# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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.
"""
Utilities to create common models
"""

import torch
from torch import nn


def get_model_size(model: nn.Module, scale="auto"):
    n_params = sum(p.numel() for p in model.parameters())

    if scale == "auto":
        if n_params > 1e9:
            scale = "B"
        elif n_params > 1e6:
            scale = "M"
        elif n_params > 1e3:
            scale = "K"
        else:
            scale = ""

    if scale == "B":
        n_params = n_params / 1e9
    elif scale == "M":
        n_params = n_params / 1e6
    elif scale == "K":
        n_params = n_params / 1e3
    elif scale == "":
        pass
    else:
        raise NotImplementedError(f"Unknown scale {scale}")

    return n_params, scale


def print_model_size(model: nn.Module, name: str = None):
    n_params, scale = get_model_size(model, scale="auto")
    if name is None:
        name = model.__class__.__name__

    print(f"{name} contains {n_params:.2f}{scale} parameters")


def compute_position_id_with_mask(mask):
    return torch.clip(torch.cumsum(mask, dim=-1) - 1, min=0, max=None)