"""
    This file is part of ComfyUI.
    Copyright (C) 2024 Stability AI

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
"""
import contextlib
import logging
import torch
from torch import Tensor
from typing import Optional, Type, Union

from . import model_management, rmsnorm
from .interruption import throw_exception_if_processing_interrupted
from .cli_args import args, PerformanceFeature
from .execution_context import current_execution_context
from .float import stochastic_rounding

logger = logging.getLogger(__name__)


def run_every_op():
    if torch.compiler.is_compiling():
        return

    throw_exception_if_processing_interrupted()


scaled_dot_product_attention = None


def _scaled_dot_product_attention(q, k, v, *args, **kwargs):
    return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)


try:
    if torch.cuda.is_available():
        from torch.nn.attention import SDPBackend, sdpa_kernel  # pylint: disable=import-error
        import inspect

        if "set_priority" in inspect.signature(sdpa_kernel).parameters:
            SDPA_BACKEND_PRIORITY = [
                SDPBackend.FLASH_ATTENTION,
                SDPBackend.EFFICIENT_ATTENTION,
                SDPBackend.MATH,
            ]

            SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION)


            def _scaled_dot_product_attention_sdpa(q, k, v, *args, **kwargs):
                with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True):
                    return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)


            scaled_dot_product_attention = _scaled_dot_product_attention_sdpa
        else:
            logger.warning("Torch version too old to set sdpa backend priority, even though you are using CUDA")
            scaled_dot_product_attention = _scaled_dot_product_attention
    else:
        scaled_dot_product_attention = _scaled_dot_product_attention
except Exception as exc_info:
    if torch.cuda.is_available():
        logger.debug("Could not set sdpa backend priority.", exc_info=exc_info)
    scaled_dot_product_attention = _scaled_dot_product_attention

NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
try:
    if comfy.model_management.is_nvidia():
        if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
            # TODO: change upper bound version once it's fixed'
            NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
            logging.info("working around nvidia conv3d memory bug.")
except:
    pass

cast_to = model_management.cast_to  # TODO: remove once no more references


def cast_to_input(weight, input, non_blocking=False, copy=True):
    return model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)


@torch.compiler.disable()
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
    if input is not None:
        if dtype is None:
            dtype = input.dtype
        if bias_dtype is None:
            bias_dtype = dtype
        if device is None:
            device = input.device

    offload_stream = model_management.get_offload_stream(device)
    if offload_stream is not None:
        wf_context = offload_stream
    else:
        wf_context = contextlib.nullcontext()

    bias = None
    non_blocking = True if torch.jit.is_tracing() or torch.jit.is_scripting() else model_management.device_supports_non_blocking(device)
    if s.bias is not None:
        has_function = len(s.bias_function) > 0
        bias = model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
        if has_function:
            with wf_context:
                for f in s.bias_function:
                    bias = f(bias)

    has_function = len(s.weight_function) > 0
    weight = model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
    if has_function:
        with wf_context:
            for f in s.weight_function:
                weight = f(weight)

    model_management.sync_stream(device, offload_stream)
    return weight, bias


class SkipInit:
    def reset_parameters(self):
        return None


class CastWeightBiasOp:
    comfy_cast_weights = False
    weight_function = []
    bias_function = []


class skip_init:
    class Linear(SkipInit, torch.nn.Linear):
        pass

    class Conv1d(SkipInit, torch.nn.Conv1d):
        pass

    class Conv2d(SkipInit, torch.nn.Conv2d):
        pass

    class Conv3d(SkipInit, torch.nn.Conv3d):
        pass

    class GroupNorm(SkipInit, torch.nn.GroupNorm):
        pass

    class LayerNorm(SkipInit, torch.nn.LayerNorm):
        pass

    class ConvTranspose2d(SkipInit, torch.nn.ConvTranspose2d):
        pass

    class ConvTranspose1d(SkipInit, torch.nn.ConvTranspose1d):
        pass

    class Embedding(SkipInit, torch.nn.Embedding):
        def forward(self, *args, **kwargs) -> Tensor:
            if "out_dtype" in kwargs:
                kwargs.pop("out_dtype")
            return super().forward(*args, **kwargs)

    @classmethod
    def conv_nd(cls, dims, *args, **kwargs):
        if dims == 2:
            return cls.Conv2d(*args, **kwargs)
        elif dims == 3:
            return cls.Conv3d(*args, **kwargs)
        else:
            raise ValueError(f"unsupported dimensions: {dims}")


class disable_weight_init:
    class Linear(torch.nn.Linear, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input):
            weight, bias = cast_bias_weight(self, input)
            return torch.nn.functional.linear(input, weight, bias)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input):
            weight, bias = cast_bias_weight(self, input)
            return self._conv_forward(input, weight, bias)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input):
            weight, bias = cast_bias_weight(self, input)
            return self._conv_forward(input, weight, bias)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def _conv_forward(self, input, weight, bias, *args, **kwargs):
            if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):
                out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)
                if bias is not None:
                    out += bias.reshape((1, -1) + (1,) * (out.ndim - 2))
                return out
            else:
                return super()._conv_forward(input, weight, bias, *args, **kwargs)

        def forward_comfy_cast_weights(self, input):
            weight, bias = cast_bias_weight(self, input)
            return self._conv_forward(input, weight, bias)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input):
            weight, bias = cast_bias_weight(self, input)
            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input):
            if self.weight is not None:
                weight, bias = cast_bias_weight(self, input)
            else:
                weight = None
                bias = None
            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class RMSNorm(rmsnorm.RMSNorm, CastWeightBiasOp):
        def reset_parameters(self):
            self.bias = None
            return None

        def forward_comfy_cast_weights(self, input):
            if self.weight is not None:
                weight, bias = cast_bias_weight(self, input)
            else:
                weight = None
            return rmsnorm.rms_norm(input, weight, self.eps)  # TODO: switch to commented out line when old torch is deprecated
            # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input, output_size=None):
            num_spatial_dims = 2
            output_padding = self._output_padding(
                input, output_size, self.stride, self.padding, self.kernel_size,
                num_spatial_dims, self.dilation)

            weight, bias = cast_bias_weight(self, input)
            return torch.nn.functional.conv_transpose2d(
                input, weight, bias, self.stride, self.padding,
                output_padding, self.groups, self.dilation)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
        def reset_parameters(self):
            return None

        def forward_comfy_cast_weights(self, input, output_size=None):
            num_spatial_dims = 1
            output_padding = self._output_padding(
                input, output_size, self.stride, self.padding, self.kernel_size,
                num_spatial_dims, self.dilation)

            weight, bias = cast_bias_weight(self, input)
            return torch.nn.functional.conv_transpose1d(
                input, weight, bias, self.stride, self.padding,
                output_padding, self.groups, self.dilation)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                return super().forward(*args, **kwargs)

    class Embedding(torch.nn.Embedding, CastWeightBiasOp):
        def reset_parameters(self):
            self.bias = None
            return None

        def forward_comfy_cast_weights(self, input, out_dtype=None):
            output_dtype = out_dtype
            if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
                out_dtype = None
            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)

        def forward(self, *args, **kwargs):
            run_every_op()
            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                return self.forward_comfy_cast_weights(*args, **kwargs)
            else:
                if "out_dtype" in kwargs:
                    kwargs.pop("out_dtype")
                return super().forward(*args, **kwargs)

    @classmethod
    def conv_nd(s, dims, *args, **kwargs):
        if dims == 2:
            return s.Conv2d(*args, **kwargs)
        elif dims == 3:
            return s.Conv3d(*args, **kwargs)
        else:
            raise ValueError(f"unsupported dimensions: {dims}")


class manual_cast(disable_weight_init):
    class Linear(disable_weight_init.Linear):
        comfy_cast_weights = True

    class Conv1d(disable_weight_init.Conv1d):
        comfy_cast_weights = True

    class Conv2d(disable_weight_init.Conv2d):
        comfy_cast_weights = True

    class Conv3d(disable_weight_init.Conv3d):
        comfy_cast_weights = True

    class GroupNorm(disable_weight_init.GroupNorm):
        comfy_cast_weights = True

    class LayerNorm(disable_weight_init.LayerNorm):
        comfy_cast_weights = True

    class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
        comfy_cast_weights = True

    class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
        comfy_cast_weights = True

    class RMSNorm(disable_weight_init.RMSNorm):
        comfy_cast_weights = True

    class Embedding(disable_weight_init.Embedding):
        comfy_cast_weights = True


def fp8_linear(self, input):
    dtype = self.weight.dtype
    if dtype not in [torch.float8_e4m3fn]:
        return None

    tensor_2d = False
    if len(input.shape) == 2:
        tensor_2d = True
        input = input.unsqueeze(1)

    input_shape = input.shape
    input_dtype = input.dtype
    if len(input.shape) == 3:
        w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
        w = w.t()

        scale_weight = self.scale_weight
        scale_input = self.scale_input
        if scale_weight is None:
            scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
        else:
            scale_weight = scale_weight.to(input.device)

        if scale_input is None:
            scale_input = torch.ones((), device=input.device, dtype=torch.float32)
            input = torch.clamp(input, min=-448, max=448, out=input)
            input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
        else:
            scale_input = scale_input.to(input.device)
            input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous()

        if bias is not None:
            o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
        else:
            o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight)

        if isinstance(o, tuple):
            o = o[0]

        if tensor_2d:
            return o.reshape(input_shape[0], -1)

        return o.reshape((-1, input_shape[1], self.weight.shape[0]))

    return None


class fp8_ops(manual_cast):
    class Linear(manual_cast.Linear):
        def reset_parameters(self):
            self.scale_weight = None
            self.scale_input = None
            return None

        def forward_comfy_cast_weights(self, input):
            if not self.training:
                try:
                    out = fp8_linear(self, input)
                    if out is not None:
                        return out
                except Exception as e:
                    logger.info("Exception during fp8 op: {}".format(e))

            weight, bias = cast_bias_weight(self, input)
            return torch.nn.functional.linear(input, weight, bias)


class scaled_fp8_op_base(manual_cast):
    pass


def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
    logger.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))

    class scaled_fp8_op(scaled_fp8_op_base):
        class Linear(manual_cast.Linear):
            def __init__(self, *args, **kwargs):
                if override_dtype is not None:
                    kwargs['dtype'] = override_dtype
                super().__init__(*args, **kwargs)

            def reset_parameters(self):
                if not hasattr(self, 'scale_weight'):
                    self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)

                if not scale_input:
                    self.scale_input = None

                if not hasattr(self, 'scale_input'):
                    self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
                return None

            def forward_comfy_cast_weights(self, input):
                if fp8_matrix_mult:
                    out = fp8_linear(self, input)
                    if out is not None:
                        return out

                weight, bias = cast_bias_weight(self, input)

                if weight.numel() < input.numel():  # TODO: optimize
                    return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
                else:
                    return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)

            def convert_weight(self, weight, inplace=False, **kwargs):
                if inplace:
                    weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
                    return weight
                else:
                    return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)

            def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
                weight = stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
                if return_weight:
                    return weight
                if inplace_update:
                    self.weight.data.copy_(weight)
                else:
                    self.weight = torch.nn.Parameter(weight, requires_grad=False)

    return scaled_fp8_op


CUBLAS_IS_AVAILABLE = False
try:
    from cublas_ops import CublasLinear

    CUBLAS_IS_AVAILABLE = True
except ImportError:
    pass

if CUBLAS_IS_AVAILABLE:
    class cublas_ops(disable_weight_init):
        class Linear(CublasLinear, disable_weight_init.Linear):
            def reset_parameters(self):
                return None

            def forward_comfy_cast_weights(self, input):
                return super().forward(input)

            def forward(self, *args, **kwargs):
                return super().forward(*args, **kwargs)
else:
    class cublas_ops(disable_weight_init):
        pass

Operations = Type[Union[manual_cast, fp8_ops, disable_weight_init, skip_init, scaled_fp8_op_base]]


def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8: Optional[torch.dtype] = None, inference_mode: Optional[bool] = None) -> Operations:
    if inference_mode is None:
        # todo: check a context here, since this isn't being used by any callers yet
        inference_mode = current_execution_context().inference_mode
    fp8_compute = model_management.supports_fp8_compute(load_device)
    if scaled_fp8 is not None:
        return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)

    if (
            fp8_compute and
            (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and
            not disable_fast_fp8
    ):
        return fp8_ops

    if (
            PerformanceFeature.CublasOps in args.fast and
            CUBLAS_IS_AVAILABLE and
            weight_dtype == torch.float16 and
            (compute_dtype == torch.float16 or compute_dtype is None)
    ):
        logger.info("Using cublas ops")
        return cublas_ops

    if compute_dtype is None or weight_dtype == compute_dtype:
        # disable_weight_init seems to interact poorly with some other optimization code
        return disable_weight_init if inference_mode else skip_init

    return manual_cast