+    hello: {
+      type: "hello",
+      name: "World",
+    }
+  }
+}
+```
+
+Then run the experiment using a local workspace to cache the result:
+
+```bash
+tango run hello.jsonnet -w /tmp/workspace
+```
+
+You'll see something like this in the output:
+
+```
+Starting new run expert-llama
+● Starting step "hello"...
+Hello, World!
+✓ Finished step "hello"
+✓ Finished run expert-llama
+```
+
+If you run this a second time the output will now look like this:
+
+```
+Starting new run open-crab
+✓ Found output for step "hello" in cache...
+✓ Finished run open-crab
+```
+
+You won't see "Hello, World!" this time because the result of the step was found in the cache, so it wasn't run again.
+
+For a more detailed introduction check out the [First Steps](https://ai2-tango.readthedocs.io/en/latest/first_steps.html) walk-through.
+
+## Installation
+
+<!-- start install -->
+
+**ai2-tango** requires Python 3.8 or later.
+
+### Installing with `pip`
+
+**ai2-tango** is available [on PyPI](https://pypi.org/project/ai2-tango/). Just run
+
+```bash
+pip install ai2-tango
+```
+
+To install with a specific integration, such as `torch` for example, run
+
+```bash
+pip install 'ai2-tango[torch]'
+```
+
+To install with all integrations, run
+
+```bash
+pip install 'ai2-tango[all]'
+```
+
+### Installing with `conda`
+
+**ai2-tango** is available on conda-forge. You can install just the base package with
+
+```bash
+conda install tango -c conda-forge
+```
+
+You can pick and choose from the integrations with one of these:
+
+```bash
+conda install tango-datasets -c conda-forge
+conda install tango-torch -c conda-forge
+conda install tango-wandb -c conda-forge
+```
+
+You can also install everything:
+
+```bash
+conda install tango-all -c conda-forge
+```
+
+Even though **ai2-tango** itself is quite small, installing everything will pull in a lot of dependencies.
+Don't be surprised if this takes a while!
+
+### Installing from source
+
+To install **ai2-tango** from source, first clone [the repository](https://github.com/allenai/tango):
+
+```bash
+git clone https://github.com/allenai/tango.git
+cd tango
+```
+
+Then run
+
+```bash
+pip install -e '.[all]'
+```
+
+To install with only a specific integration, such as `torch` for example, run
+
+```bash
+pip install -e '.[torch]'
+```
+
+Or to install just the base tango library, you can run
+
+```bash
+pip install -e .
+```
+
+### Checking your installation
+
+Run
+
+```bash
+tango info
+```
+
+to check your installation.
+
+### Docker image
+
+You can build a Docker image suitable for tango projects by using [the official Dockerfile](https://github.com/allenai/tango/blob/main/Dockerfile) as a starting point for your own Dockerfile, or you can simply use one of our [prebuilt images](https://github.com/allenai/tango/pkgs/container/tango) as a base image in your Dockerfile. For example:
+
+```Dockerfile
+# Start from a prebuilt tango base image.
+# You can choose the right tag from the available options here:
+# https://github.com/allenai/tango/pkgs/container/tango/versions
+FROM ghcr.io/allenai/tango:cuda11.3
+
+# Install your project's additional requirements.
+COPY requirements.txt .
+RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt
+
+# Install source code.
+# This instruction copies EVERYTHING in the current directory (build context),
+# which may not be what you want. Consider using a ".dockerignore" file to
+# exclude files and directories that you don't want on the image.
+COPY . .
+```
+
+Make sure to choose the right base image for your use case depending on the version of tango you're using and the CUDA version that your host machine supports.
+You can see a list of all available image tags [on GitHub](https://github.com/allenai/tango/pkgs/container/tango/versions).
+
+<!-- end install -->
+
+## FAQ
+
+<!-- start faq -->
+
+### Why is the library named Tango?
+
+The motivation behind this library is that we can make research easier by composing it into well-defined steps.  What happens when you choreograph a number of steps together?  Well, you get a dance.  And since our [team's leader](https://nasmith.github.io/) is part of a tango band, "AI2 Tango" was an obvious choice!
+
+### How can I debug my steps through the Tango CLI?
+
+You can run the `tango` command through [pdb](https://docs.python.org/3/library/pdb.html). For example:
+
+```bash
+python -m pdb -m tango run config.jsonnet
+```
+
+### How is Tango different from [Metaflow](https://metaflow.org), [Airflow](https://airflow.apache.org), or [redun](https://github.com/insitro/redun)?
+
+We've found that existing DAG execution engines like these tools are great for production workflows but not as well suited for messy, collaborative research projects
+where code is changing constantly. AI2 Tango was built *specifically* for these kinds of research projects.
+
+### How does Tango's caching mechanism work?
+
+AI2 Tango caches the results of steps based on the `unique_id` of the step. The `unique_id` is essentially a hash of all of the inputs to the step along with:
+
+1. the step class's fully qualified name, and
+2. the step class's `VERSION` class variable (an arbitrary string).
+
+Unlike other workflow engines like [redun](https://github.com/insitro/redun), Tango does *not* take into account the source code of the class itself (other than its fully qualified name) because we've found that using a hash of the source code bytes is way too sensitive and less transparent for users.
+When you change the source code of your step in a meaningful way you can just manually change the `VERSION` class variable to indicate to Tango
+that the step has been updated.
+
+<!-- end faq -->
+
+## Team
+
+<!-- start team -->
+
+**ai2-tango** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).
+AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
+To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/tango/graphs/contributors) page.
+
+<!-- end team -->
+
+## License
+
+<!-- start license -->
+
+**ai2-tango** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+A full copy of the license can be found [on GitHub](https://github.com/allenai/tango/blob/main/LICENSE).
+
+<!-- end license -->
+
+
+%package -n python3-ai2-tango
+Summary:	A library for choreographing your machine learning research.
+Provides:	python-ai2-tango
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ai2-tango
+<div align="center">
+<br>
+<img src="https://raw.githubusercontent.com/allenai/tango/main/docs/source/_static/tango_final_horizontal.png" width="600"/>
+<br>
+<br>
+<p>
+<!-- start tagline -->
+AI2 Tango replaces messy directories and spreadsheets full of file versions by organizing experiments into discrete steps that can be cached and reused throughout the lifetime of a research project.
+<!-- end tagline -->
+</p>
+<hr/>
+<a href="https://github.com/allenai/tango/actions">
+    <img alt="CI" src="https://github.com/allenai/tango/workflows/CI/badge.svg?event=push&branch=main">
+</a>
+<a href="https://pypi.org/project/ai2-tango/">
+    <img alt="PyPI" src="https://img.shields.io/pypi/v/ai2-tango">
+</a>
+<a href="https://ai2-tango.readthedocs.io/en/latest/?badge=latest">
+    <img src="https://readthedocs.org/projects/ai2-tango/badge/?version=latest" alt="Documentation Status" />
+</a>
+<a href="https://github.com/allenai/tango/blob/main/LICENSE">
+    <img alt="License" src="https://img.shields.io/github/license/allenai/tango.svg?color=blue&cachedrop">
+</a>
+<br/>
+</div>
+
+## Quick links
+
+- [Documentation](https://ai2-tango.readthedocs.io/)
+- [PyPI Package](https://pypi.org/project/ai2-tango/)
+- [Contributing](https://github.com/allenai/tango/blob/main/CONTRIBUTING.md)
+- [License](https://github.com/allenai/tango/blob/main/LICENSE)
+
+## In this README
+
+- [Quick start](#quick-start)
+- [Installation](#installation)
+  - [Installing with PIP](#installing-with-pip)
+  - [Installing with Conda](#installing-with-conda)
+  - [Installing from source](#installing-from-source)
+  - [Checking your installation](#checking-your-installation)
+  - [Docker image](#docker-image)
+- [FAQ](#faq)
+- [Team](#team)
+- [License](#license)
+
+## Quick start
+
+Create a Tango step:
+
+```python
+# hello.py
+
+from tango import step
+
+@step()
+def hello(name: str) -> str:
+    message = f"Hello, {name}!"
+    print(message)
+    return message
+```
+
+And create a corresponding experiment configuration file:
+
+```jsonnet
+// hello.jsonnet
+
+{
+  steps: {
+    hello: {
+      type: "hello",
+      name: "World",
+    }
+  }
+}
+```
+
+Then run the experiment using a local workspace to cache the result:
+
+```bash
+tango run hello.jsonnet -w /tmp/workspace
+```
+
+You'll see something like this in the output:
+
+```
+Starting new run expert-llama
+● Starting step "hello"...
+Hello, World!
+✓ Finished step "hello"
+✓ Finished run expert-llama
+```
+
+If you run this a second time the output will now look like this:
+
+```
+Starting new run open-crab
+✓ Found output for step "hello" in cache...
+✓ Finished run open-crab
+```
+
+You won't see "Hello, World!" this time because the result of the step was found in the cache, so it wasn't run again.
+
+For a more detailed introduction check out the [First Steps](https://ai2-tango.readthedocs.io/en/latest/first_steps.html) walk-through.
+
+## Installation
+
+<!-- start install -->
+
+**ai2-tango** requires Python 3.8 or later.
+
+### Installing with `pip`
+
+**ai2-tango** is available [on PyPI](https://pypi.org/project/ai2-tango/). Just run
+
+```bash
+pip install ai2-tango
+```
+
+To install with a specific integration, such as `torch` for example, run
+
+```bash
+pip install 'ai2-tango[torch]'
+```
+
+To install with all integrations, run
+
+```bash
+pip install 'ai2-tango[all]'
+```
+
+### Installing with `conda`
+
+**ai2-tango** is available on conda-forge. You can install just the base package with
+
+```bash
+conda install tango -c conda-forge
+```
+
+You can pick and choose from the integrations with one of these:
+
+```bash
+conda install tango-datasets -c conda-forge
+conda install tango-torch -c conda-forge
+conda install tango-wandb -c conda-forge
+```
+
+You can also install everything:
+
+```bash
+conda install tango-all -c conda-forge
+```
+
+Even though **ai2-tango** itself is quite small, installing everything will pull in a lot of dependencies.
+Don't be surprised if this takes a while!
+
+### Installing from source
+
+To install **ai2-tango** from source, first clone [the repository](https://github.com/allenai/tango):
+
+```bash
+git clone https://github.com/allenai/tango.git
+cd tango
+```
+
+Then run
+
+```bash
+pip install -e '.[all]'
+```
+
+To install with only a specific integration, such as `torch` for example, run
+
+```bash
+pip install -e '.[torch]'
+```
+
+Or to install just the base tango library, you can run
+
+```bash
+pip install -e .
+```
+
+### Checking your installation
+
+Run
+
+```bash
+tango info
+```
+
+to check your installation.
+
+### Docker image
+
+You can build a Docker image suitable for tango projects by using [the official Dockerfile](https://github.com/allenai/tango/blob/main/Dockerfile) as a starting point for your own Dockerfile, or you can simply use one of our [prebuilt images](https://github.com/allenai/tango/pkgs/container/tango) as a base image in your Dockerfile. For example:
+
+```Dockerfile
+# Start from a prebuilt tango base image.
+# You can choose the right tag from the available options here:
+# https://github.com/allenai/tango/pkgs/container/tango/versions
+FROM ghcr.io/allenai/tango:cuda11.3
+
+# Install your project's additional requirements.
+COPY requirements.txt .
+RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt
+
+# Install source code.
+# This instruction copies EVERYTHING in the current directory (build context),
+# which may not be what you want. Consider using a ".dockerignore" file to
+# exclude files and directories that you don't want on the image.
+COPY . .
+```
+
+Make sure to choose the right base image for your use case depending on the version of tango you're using and the CUDA version that your host machine supports.
+You can see a list of all available image tags [on GitHub](https://github.com/allenai/tango/pkgs/container/tango/versions).
+
+<!-- end install -->
+
+## FAQ
+
+<!-- start faq -->
+
+### Why is the library named Tango?
+
+The motivation behind this library is that we can make research easier by composing it into well-defined steps.  What happens when you choreograph a number of steps together?  Well, you get a dance.  And since our [team's leader](https://nasmith.github.io/) is part of a tango band, "AI2 Tango" was an obvious choice!
+
+### How can I debug my steps through the Tango CLI?
+
+You can run the `tango` command through [pdb](https://docs.python.org/3/library/pdb.html). For example:
+
+```bash
+python -m pdb -m tango run config.jsonnet
+```
+
+### How is Tango different from [Metaflow](https://metaflow.org), [Airflow](https://airflow.apache.org), or [redun](https://github.com/insitro/redun)?
+
+We've found that existing DAG execution engines like these tools are great for production workflows but not as well suited for messy, collaborative research projects
+where code is changing constantly. AI2 Tango was built *specifically* for these kinds of research projects.
+
+### How does Tango's caching mechanism work?
+
+AI2 Tango caches the results of steps based on the `unique_id` of the step. The `unique_id` is essentially a hash of all of the inputs to the step along with:
+
+1. the step class's fully qualified name, and
+2. the step class's `VERSION` class variable (an arbitrary string).
+
+Unlike other workflow engines like [redun](https://github.com/insitro/redun), Tango does *not* take into account the source code of the class itself (other than its fully qualified name) because we've found that using a hash of the source code bytes is way too sensitive and less transparent for users.
+When you change the source code of your step in a meaningful way you can just manually change the `VERSION` class variable to indicate to Tango
+that the step has been updated.
+
+<!-- end faq -->
+
+## Team
+
+<!-- start team -->
+
+**ai2-tango** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).
+AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
+To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/tango/graphs/contributors) page.
+
+<!-- end team -->
+
+## License
+
+<!-- start license -->
+
+**ai2-tango** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+A full copy of the license can be found [on GitHub](https://github.com/allenai/tango/blob/main/LICENSE).
+
+<!-- end license -->
+
+
+%package help
+Summary:	Development documents and examples for ai2-tango
+Provides:	python3-ai2-tango-doc
+%description help
+<div align="center">
+<br>
+<img src="https://raw.githubusercontent.com/allenai/tango/main/docs/source/_static/tango_final_horizontal.png" width="600"/>
+<br>
+<br>
+<p>
+<!-- start tagline -->
+AI2 Tango replaces messy directories and spreadsheets full of file versions by organizing experiments into discrete steps that can be cached and reused throughout the lifetime of a research project.
+<!-- end tagline -->
+</p>
+<hr/>
+<a href="https://github.com/allenai/tango/actions">
+    <img alt="CI" src="https://github.com/allenai/tango/workflows/CI/badge.svg?event=push&branch=main">
+</a>
+<a href="https://pypi.org/project/ai2-tango/">
+    <img alt="PyPI" src="https://img.shields.io/pypi/v/ai2-tango">
+</a>
+<a href="https://ai2-tango.readthedocs.io/en/latest/?badge=latest">
+    <img src="https://readthedocs.org/projects/ai2-tango/badge/?version=latest" alt="Documentation Status" />
+</a>
+<a href="https://github.com/allenai/tango/blob/main/LICENSE">
+    <img alt="License" src="https://img.shields.io/github/license/allenai/tango.svg?color=blue&cachedrop">
+</a>
+<br/>
+</div>
+
+## Quick links
+
+- [Documentation](https://ai2-tango.readthedocs.io/)
+- [PyPI Package](https://pypi.org/project/ai2-tango/)
+- [Contributing](https://github.com/allenai/tango/blob/main/CONTRIBUTING.md)
+- [License](https://github.com/allenai/tango/blob/main/LICENSE)
+
+## In this README
+
+- [Quick start](#quick-start)
+- [Installation](#installation)
+  - [Installing with PIP](#installing-with-pip)
+  - [Installing with Conda](#installing-with-conda)
+  - [Installing from source](#installing-from-source)
+  - [Checking your installation](#checking-your-installation)
+  - [Docker image](#docker-image)
+- [FAQ](#faq)
+- [Team](#team)
+- [License](#license)
+
+## Quick start
+
+Create a Tango step:
+
+```python
+# hello.py
+
+from tango import step
+
+@step()
+def hello(name: str) -> str:
+    message = f"Hello, {name}!"
+    print(message)
+    return message
+```
+
+And create a corresponding experiment configuration file:
+
+```jsonnet
+// hello.jsonnet
+
+{
+  steps: {
+    hello: {
+      type: "hello",
+      name: "World",
+    }
+  }
+}
+```
+
+Then run the experiment using a local workspace to cache the result:
+
+```bash
+tango run hello.jsonnet -w /tmp/workspace
+```
+
+You'll see something like this in the output:
+
+```
+Starting new run expert-llama
+● Starting step "hello"...
+Hello, World!
+✓ Finished step "hello"
+✓ Finished run expert-llama
+```
+
+If you run this a second time the output will now look like this:
+
+```
+Starting new run open-crab
+✓ Found output for step "hello" in cache...
+✓ Finished run open-crab
+```
+
+You won't see "Hello, World!" this time because the result of the step was found in the cache, so it wasn't run again.
+
+For a more detailed introduction check out the [First Steps](https://ai2-tango.readthedocs.io/en/latest/first_steps.html) walk-through.
+
+## Installation
+
+<!-- start install -->
+
+**ai2-tango** requires Python 3.8 or later.
+
+### Installing with `pip`
+
+**ai2-tango** is available [on PyPI](https://pypi.org/project/ai2-tango/). Just run
+
+```bash
+pip install ai2-tango
+```
+
+To install with a specific integration, such as `torch` for example, run
+
+```bash
+pip install 'ai2-tango[torch]'
+```
+
+To install with all integrations, run
+
+```bash
+pip install 'ai2-tango[all]'
+```
+
+### Installing with `conda`
+
+**ai2-tango** is available on conda-forge. You can install just the base package with
+
+```bash
+conda install tango -c conda-forge
+```
+
+You can pick and choose from the integrations with one of these:
+
+```bash
+conda install tango-datasets -c conda-forge
+conda install tango-torch -c conda-forge
+conda install tango-wandb -c conda-forge
+```
+
+You can also install everything:
+
+```bash
+conda install tango-all -c conda-forge
+```
+
+Even though **ai2-tango** itself is quite small, installing everything will pull in a lot of dependencies.
+Don't be surprised if this takes a while!
+
+### Installing from source
+
+To install **ai2-tango** from source, first clone [the repository](https://github.com/allenai/tango):
+
+```bash
+git clone https://github.com/allenai/tango.git
+cd tango
+```
+
+Then run
+
+```bash
+pip install -e '.[all]'
+```
+
+To install with only a specific integration, such as `torch` for example, run
+
+```bash
+pip install -e '.[torch]'
+```
+
+Or to install just the base tango library, you can run
+
+```bash
+pip install -e .
+```
+
+### Checking your installation
+
+Run
+
+```bash
+tango info
+```
+
+to check your installation.
+
+### Docker image
+
+You can build a Docker image suitable for tango projects by using [the official Dockerfile](https://github.com/allenai/tango/blob/main/Dockerfile) as a starting point for your own Dockerfile, or you can simply use one of our [prebuilt images](https://github.com/allenai/tango/pkgs/container/tango) as a base image in your Dockerfile. For example:
+
+```Dockerfile
+# Start from a prebuilt tango base image.
+# You can choose the right tag from the available options here:
+# https://github.com/allenai/tango/pkgs/container/tango/versions
+FROM ghcr.io/allenai/tango:cuda11.3
+
+# Install your project's additional requirements.
+COPY requirements.txt .
+RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt
+
+# Install source code.
+# This instruction copies EVERYTHING in the current directory (build context),
+# which may not be what you want. Consider using a ".dockerignore" file to
+# exclude files and directories that you don't want on the image.
+COPY . .
+```
+
+Make sure to choose the right base image for your use case depending on the version of tango you're using and the CUDA version that your host machine supports.
+You can see a list of all available image tags [on GitHub](https://github.com/allenai/tango/pkgs/container/tango/versions).
+
+<!-- end install -->
+
+## FAQ
+
+<!-- start faq -->
+
+### Why is the library named Tango?
+
+The motivation behind this library is that we can make research easier by composing it into well-defined steps.  What happens when you choreograph a number of steps together?  Well, you get a dance.  And since our [team's leader](https://nasmith.github.io/) is part of a tango band, "AI2 Tango" was an obvious choice!
+
+### How can I debug my steps through the Tango CLI?
+
+You can run the `tango` command through [pdb](https://docs.python.org/3/library/pdb.html). For example:
+
+```bash
+python -m pdb -m tango run config.jsonnet
+```
+
+### How is Tango different from [Metaflow](https://metaflow.org), [Airflow](https://airflow.apache.org), or [redun](https://github.com/insitro/redun)?
+
+We've found that existing DAG execution engines like these tools are great for production workflows but not as well suited for messy, collaborative research projects
+where code is changing constantly. AI2 Tango was built *specifically* for these kinds of research projects.
+
+### How does Tango's caching mechanism work?
+
+AI2 Tango caches the results of steps based on the `unique_id` of the step. The `unique_id` is essentially a hash of all of the inputs to the step along with:
+
+1. the step class's fully qualified name, and
+2. the step class's `VERSION` class variable (an arbitrary string).
+
+Unlike other workflow engines like [redun](https://github.com/insitro/redun), Tango does *not* take into account the source code of the class itself (other than its fully qualified name) because we've found that using a hash of the source code bytes is way too sensitive and less transparent for users.
+When you change the source code of your step in a meaningful way you can just manually change the `VERSION` class variable to indicate to Tango
+that the step has been updated.
+
+<!-- end faq -->
+
+## Team
+
+<!-- start team -->
+
+**ai2-tango** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).
+AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
+To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/tango/graphs/contributors) page.
+
+<!-- end team -->
+
+## License
+
+<!-- start license -->
+
+**ai2-tango** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+A full copy of the license can be found [on GitHub](https://github.com/allenai/tango/blob/main/LICENSE).
+
+<!-- end license -->
+
+
+%prep
+%autosetup -n ai2-tango-1.2.1
+
+%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-ai2-tango -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..6225303
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+faa9c628e057896526f15f7f0771acb1  ai2-tango-1.2.1.tar.gz