# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""General utilities."""
import importlib
import operator
import platform
import sys
from importlib.util import find_spec
from typing import Callable

import pkg_resources
import torch
from packaging.version import Version
from pkg_resources import DistributionNotFound


def _package_available(package_name: str) -> bool:
    """Check if a package is available in your environment.

    >>> _package_available('os')
    True
    >>> _package_available('bla')
    False
    """
    try:
        return find_spec(package_name) is not None
    except ModuleNotFoundError:
        return False


def _module_available(module_path: str) -> bool:
    """Check if a module path is available in your environment.

    >>> _module_available('os')
    True
    >>> _module_available('os.bla')
    False
    >>> _module_available('bla.bla')
    False
    """
    module_names = module_path.split(".")
    if not _package_available(module_names[0]):
        return False
    try:
        importlib.import_module(module_path)
    except ImportError:
        return False
    return True


def _compare_version(package: str, op: Callable, version: str, use_base_version: bool = False) -> bool:
    """Compare package version with some requirements.

    >>> _compare_version("torch", operator.ge, "0.1")
    True
    >>> _compare_version("does_not_exist", operator.ge, "0.0")
    False
    """
    try:
        pkg = importlib.import_module(package)
    except (ImportError, DistributionNotFound):
        return False
    try:
        if hasattr(pkg, "__version__"):
            pkg_version = Version(pkg.__version__)
        else:
            # try pkg_resources to infer version
            pkg_version = Version(pkg_resources.get_distribution(package).version)
    except TypeError:
        # this is mocked by Sphinx, so it should return True to generate all summaries
        return True
    if use_base_version:
        pkg_version = Version(pkg_version.base_version)
    return op(pkg_version, Version(version))


_IS_WINDOWS = platform.system() == "Windows"
_IS_INTERACTIVE = hasattr(sys, "ps1")  # https://stackoverflow.com/a/64523765
_PYTHON_GREATER_EQUAL_3_8_0 = Version(platform.python_version()) >= Version("3.8.0")
_TORCH_GREATER_EQUAL_1_8_1 = _compare_version("torch", operator.ge, "1.8.1")
_TORCH_GREATER_EQUAL_1_9 = _compare_version("torch", operator.ge, "1.9.0")
_TORCH_GREATER_EQUAL_1_9_1 = _compare_version("torch", operator.ge, "1.9.1")
_TORCH_GREATER_EQUAL_1_10 = _compare_version("torch", operator.ge, "1.10.0")
_TORCH_LESSER_EQUAL_1_10_2 = _compare_version("torch", operator.le, "1.10.2")
_TORCH_GREATER_EQUAL_1_11 = _compare_version("torch", operator.ge, "1.11.0")

_APEX_AVAILABLE = _module_available("apex.amp")
_BAGUA_AVAILABLE = _package_available("bagua")
_DEEPSPEED_AVAILABLE = _package_available("deepspeed")
_DEEPSPEED_GREATER_EQUAL_0_5_9 = _DEEPSPEED_AVAILABLE and _compare_version("deepspeed", operator.ge, "0.5.9")
_DEEPSPEED_GREATER_EQUAL_0_6 = _DEEPSPEED_AVAILABLE and _compare_version("deepspeed", operator.ge, "0.6.0")
_DOCSTRING_PARSER_AVAILABLE = _package_available("docstring_parser")
_FAIRSCALE_AVAILABLE = not _IS_WINDOWS and _module_available("fairscale.nn")
_FAIRSCALE_OSS_FP16_BROADCAST_AVAILABLE = _FAIRSCALE_AVAILABLE and _compare_version("fairscale", operator.ge, "0.3.3")
_FAIRSCALE_FULLY_SHARDED_AVAILABLE = _FAIRSCALE_AVAILABLE and _compare_version("fairscale", operator.ge, "0.3.4")
_GROUP_AVAILABLE = not _IS_WINDOWS and _module_available("torch.distributed.group")
_HOROVOD_AVAILABLE = _module_available("horovod.torch")
_HYDRA_AVAILABLE = _package_available("hydra")
_HYDRA_EXPERIMENTAL_AVAILABLE = _module_available("hydra.experimental")
_JSONARGPARSE_AVAILABLE = _package_available("jsonargparse") and _compare_version("jsonargparse", operator.ge, "4.7.1")
_KINETO_AVAILABLE = _TORCH_GREATER_EQUAL_1_8_1 and torch.profiler.kineto_available()
_NEPTUNE_AVAILABLE = _package_available("neptune")
_NEPTUNE_GREATER_EQUAL_0_9 = _NEPTUNE_AVAILABLE and _compare_version("neptune", operator.ge, "0.9.0")
_OMEGACONF_AVAILABLE = _package_available("omegaconf")
_POPTORCH_AVAILABLE = _package_available("poptorch")
_HABANA_FRAMEWORK_AVAILABLE = _package_available("habana_frameworks")
_RICH_AVAILABLE = _package_available("rich") and _compare_version("rich", operator.ge, "10.2.2")
_TORCH_QUANTIZE_AVAILABLE = bool([eg for eg in torch.backends.quantized.supported_engines if eg != "none"])
_TORCHTEXT_AVAILABLE = _package_available("torchtext")
_TORCHTEXT_LEGACY: bool = _TORCHTEXT_AVAILABLE and _compare_version("torchtext", operator.lt, "0.11.0")
_TORCHVISION_AVAILABLE = _package_available("torchvision")
_WANDB_AVAILABLE = _package_available("wandb")
_WANDB_GREATER_EQUAL_0_10_22 = _WANDB_AVAILABLE and _compare_version("wandb", operator.ge, "0.10.22")
_WANDB_GREATER_EQUAL_0_12_10 = _WANDB_AVAILABLE and _compare_version("wandb", operator.ge, "0.12.10")
_XLA_AVAILABLE: bool = _package_available("torch_xla")


from pytorch_lightning.utilities.xla_device import XLADeviceUtils  # noqa: E402

_TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()

if _POPTORCH_AVAILABLE:
    import poptorch

    _IPU_AVAILABLE = poptorch.ipuHardwareIsAvailable()
else:
    _IPU_AVAILABLE = False

if _HABANA_FRAMEWORK_AVAILABLE:
    from habana_frameworks.torch.utils.library_loader import is_habana_avaialble

    _HPU_AVAILABLE = is_habana_avaialble()
else:
    _HPU_AVAILABLE = False


# experimental feature within PyTorch Lightning.
def _fault_tolerant_training() -> bool:
    from pytorch_lightning.utilities.enums import _FaultTolerantMode

    return _FaultTolerantMode.detect_current_mode().is_enabled