'''
Copyright 2020 The Microsoft DeepSpeed Team
'''

import sys
import types
import json
from typing import Optional, Union
import torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from packaging import version as pkg_version

from . import ops
from . import module_inject

from .runtime.engine import DeepSpeedEngine, DeepSpeedOptimizerCallable, DeepSpeedSchedulerCallable
from .runtime.engine import ADAM_OPTIMIZER, LAMB_OPTIMIZER
from .runtime.pipe.engine import PipelineEngine
from .inference.engine import InferenceEngine
from .inference.config import DeepSpeedInferenceConfig
from .runtime.lr_schedules import add_tuning_arguments
from .runtime.config import DeepSpeedConfig, DeepSpeedConfigError
from .runtime.activation_checkpointing import checkpointing
from .ops.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
from .module_inject import replace_transformer_layer, revert_transformer_layer

from .utils import log_dist, OnDevice
from .comm.comm import init_distributed

from .runtime import zero
from .runtime import DeepSpeedOptimizer, ZeROOptimizer

from .pipe import PipelineModule

from .git_version_info import version, git_hash, git_branch


def _parse_version(version_str):
    '''Parse a version string and extract the major, minor, and patch versions.'''
    ver = pkg_version.parse(version_str)
    return ver.major, ver.minor, ver.micro


# Export version information
__version__ = version
__version_major__, __version_minor__, __version_patch__ = _parse_version(__version__)
__git_hash__ = git_hash
__git_branch__ = git_branch


def initialize(args=None,
               model: torch.nn.Module = None,
               optimizer: Optional[Union[Optimizer,
                                         DeepSpeedOptimizerCallable]] = None,
               model_parameters: Optional[torch.nn.Module] = None,
               training_data: Optional[torch.utils.data.Dataset] = None,
               lr_scheduler: Optional[Union[_LRScheduler,
                                            DeepSpeedSchedulerCallable]] = None,
               mpu=None,
               dist_init_required: Optional[bool] = None,
               collate_fn=None,
               config=None,
               config_params=None):
    """Initialize the DeepSpeed Engine.

    Arguments:
        args: an object containing local_rank and deepspeed_config fields.
            This is optional if `config` is passed.

        model: Required: nn.module class before apply any wrappers

        optimizer: Optional: a user defined Optimizer or Callable that returns an Optimizer object.
            This overrides any optimizer definition in the DeepSpeed json config.

        model_parameters: Optional: An iterable of torch.Tensors or dicts.
            Specifies what Tensors should be optimized.

        training_data: Optional: Dataset of type torch.utils.data.Dataset

        lr_scheduler: Optional: Learning Rate Scheduler Object or a Callable that takes an Optimizer and returns a Scheduler object.
            The scheduler object should define a get_lr(), step(), state_dict(), and load_state_dict() methods

        mpu: Optional: A model parallelism unit object that implements
            get_{model,data}_parallel_{rank,group,world_size}()

        dist_init_required: Optional: None will auto-initialize torch distributed if needed,
            otherwise the user can force it to be initialized or not via boolean.

        collate_fn: Optional: Merges a list of samples to form a
            mini-batch of Tensor(s).  Used when using batched loading from a
            map-style dataset.

        config: Optional: Instead of requiring args.deepspeed_config you can pass your deepspeed config
            as an argument instead, as a path or a dictionary.

        config_params: Optional: Same as `config`, kept for backwards compatibility.

    Returns:
        A tuple of ``engine``, ``optimizer``, ``training_dataloader``, ``lr_scheduler``

        * ``engine``: DeepSpeed runtime engine which wraps the client model for distributed training.

        * ``optimizer``: Wrapped optimizer if a user defined ``optimizer`` is supplied, or if
          optimizer is specified in json config else ``None``.

        * ``training_dataloader``: DeepSpeed dataloader if ``training_data`` was supplied,
          otherwise ``None``.

        * ``lr_scheduler``: Wrapped lr scheduler if user ``lr_scheduler`` is passed, or
          if ``lr_scheduler`` specified in JSON configuration. Otherwise ``None``.
    """
    log_dist("DeepSpeed info: version={}, git-hash={}, git-branch={}".format(
        __version__,
        __git_hash__,
        __git_branch__),
             ranks=[0])

    # Disable zero.Init context if it's currently enabled
    zero.partition_parameters.shutdown_init_context()

    assert model is not None, "deepspeed.initialize requires a model"

    if not isinstance(model, PipelineModule):
        engine = DeepSpeedEngine(args=args,
                                 model=model,
                                 optimizer=optimizer,
                                 model_parameters=model_parameters,
                                 training_data=training_data,
                                 lr_scheduler=lr_scheduler,
                                 mpu=mpu,
                                 dist_init_required=dist_init_required,
                                 collate_fn=collate_fn,
                                 config=config,
                                 config_params=config_params)
    else:
        assert mpu is None, "mpu must be None with pipeline parallelism"
        engine = PipelineEngine(args=args,
                                model=model,
                                optimizer=optimizer,
                                model_parameters=model_parameters,
                                training_data=training_data,
                                lr_scheduler=lr_scheduler,
                                mpu=model.mpu(),
                                dist_init_required=dist_init_required,
                                collate_fn=collate_fn,
                                config=config,
                                config_params=config_params)

    return_items = [
        engine,
        engine.optimizer,
        engine.training_dataloader,
        engine.lr_scheduler
    ]
    return tuple(return_items)


def _add_core_arguments(parser):
    r"""Helper (internal) function to update an argument parser with an argument group of the core DeepSpeed arguments.
        The core set of DeepSpeed arguments include the following:
        1) --deepspeed: boolean flag to enable DeepSpeed
        2) --deepspeed_config <json file path>: path of a json configuration file to configure DeepSpeed runtime.

        This is a helper function to the public add_config_arguments()

    Arguments:
        parser: argument parser
    Return:
        parser: Updated Parser
    """
    group = parser.add_argument_group('DeepSpeed', 'DeepSpeed configurations')

    group.add_argument(
        '--deepspeed',
        default=False,
        action='store_true',
        help=
        'Enable DeepSpeed (helper flag for user code, no impact on DeepSpeed backend)')

    group.add_argument('--deepspeed_config',
                       default=None,
                       type=str,
                       help='DeepSpeed json configuration file.')

    group.add_argument(
        '--deepscale',
        default=False,
        action='store_true',
        help=
        'Deprecated enable DeepSpeed (helper flag for user code, no impact on DeepSpeed backend)'
    )

    group.add_argument('--deepscale_config',
                       default=None,
                       type=str,
                       help='Deprecated DeepSpeed json configuration file.')

    group.add_argument(
        '--deepspeed_mpi',
        default=False,
        action='store_true',
        help=
        "Run via MPI, this will attempt to discover the necessary variables to initialize torch "
        "distributed from the MPI environment")

    return parser


def add_config_arguments(parser):
    r"""Update the argument parser to enabling parsing of DeepSpeed command line arguments.
        The set of DeepSpeed arguments include the following:
        1) --deepspeed: boolean flag to enable DeepSpeed
        2) --deepspeed_config <json file path>: path of a json configuration file to configure DeepSpeed runtime.

    Arguments:
        parser: argument parser
    Return:
        parser: Updated Parser
    """
    parser = _add_core_arguments(parser)

    return parser


def default_inference_config():
    """
        Return a default DeepSpeed inference configuration dictionary.
    """
    return DeepSpeedInferenceConfig().dict()


def init_inference(model, config=None, **kwargs):
    """Initialize the DeepSpeed InferenceEngine.

    Description: all four cases are valid and supported in DS init_inference() API.

    # Case 1: user provides no config and no kwargs. Default config will be used.

    .. code-block:: python

        generator.model = deepspeed.init_inference(generator.model)
        string = generator("DeepSpeed is")
        print(string)

    # Case 2: user provides a config and no kwargs. User supplied config will be used.

    .. code-block:: python

        generator.model = deepspeed.init_inference(generator.model, config=config)
        string = generator("DeepSpeed is")
        print(string)

    # Case 3: user provides no config and uses keyword arguments (kwargs) only.

    .. code-block:: python

        generator.model = deepspeed.init_inference(generator.model,
                                                    mp_size=world_size,
                                                    dtype=torch.half,
                                                    replace_with_kernel_inject=True)
        string = generator("DeepSpeed is")
        print(string)

    # Case 4: user provides config and keyword arguments (kwargs). Both config and kwargs are merged and kwargs take precedence.

    .. code-block:: python

        generator.model = deepspeed.init_inference(generator.model, config={"dtype": torch.half}, replace_with_kernel_inject=True)
        string = generator("DeepSpeed is")
        print(string)

    Arguments:
        model: Required: original nn.module object without any wrappers

        config: Optional: instead of arguments, you can pass in a DS inference config dict or path to JSON file

    Returns:
        A deepspeed.InferenceEngine wrapped model.
    """
    log_dist("DeepSpeed info: version={}, git-hash={}, git-branch={}".format(
        __version__,
        __git_hash__,
        __git_branch__),
             ranks=[0])

    # Load config_dict from config first
    if config is None:
        config = {}
    if isinstance(config, str):
        with open(config, "r") as f:
            config_dict = json.load(f)
    elif isinstance(config, dict):
        config_dict = config
    else:
        raise ValueError(
            f"'config' argument expected string or dictionary, got {type(config)}")

    # Update with values from kwargs, ensuring no conflicting overlap between config and kwargs
    overlap_keys = set(config_dict.keys()).intersection(kwargs.keys())
    # If there is overlap, error out if values are different
    for key in overlap_keys:
        if config_dict[key] != kwargs[key]:
            raise ValueError(
                f"Conflicting argument '{key}' in 'config':{config_dict[key]} and kwargs:{kwargs[key]}"
            )
    config_dict.update(kwargs)

    ds_inference_config = DeepSpeedInferenceConfig(**config_dict)

    engine = InferenceEngine(model, config=ds_inference_config)

    return engine