"""
# Copyright (c) 2025  PaddlePaddle Authors. 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.
"""

from typing import Optional

import paddle

import fastdeploy


def cutlass_scaled_mm(
    a: paddle.Tensor,
    b: paddle.Tensor,
    scale_a: paddle.Tensor,
    scale_b: paddle.Tensor,
    out_dtype: paddle.dtype,
    bias: Optional[paddle.Tensor] = None,
) -> paddle.Tensor:
    """
    `cutlass_scaled_mm` implements a fused version of
        `output = paddle.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
    where scale_a * a and scale_b * b are implemented using numpy-style
    broadcasting.

    In order to support blockwise scaling like found in DeepSeek V3 we also
    support extended "group" broadcast rules. We extend the numpy-style
    broadcasting rules with the following rule:
        "if the extent of a dimension in the source shape is between 1 and
        corresponding extent in the target shape we repeat each element along
        that dimension  src_shape[dim] // target_shape[dim] times consecutively"
    example if we have:
          a = [[1, 2], and target_shape = (2, 4)
               [3, 4]]
    then we would expand a to:
          a = [[1, 1, 2, 2],
               [3, 3, 4, 4]]
    currently we only support the case:
        scale_a.shape * [1, 128] == a.shape
        scale_b.shape * [128, 128] == b.shape
    """
    assert out_dtype == paddle.bfloat16 or out_dtype == paddle.float16
    assert bias is None or bias.shape[0] == b.shape[0] and bias.dtype == out_dtype
    # Ensure input tensors have valid shapes
    # assert a.numel() > 0, "Input tensor 'a' must not be empty"
    # assert b.numel() > 0, "Input tensor 'b' must not be empty"
    # assert scale_a.numel() > 0, "Scale tensor 'scale_a' must not be empty"
    # assert scale_b.numel() > 0, "Scale tensor 'scale_b' must not be empty"

    m = a.shape[0]
    n = b.shape[0]
    cutlass_compatible_b = b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
    assert cutlass_compatible_b, (
        f"Tensor 'b' shape {b.shape} is not compatible with CUTLASS: " f"both dimensions must be multiples of 16"
    )

    out = paddle.empty([m, n], dtype=out_dtype)
    fastdeploy.model_executor.ops.gpu.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)

    return out


def scaled_fp8_quant(
    input: paddle.Tensor,
    scale: Optional[paddle.Tensor] = None,
    num_token_padding: Optional[int] = None,
    scale_ub: float = 0,
    use_per_token_if_dynamic: bool = False,
) -> tuple[paddle.Tensor, paddle.Tensor]:
    """
    Quantize input tensor to FP8 and return quantized tensor and scale.

    This function supports both static and dynamic quantization: If you
    provide the scale, it will use static scaling and if you omit it,
    the scale will be determined dynamically. The function also allows
    optional padding of the output tensors for downstream kernels that
    will benefit from padding.

    Args:
        input: The input tensor to be quantized to FP8
        scale: Optional scaling factor for the FP8 quantization
        scale_ub: Optional upper bound for scaling factor in dynamic
            per token case
        num_token_padding: If specified, pad the first dimension
            of the output to at least this value.
        use_per_token_if_dynamic: Whether to do per_tensor or per_token
            in the dynamic quantization case.

    Returns:
        tuple[paddle.Tensor, paddle.Tensor]: The output tensor in FP8 and
            scaling factor.
    """
    # This code assumes batch_dim and num_tokens are flattened
    assert input.ndim == 2
    shape = input.shape
    if num_token_padding:
        shape = (max(num_token_padding, input.shape[0]), shape[1])
    output = paddle.empty(shape, dtype=paddle.float8_e4m3fn)

    if scale is None:
        if use_per_token_if_dynamic:
            scale = paddle.empty([shape[0], 1], dtype=paddle.float32)
            from fastdeploy.model_executor.ops.gpu import (
                dynamic_per_token_scaled_fp8_quant,
            )

            dynamic_per_token_scaled_fp8_quant(output, input, scale, scale_ub)
        else:
            scale = paddle.zeros([1], dtype=paddle.float32)
            from fastdeploy.model_executor.ops.gpu import dynamic_scaled_fp8_quant

            dynamic_scaled_fp8_quant(output, input, scale)
    else:
        # num_token_padding not implemented for this case
        # assert (scale.numel() == 1 or num_token_padding is None)
        from fastdeploy.model_executor.ops.gpu import static_scaled_fp8_quant

        static_scaled_fp8_quant(output, input, scale)

    return output, scale
