import os
import unittest

from distutils.util import strtobool

from transformers.file_utils import _tf_available, _torch_available


SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"


def parse_flag_from_env(key, default=False):
    try:
        value = os.environ[key]
    except KeyError:
        # KEY isn't set, default to `default`.
        _value = default
    else:
        # KEY is set, convert it to True or False.
        try:
            _value = strtobool(value)
        except ValueError:
            # More values are supported, but let's keep the message simple.
            raise ValueError("If set, {} must be yes or no.".format(key))
    return _value

_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_custom_tokenizers = parse_flag_from_env("RUN_CUSTOM_TOKENIZERS", default=False)


def slow(test_case):
    """
    Decorator marking a test as slow.

    Slow tests are skipped by default. Set the RUN_SLOW environment variable
    to a truthy value to run them.

    """
    if not _run_slow_tests:
        test_case = unittest.skip("test is slow")(test_case)
    return test_case


def custom_tokenizers(test_case):
    """
    Decorator marking a test for a custom tokenizer.

    Custom tokenizers require additional dependencies, and are skipped
    by default. Set the RUN_CUSTOM_TOKENIZERS environment variable
    to a truthy value to run them.
    """
    if not _run_custom_tokenizers:
        test_case = unittest.skip("test of custom tokenizers")(test_case)
    return test_case


def require_torch(test_case):
    """
    Decorator marking a test that requires PyTorch.

    These tests are skipped when PyTorch isn't installed.

    """
    if not _torch_available:
        test_case = unittest.skip("test requires PyTorch")(test_case)
    return test_case


def require_tf(test_case):
    """
    Decorator marking a test that requires TensorFlow.

    These tests are skipped when TensorFlow isn't installed.

    """
    if not _tf_available:
        test_case = unittest.skip("test requires TensorFlow")(test_case)
    return test_case


if _torch_available:
    # Set the USE_CUDA environment variable to select a GPU.
    torch_device = "cuda" if parse_flag_from_env("USE_CUDA") else "cpu"
else:
    torch_device = None
