# Copyright (c) 2024, EleutherAI contributors
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# 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.

import torch
import torch.nn.functional as F
from typing import Optional
from torch import Tensor

# flags required to enable jit fusion kernels
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)


def bias_dropout_add(
    x: Tensor, bias: Tensor, residual: Optional[Tensor], prob: float, training: bool
) -> Tensor:
    out = torch.nn.functional.dropout(x + bias, p=prob, training=training)
    if residual is not None:
        out = residual + out
    return out


def get_bias_dropout_add(training):
    def _bias_dropout_add(x, bias, residual, prob):
        return bias_dropout_add(x, bias, residual, prob, training)

    return _bias_dropout_add


@torch.jit.script
def bias_dropout_add_fused_train(
    x: Tensor, bias: Tensor, residual: Optional[Tensor], prob: float
) -> Tensor:
    return bias_dropout_add(x, bias, residual, prob, True)


@torch.jit.script
def bias_dropout_add_fused_inference(
    x: Tensor, bias: Tensor, residual: Optional[Tensor], prob: float
) -> Tensor:
    return bias_dropout_add(x, bias, residual, prob, False)