from typing import Callable, List, Optional

import torch
from compressed_tensors.quantization import QuantizationStrategy
from torch.nn import Parameter

from aphrodite.common.utils import is_hip
from aphrodite.modeling.parameter import (ChannelQuantScaleParameter,
                                          ModelWeightParameter,
                                          PerTensorScaleParameter)
from aphrodite.quantization.compressed_tensors.schemes import (
    CompressedTensorsScheme)
from aphrodite.quantization.utils.w8a8_utils import (
    apply_fp8_linear, cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz,
    requantize_with_max_scale)

__all__ = ["CompressedTensorsW8A8Fp8"]


class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):

    def __init__(self, strategy: str, is_static_input_scheme: bool):
        self.strategy = strategy
        self.is_static_input_scheme = is_static_input_scheme
        self.cutlass_fp8_supported = cutlass_fp8_supported()

    @classmethod
    def get_min_capability(cls) -> int:
        # lovelace and up
        return 89

    def process_weights_after_loading(self, layer) -> None:
        # If per tensor, when we have a fused module (e.g. QKV) with per
        # tensor scales (thus N scales being passed to the kernel),
        # requantize so we can always run per tensor
        if self.strategy == QuantizationStrategy.TENSOR:
            max_w_scale, weight = requantize_with_max_scale(
                weight=layer.weight,
                weight_scale=layer.weight_scale,
                logical_widths=layer.logical_widths,
            )

            if is_hip():
                weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
                    weight=weight,
                    weight_scale=max_w_scale,
                    input_scale=layer.input_scale)
                if input_scale is not None:
                    layer.input_scale = Parameter(input_scale,
                                                  requires_grad=False)

            layer.weight = Parameter(weight.t(), requires_grad=False)
            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)

        # If channelwise, scales are already lined up, so just transpose.
        elif self.strategy == QuantizationStrategy.CHANNEL:
            weight = layer.weight
            if is_hip():
                weight, weight_scale, input_scale = \
                    normalize_e4m3fn_to_e4m3fnuz(
                        weight=weight,
                        weight_scale=layer.weight_scale,
                        input_scale=layer.input_scale)
                if input_scale is not None:
                    layer.input_scale = Parameter(input_scale,
                                                  requires_grad=False)
            else:
                weight_scale = layer.weight_scale.data
            layer.weight = Parameter(weight.t(), requires_grad=False)
            # required by torch.compile to be torch.nn.Parameter
            layer.weight_scale = Parameter(weight_scale, requires_grad=False)

        else:
            raise ValueError(f"Unknown quantization strategy {self.strategy}")

        # INPUT SCALE
        if self.is_static_input_scheme:
            layer.input_scale = Parameter(layer.input_scale.max(),
                                          requires_grad=False)
        else:
            layer.input_scale = None

    def create_weights(self, layer: torch.nn.Module,
                       output_partition_sizes: List[int],
                       input_size_per_partition: int,
                       params_dtype: torch.dtype, weight_loader: Callable,
                       **kwargs):
        output_size_per_partition = sum(output_partition_sizes)
        layer.logical_widths = output_partition_sizes

        # WEIGHT
        weight = ModelWeightParameter(data=torch.empty(
            output_size_per_partition,
            input_size_per_partition,
            dtype=torch.float8_e4m3fn),
                                      input_dim=1,
                                      output_dim=0,
                                      weight_loader=weight_loader)
        layer.register_parameter("weight", weight)

        # WEIGHT SCALE
        # TODO: update create_xxx_parameter functions to return
        # the newly added parameters
        if self.strategy == QuantizationStrategy.CHANNEL:
            weight_scale = ChannelQuantScaleParameter(
                data=torch.empty((sum(output_partition_sizes), 1),
                                 dtype=torch.float32),
                output_dim=0,
                weight_loader=weight_loader)
        else:
            assert self.strategy == QuantizationStrategy.TENSOR
            weight_scale = PerTensorScaleParameter(data=torch.empty(
                len(output_partition_sizes), dtype=torch.float32),
                                                   weight_loader=weight_loader)

        # min requirement for fp8 kernels
        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

        # INPUT SCALE
        if self.is_static_input_scheme:
            input_scale = PerTensorScaleParameter(data=torch.empty(
                len(output_partition_sizes), dtype=torch.float32),
                                                  weight_loader=weight_loader)
            input_scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("input_scale", input_scale)

    def apply_weights(self,
                      layer: torch.nn.Module,
                      x: torch.Tensor,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:

        return apply_fp8_linear(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=layer.input_scale,
            bias=bias,
            cutlass_fp8_supported=self.cutlass_fp8_supported,
            use_per_token_if_dynamic=True)
