# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

"""Utility functions for the HP tool."""
import copy
from typing import List, Optional, Tuple

import omegaconf
import yaml


def _calculate_model_size(
    vocab_size: int = None,
    seq_length: int = None,
    hidden_size: int = None,
    num_layers: int = None,
    ffn_size: int = None,
    kv_channels: int = None,
    att_heads: int = None,
    model_name: str = "gpt3",
):
    """
    Calculates the model size (number of parameters in billions), given the model parameters
    and name.
    :param int vocab_size: vocabulary size to be used during training.
    :param int seq_length: input sequence length to be used during training.
    :param int hidden_size: size of the hidden layers of the model.
    :param int num_layers: number of layers in the model.
    :param int ffn_size: FFN size of the model.
    :param int kv_channels: number of KV channels in the transformer layers.
    :param int att_heads: number of attention heads in the transformer layers.
    :param str model_name: name of the model, i.e gpt3, t5, mt5...
    :return: size of the model in billions of parameters.
    :rtype: float
    :raises NotImplementedError: if the model name is not valid.
    """
    if model_name == "gpt3":
        model_size = (
            12
            * num_layers
            * hidden_size ** 2
            * (1 + (13 / (12 * hidden_size)) + ((vocab_size + seq_length) / (12 * num_layers * hidden_size)))
            / 1e9
        )
    elif model_name in ["t5", "mt5"]:
        # 2 L F + 3 L P + H (2 + 4 L F + L (21 + 12 P) + 1 S + 1 V)
        proj_size = att_heads * kv_channels
        model_size = (
            2 * num_layers * 1.5 * ffn_size
            + 3 * num_layers * proj_size
            + hidden_size
            * (2 + 4 * num_layers * 1.5 * ffn_size + num_layers * (21 + 12 * proj_size) + seq_length + vocab_size)
        ) / 1e9
    elif model_name == "bert":
        model_size = (
            num_layers * (ffn_size + hidden_size * (4 * hidden_size + 3 * att_heads + 2 * ffn_size + 6))
            + hidden_size * (vocab_size + seq_length + hidden_size + 5)
        ) / 1e9

    else:
        raise NotImplementedError("Model name is not valid.")

    return model_size


def calculate_model_size_params(
    model_size_in_b: float, vocab_size: int = 51200, seq_length: int = 2048, model_name: str = "gpt3"
) -> Tuple[int, int, float]:
    """
    Calculates the parameters that affect model_size: hidden size, attention heads,
    KV channels, and FFN size. It also calculates the learning rate.
    :param float model_size_in_b: float, number of parameters in the desired model config, in billions.
    :param int seq_length: int, sequence length to be used during training.
    :param int vocab_size: int, size of the vocabulary to use for training.
    :param str model_name: str, name of the model to be trained, i.e. gpt3, t5, mt5...
    :returns: tuple (layers, hs, att_h, ffn, kv, lr)
        WHERE
        int layers is the number of layers in the model.
        int hs is the hidden size of the model.
        int att_h is the number of attention heads in the model.
        int ffn is the FFN hidden size of the model.
        int kv is the number of KV channels in the model.
        float lr is the learning rate used to train the model.
    :raises ValueError: if the model size is larger than the max supported model size.
    :raises NotImplementedError: if the model name is not supported.
    """
    ffn, kv = None, None  # Only needed for some models.
    if model_name == "gpt3":
        if model_size_in_b < 0.25:
            hs, att_h, lr = 768, 12, 6e-4
        elif model_size_in_b < 0.5:
            hs, att_h, lr = 1024, 16, 3e-4
        elif model_size_in_b < 1:
            hs, att_h, lr = 1536, 16, 2.5e-4
        elif model_size_in_b < 2:
            hs, att_h, lr = 2048, 16, 2e-4
        elif model_size_in_b < 3:
            hs, att_h, lr = 2560, 32, 1.6e-4
        elif model_size_in_b < 4.5:
            hs, att_h, lr = 3072, 32, 1.4e-4
        elif model_size_in_b < 8:
            hs, att_h, lr = 4096, 32, 1.2e-4
        elif model_size_in_b < 15:
            hs, att_h, lr = 5120, 40, 1e-4
        elif model_size_in_b < 25:
            hs, att_h, lr = 6144, 48, 1e-4
        elif model_size_in_b < 52:
            hs, att_h, lr = 8192, 64, 0.8e-4
        elif model_size_in_b < 105:
            hs, att_h, lr = 10240, 80, 0.7e-4
        elif model_size_in_b < 205:
            hs, att_h, lr = 12288, 96, 0.6e-4
        elif model_size_in_b < 405:
            hs, att_h, lr = 20480, 128, 0.5e-4
        elif model_size_in_b < 805:
            hs, att_h, lr = 20480, 128, 0.4e-4
        elif model_size_in_b < 1105:
            hs, att_h, lr = 25600, 160, 0.3e-4
        else:
            raise ValueError("Model_size for GPT-3 must be smaller than 1.1T parameters.")
    elif model_name == "t5":
        kv, lr = 64, 1e-4
        if model_size_in_b < 0.1:
            hs, att_h, ffn = 512, 6, 1024
        elif model_size_in_b < 0.4:
            hs, att_h, ffn = 768, 12, 2048
        elif model_size_in_b < 1:
            hs, att_h, ffn = 1024, 16, 2816
        elif model_size_in_b < 5:
            hs, att_h, ffn = 2048, 32, 5120
        elif model_size_in_b < 15:
            hs, att_h, ffn = 4096, 64, 10240
        elif model_size_in_b < 25.9:
            hs, att_h, ffn = 5120, 80, 10880
        elif model_size_in_b < 43.0:
            hs, att_h, ffn = 6144, 96, 10880
        elif model_size_in_b <= 85.5:
            hs, att_h, ffn = 6144, 96, 16384
        elif model_size_in_b <= 165.5:
            hs, att_h, ffn, kv = 7680, 96, 20480, 128
        elif model_size_in_b <= 250:
            hs, att_h, ffn, kv = 12288, 96, 32768, 128
        else:
            raise ValueError("Model_size for T5 must be smaller than 250B parameters.")
    elif model_name == "mt5":
        kv, lr = 64, 1e-4
        if model_size_in_b < 0.25:
            hs, att_h, ffn = 512, 6, 1024
        elif model_size_in_b < 0.5:
            hs, att_h, ffn = 768, 12, 2048
        elif model_size_in_b < 1.2:
            hs, att_h, ffn = 1024, 16, 2816
        elif model_size_in_b < 5:
            hs, att_h, ffn = 2048, 32, 5120
        elif model_size_in_b < 15:
            hs, att_h, ffn = 4096, 64, 10240
        elif model_size_in_b < 25.9:
            hs, att_h, ffn = 5120, 80, 10880
        elif model_size_in_b < 43.0:
            hs, att_h, ffn = 6144, 96, 10880
        elif model_size_in_b <= 85.5:
            hs, att_h, ffn = 6144, 96, 16384
        elif model_size_in_b <= 165.5:
            hs, att_h, ffn, kv = 7680, 96, 20480, 128
        elif model_size_in_b <= 250:
            hs, att_h, ffn, kv = 12288, 96, 32768, 128
        else:
            raise ValueError("Model_size for mT5 must be smaller than 250B parameters.")
    elif model_name == "bert":
        lr = 1e-4
        if model_size_in_b < 0.25:
            hs, att_h, lr = 768, 12, 2e-4
        elif model_size_in_b < 0.5:
            hs, att_h, lr = 1024, 16, 2e-4
        elif model_size_in_b < 1:
            hs, att_h = 1536, 16
        elif model_size_in_b < 2:
            hs, att_h = 2048, 16
        elif model_size_in_b < 3:
            hs, att_h = 2560, 32
        elif model_size_in_b < 4.5:
            hs, att_h = 2560, 32
        elif model_size_in_b < 8:
            hs, att_h = 4096, 32
        elif model_size_in_b < 15:
            hs, att_h = 5120, 40
        elif model_size_in_b <= 25:
            hs, att_h = 6144, 48
        elif model_size_in_b <= 46.5:
            hs, att_h = 7680, 48
        elif model_size_in_b <= 87.5:
            hs, att_h = 9216, 96
        elif model_size_in_b <= 165.5:
            hs, att_h = 9216, 96
        elif model_size_in_b <= 250.5:
            hs, att_h = 12288, 96
        else:
            raise ValueError("Model_size for BERT must be smaller than 25B parameters.")
        ffn = 4 * hs
    else:
        raise NotImplementedError("Model name is not valid.")

    # Try powers of 2
    margin = 0.01
    for attempt in range(0, 10):
        for layers in (2 ** p for p in range(1, 10)):
            out_size = _calculate_model_size(
                vocab_size=vocab_size,
                seq_length=seq_length,
                hidden_size=hs,
                num_layers=layers,
                ffn_size=ffn,
                kv_channels=kv,
                att_heads=att_h,
                model_name=model_name,
            )
            if model_size_in_b * (1.0 - margin) < out_size < model_size_in_b * (1.0 + margin):
                return layers, hs, att_h, ffn, kv, lr
        margin += 0.01  # Double margin of acceptable model sizes.

    # Try multiples of 16
    margin = 0.01
    for attempt in range(0, 6):
        for layers in range(16, 201, 16):
            out_size = _calculate_model_size(
                vocab_size=vocab_size,
                seq_length=seq_length,
                hidden_size=hs,
                num_layers=layers,
                ffn_size=ffn,
                kv_channels=kv,
                att_heads=att_h,
                model_name=model_name,
            )
            if model_size_in_b * (1.0 - margin) < out_size < model_size_in_b * (1.0 + margin):
                return layers, hs, att_h, ffn, kv, lr
        margin += 0.01  # Double margin of acceptable model sizes.

    # Try multiples of 2
    margin = 0.01
    for attempt in range(0, 6):
        for layers in range(2, 201, 2):
            out_size = _calculate_model_size(
                vocab_size=vocab_size,
                seq_length=seq_length,
                hidden_size=hs,
                num_layers=layers,
                ffn_size=ffn,
                kv_channels=kv,
                att_heads=att_h,
                model_name=model_name,
            )
            if model_size_in_b * (1.0 - margin) < out_size < model_size_in_b * (1.0 + margin):
                return layers, hs, att_h, ffn, kv, lr
        margin += 0.01  # Double margin of acceptable model sizes.

    # Try multiples of 5
    margin = 0.01
    for attempt in range(0, 6):
        for layers in range(5, 201, 5):
            out_size = _calculate_model_size(
                vocab_size=vocab_size,
                seq_length=seq_length,
                hidden_size=hs,
                num_layers=layers,
                ffn_size=ffn,
                kv_channels=kv,
                att_heads=att_h,
                model_name=model_name,
            )
            if model_size_in_b * (1.0 - margin) < out_size < model_size_in_b * (1.0 + margin):
                return layers, hs, att_h, ffn, kv, lr
        margin += 0.01  # Double margin of acceptable model sizes.

    # Try any valid number
    margin = 0.01
    for attempt in range(0, 10):
        for layers in range(1, 200):
            out_size = _calculate_model_size(
                vocab_size=vocab_size,
                seq_length=seq_length,
                hidden_size=hs,
                num_layers=layers,
                ffn_size=ffn,
                kv_channels=kv,
                att_heads=att_h,
                model_name=model_name,
            )
            if model_size_in_b * (1.0 - margin) < out_size < model_size_in_b * (1.0 + margin):
                return layers, hs, att_h, ffn, kv, lr
        margin += 0.01  # Double margin of acceptable model sizes.
    raise Exception("Number of layers not found, config is not possible.")


def generic_base_config(cfg: omegaconf.dictconfig.DictConfig, custom_cfg, model_name: str = "gpt3") -> dict:
    """
    Generates a base config dictionary from a base config yaml file.
    :param omegaconf.dictconfig.DictConfig cfg: hydra-like config object for the HP tool.
    :param str model_name: name of the model, i.e. gpt3, t5, mt5...
    :returns: dictionary containing the base configuration for the model.
    :rtype: dict
    """
    cfg_path = f"{cfg.auto_configurator_path}/base_configs/{model_name}.yaml" if custom_cfg is None else custom_cfg
    with open(cfg_path) as f:
        base_cfg = yaml.safe_load(f)
    return base_cfg


def modify_cfg(
    base_cfg: dict,
    act: int,
    num_mbs_act: int,
    act_per_pipe: int,
    tp: int,
    pp: int,
    virtual_pipelines: int,
    mbs: int,
    max_minutes: int,
    max_steps: int,
    num_nodes: int,
    model_name: str,
) -> dict:
    """
    Modify the base configuration for the model with the new parameters that are specific to the current model, which the HP tool heuristics selected.
    :param dict base_cfg: base configuration for the current model, which will be modified in this function.
    :param int act: number of activation checkpointing layers to use for the model.
    :param int num_mbs_act:
    :param int act_per_pipe:
    :param int tp: Tensor Parallelism (TP) value to be set for the model.
    :param int pp: Pipeline Parallelism (PP) value to be set for the model.
    :param int virtual_pipelines: Virtual Pipelines value to be set for the model.
    :param int mbs: Micro Batch Size (MBS) value to be set for the model.
    :param int max_minutes: maximum amount of time to run this model for.
    :param int max_steps: maximum number of steps to run this model for.
    :param int num_nodes: number of nodes to use for the training run.
    :param str model_name: name of the model, i.e. gpt3, t5, mt5...
    :return: dictionary containing the updated model configuration parameters.
    :rtype: dict
    """
    new_cfg = copy.deepcopy(base_cfg)
    if act is not None:
        if model_name in ["gpt3", "bert"]:
            new_cfg["model"]["activations_checkpoint_num_layers"] = act
        else:
            new_cfg["model"]["encoder"]["activations_checkpoint_num_layers"] = act // 2
            new_cfg["model"]["decoder"]["activations_checkpoint_num_layers"] = act // 2

    if num_mbs_act is not None and model_name in ["gpt3", "bert"]:
        new_cfg["model"]["num_micro_batches_with_partial_activation_checkpoints"] = num_mbs_act

    if act_per_pipe is not None and model_name in ["gpt3", "bert"]:
        new_cfg["model"]["activations_checkpoint_layers_per_pipeline"] = act_per_pipe

    if virtual_pipelines is not None and model_name in ["gpt3", "bert"]:
        new_cfg["model"]["virtual_pipeline_model_parallel_size"] = virtual_pipelines

    new_cfg["model"]["tensor_model_parallel_size"] = tp
    new_cfg["model"]["pipeline_model_parallel_size"] = pp
    new_cfg["model"]["micro_batch_size"] = mbs

    if model_name in ["gpt3", "bert"]:
        att_heads = new_cfg["model"]["num_attention_heads"]
        num_layers = new_cfg["model"]["num_layers"]
    else:
        att_heads = new_cfg["model"]["encoder"]["num_attention_heads"]
        num_layers = new_cfg["model"]["encoder"]["num_layers"]

    # gbs = mbs * num_gpus * accumulate_grad_batches / (tp * pp)
    num_gpus = new_cfg["trainer"]["num_nodes"] * new_cfg["trainer"]["devices"]
    gbs = new_cfg["model"]["global_batch_size"]

    mod_gbs = gbs % (mbs * num_gpus / (tp * pp))
    mod_att_heads = att_heads % tp
    mod_layers = num_layers % pp
    if mod_gbs == 0 and mod_att_heads == 0 and mod_layers == 0:
        # Valid config
        new_cfg["trainer"]["num_nodes"] = num_nodes  # Necessary for short single-node test.
        new_cfg["trainer"]["max_steps"] = max_steps
        new_cfg["trainer"]["val_check_interval"] = max_steps
        days = max_minutes // 3600
        hours = (max_minutes % 3600) // 60
        mins = (max_minutes % 3600) % 60
        new_cfg["run"]["time_limit"] = f"{days}-{hours}:{mins}:00"
        new_cfg["run"][
            "name"
        ] = f"{new_cfg['run']['name']}_{num_nodes}nodes_tp_{tp}_pp_{pp}_mbs_{mbs}_act_ckpt_{act}_num_mbs_act_{num_mbs_act}_act_per_pipe_{act_per_pipe}"
        print(
            f"Valid config: GBS={gbs}, MBS={mbs}, TP={tp}, PP={pp}, act_ckpt_layers={act}, num_mbs_act={num_mbs_act}, act_per_pipe={act_per_pipe}. Adding to directory."
        )
        return new_cfg
    return None


def create_slurm_file(
    new_script_path: str,
    cmds: List[str],
    job_name: str,
    flags: str = "",
    dependency: Optional[str] = None,
    time: str = "04:00:00",
    exclusive: bool = True,
    mem: Optional[int] = None,
    overcommit: bool = True,
    nodes: Optional[int] = None,
    ntasks: Optional[int] = None,
    ntasks_per_node: Optional[int] = None,
    gpus_per_task: Optional[int] = None,
    gpus_per_node: Optional[int] = None,
    partition: str = "batch",
    account: Optional[str] = None,
    exclude: Optional[str] = None,
    output: Optional[str] = None,
    comment: Optional[str] = None,
):
    """
    Creates a slurm script file to launch a job on a slurm based cluster. Saves the script
    to the local file system in the path specified on new_script_path.
    :param str new_stript_path: path where the SLURM script will be stored in the file system.
    :param List[str] cmds: list of commands to run, each one inside an srun line.
    :param str job_name: name of the slurm job.
    :param str flags: flags to be added to each of the srun commands.
    :param Optional[str] dependency: job_id(s) to the jobs that need to run before the current job.
    :param str time: slurm style time-limit for the job.
    :param bool exclusive: slurm exclusive parameter.
    :param Optional[int] mem: slurm mem parameter.
    :param bool overcommit: slurm overcommit parameter.
    :param Optional[int] nodes: number of nodes to use to train the model.
    :param Optional[int] ntasks: slurm ntasks parameter.
    :param Optional[int] ntasks_per_node: slurm ntasks_per_node parameter.
    :param Optional[int] gpus_per_task: slurm gpus_per_task parameter.
    :param Optional[int] gpus_per_node: slurm gpus_per_node parameter.
    :param str partition: slurm partition parameter.
    :param Optional[str] account: slurm account parameter.
    :param Optional[str] exclude: slurm exclude parameter.
    :return: None
    """
    with open(new_script_path, "w") as f:
        f.writelines("#!/usr/bin/env bash\n")
        if nodes is not None:
            f.writelines(f"#SBATCH --nodes={nodes}\n")
        if ntasks is not None:
            f.writelines(f"#SBATCH --ntasks={ntasks}\n")
        if ntasks_per_node is not None:
            f.writelines(f"#SBATCH --ntasks-per-node={ntasks_per_node}\n")
        if gpus_per_task is not None:
            f.writelines(f"#SBATCH --gpus-per-task={gpus_per_task}\n")
        if gpus_per_node is not None:
            f.writelines(f"#SBATCH --gpus-per-node={gpus_per_node}\n")
        if dependency is not None:
            dependency = dependency.strip()
            if dependency != "singleton":
                dependency = f"afterany:{dependency}"
            f.writelines(f"#SBATCH --dependency={dependency}\n")
        f.writelines(f"#SBATCH -p {partition}\n")
        if account is not None:
            f.writelines(f"#SBATCH -A {account}\n")
        f.writelines(f"#SBATCH --job-name={job_name}\n")
        if mem is not None:
            f.writelines(f"#SBATCH --mem={mem}\n")
        if exclusive:
            f.writelines("#SBATCH --exclusive\n")
        if overcommit:
            f.writelines("#SBATCH --overcommit\n")
        if exclude:
            f.writelines(f"#SBATCH --exclude={','.join(exclude)}\n")
        if output:
            f.writelines(f"#SBATCH --output={output}\n")
        if comment:
            f.writelines(f"#SBATCH --comment={comment}\n")
        f.writelines(f"#SBATCH --time={time}\n\n")
        for cmd in cmds:
            # assert "'" not in cmd
            f.writelines(f"srun {flags} sh -c '{cmd}'\n\n")
        f.writelines("set +x\n")


def convert_to_cli(cfg: omegaconf.dictconfig.DictConfig, root: bool = True) -> str:
    """
    Converts hydra-like OmegaConf config dictionary object to a sring that can be used to override
    hydra parameters using the CLI.
    :param omegaconf.dictconfig.DictConfig cfg: the config object to be converted to str format.
    :return: the string containing the overrides for hydra.
    :rtype: str
    """
    result = []
    if cfg.get("search_config_value") is not None:
        result.append(f"search_config={cfg['search_config_value']}")

    for k, v in cfg.items():
        if k in [
            "training_container",
            "inference_container",
            "training_container_image",
            "inference_container_image",
            "ci_test",
        ]:
            continue
        if isinstance(v, omegaconf.dictconfig.DictConfig):
            output = convert_to_cli(v, False)
            result.extend([f"{k}.{x}" for x in output if x != ""])
        elif isinstance(v, omegaconf.listconfig.ListConfig):
            result.append(f"{k}={str(v).replace(' ', '')}")
        elif isinstance(v, str) and "{" in v:
            continue
        else:
            result.append(f"{k}={convert_to_null(v)}")
    return " \\\n  ".join(result) if root else result


def convert_to_null(val: Optional[str]) -> str:
    """
    Converts a value to the str null if None is provided, to be able to pass it to hydra.
    :param Optional[str] val: value to be replaced with 'null' if the value is None.
    :return: either the input value itself or 'null'.
    :rtype: str
    """
    if val is None:
        return "null"
    return val


def add_container_mounts(container_mounts: Optional[List[str]]) -> str:
    """
    Converts the config container mounts to the right format for an srun command.
    :param Optional[List[str]] container_mounts: list of container mounts as in the config file.
    :return: the string that can be used in the srun command to add the container mounts.
    :rtype: str
    """
    mounts_str = ""
    if container_mounts[0] is None or container_mounts[0] == "None":
        return ""
    if container_mounts is not None:
        assert isinstance(container_mounts, omegaconf.listconfig.ListConfig), "container_mounts must be a list."
        for mount in container_mounts:
            if mount is not None and isinstance(mount, str):
                mounts_str += f",{mount}" if ":" in mount else f",{mount}:{mount}"
    return mounts_str

    

 
