src/transformers/commands/env.py/EnvironmentCommand/run
class EnvironmentCommand:    def run(self):
        safetensors_version = "not installed"
        if is_safetensors_available():
            import safetensors

            safetensors_version = safetensors.__version__
        elif importlib.util.find_spec("safetensors") is not None:
            import safetensors

            safetensors_version = f"{safetensors.__version__} but is ignored because of PyTorch version too old."

        accelerate_version = "not installed"
        accelerate_config = accelerate_config_str = "not found"
        if is_accelerate_available():
            import accelerate
            from accelerate.commands.config import default_config_file, load_config_from_file

            accelerate_version = accelerate.__version__
            # Get the default from the config file.
            if self._accelerate_config_file is not None or os.path.isfile(default_config_file):
                accelerate_config = load_config_from_file(self._accelerate_config_file).to_dict()

            accelerate_config_str = (
                "\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()])
                if isinstance(accelerate_config, dict)
                else f"\t{accelerate_config}"
            )

        pt_version = "not installed"
        pt_cuda_available = "NA"
        if is_torch_available():
            import torch

            pt_version = torch.__version__
            pt_cuda_available = torch.cuda.is_available()

        tf_version = "not installed"
        tf_cuda_available = "NA"
        if is_tf_available():
            import tensorflow as tf

            tf_version = tf.__version__
            try:
                # deprecated in v2.1
                tf_cuda_available = tf.test.is_gpu_available()
            except AttributeError:
                # returns list of devices, convert to bool
                tf_cuda_available = bool(tf.config.list_physical_devices("GPU"))

        flax_version = "not installed"
        jax_version = "not installed"
        jaxlib_version = "not installed"
        jax_backend = "NA"
        if is_flax_available():
            import flax
            import jax
            import jaxlib

            flax_version = flax.__version__
            jax_version = jax.__version__
            jaxlib_version = jaxlib.__version__
            jax_backend = jax.lib.xla_bridge.get_backend().platform

        info = {
            "`transformers` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "Huggingface_hub version": huggingface_hub.__version__,
            "Safetensors version": f"{safetensors_version}",
            "Accelerate version": f"{accelerate_version}",
            "Accelerate config": f"{accelerate_config_str}",
            "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
            "Tensorflow version (GPU?)": f"{tf_version} ({tf_cuda_available})",
            "Flax version (CPU?/GPU?/TPU?)": f"{flax_version} ({jax_backend})",
            "Jax version": f"{jax_version}",
            "JaxLib version": f"{jaxlib_version}",
            "Using GPU in script?": "<fill in>",
            "Using distributed or parallel set-up in script?": "<fill in>",
        }

        print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
        print(self.format_dict(info))

        return info