import argparse
from pathlib import Path

from ...ltx_core.loader import LTXV_LORA_COMFY_RENAMING_MAP, LoraPathStrengthAndSDOps
from .constants import (
    DEFAULT_1_STAGE_HEIGHT,
    DEFAULT_1_STAGE_WIDTH,
    DEFAULT_2_STAGE_HEIGHT,
    DEFAULT_2_STAGE_WIDTH,
    DEFAULT_CFG_GUIDANCE_SCALE,
    DEFAULT_FRAME_RATE,
    DEFAULT_LORA_STRENGTH,
    DEFAULT_NEGATIVE_PROMPT,
    DEFAULT_NUM_FRAMES,
    DEFAULT_NUM_INFERENCE_STEPS,
    DEFAULT_SEED,
)


class VideoConditioningAction(argparse.Action):
    def __call__(
        self,
        parser: argparse.ArgumentParser,  # noqa: ARG002
        namespace: argparse.Namespace,
        values: list[str],
        option_string: str | None = None,  # noqa: ARG002
    ) -> None:
        path, strength_str = values
        resolved_path = resolve_path(path)
        strength = float(strength_str)
        current = getattr(namespace, self.dest) or []
        current.append((resolved_path, strength))
        setattr(namespace, self.dest, current)


class ImageAction(argparse.Action):
    def __call__(
        self,
        parser: argparse.ArgumentParser,  # noqa: ARG002
        namespace: argparse.Namespace,
        values: list[str],
        option_string: str | None = None,  # noqa: ARG002
    ) -> None:
        path, frame_idx, strength_str = values
        resolved_path = resolve_path(path)
        frame_idx = int(frame_idx)
        strength = float(strength_str)
        current = getattr(namespace, self.dest) or []
        current.append((resolved_path, frame_idx, strength))
        setattr(namespace, self.dest, current)


class LoraAction(argparse.Action):
    def __call__(
        self,
        parser: argparse.ArgumentParser,  # noqa: ARG002
        namespace: argparse.Namespace,
        values: list[str],
        option_string: str | None = None,
    ) -> None:
        if len(values) > 2:
            msg = f"{option_string} accepts at most 2 arguments (PATH and optional STRENGTH), got {len(values)} values"
            raise argparse.ArgumentError(self, msg)

        path = values[0]
        strength_str = values[1] if len(values) > 1 else str(DEFAULT_LORA_STRENGTH)

        resolved_path = resolve_path(path)
        strength = float(strength_str)

        current = getattr(namespace, self.dest) or []
        current.append(LoraPathStrengthAndSDOps(resolved_path, strength, LTXV_LORA_COMFY_RENAMING_MAP))
        setattr(namespace, self.dest, current)


def resolve_path(path: str) -> str:
    return str(Path(path).expanduser().resolve().as_posix())


def basic_arg_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--checkpoint-path",
        type=resolve_path,
        required=True,
        help="Path to LTX-2 model checkpoint (.safetensors file).",
    )
    parser.add_argument(
        "--gemma-root",
        type=resolve_path,
        required=True,
        help="Path to the root directory containing the Gemma text encoder model files.",
    )
    parser.add_argument(
        "--prompt",
        type=str,
        required=True,
        help="Text prompt describing the desired video content to be generated by the model.",
    )
    parser.add_argument(
        "--output-path",
        type=resolve_path,
        required=True,
        help="Path to the output video file (MP4 format).",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=DEFAULT_SEED,
        help=(
            f"Random seed value used to initialize the noise tensor for "
            f"reproducible generation (default: {DEFAULT_SEED})."
        ),
    )
    parser.add_argument(
        "--height",
        type=int,
        default=DEFAULT_1_STAGE_HEIGHT,
        help=f"Height of the generated video in pixels, should be divisible by 32 (default: {DEFAULT_1_STAGE_HEIGHT}).",
    )
    parser.add_argument(
        "--width",
        type=int,
        default=DEFAULT_1_STAGE_WIDTH,
        help=f"Width of the generated video in pixels, should be divisible by 32 (default: {DEFAULT_1_STAGE_WIDTH}).",
    )
    parser.add_argument(
        "--num-frames",
        type=int,
        default=DEFAULT_NUM_FRAMES,
        help=f"Number of frames to generate in the output video sequence, num-frames = (8 x K) + 1, "
        f"where k is a non-negative integer (default: {DEFAULT_NUM_FRAMES}).",
    )
    parser.add_argument(
        "--frame-rate",
        type=float,
        default=DEFAULT_FRAME_RATE,
        help=f"Frame rate of the generated video (fps) (default: {DEFAULT_FRAME_RATE}).",
    )
    parser.add_argument(
        "--num-inference-steps",
        type=int,
        default=DEFAULT_NUM_INFERENCE_STEPS,
        help=(
            f"Number of denoising steps in the diffusion sampling process. "
            f"Higher values improve quality but increase generation time (default: {DEFAULT_NUM_INFERENCE_STEPS})."
        ),
    )
    parser.add_argument(
        "--image",
        dest="images",
        action=ImageAction,
        nargs=3,
        metavar=("PATH", "FRAME_IDX", "STRENGTH"),
        default=[],
        help=(
            "Image conditioning input: path to image file, target frame index, "
            "and conditioning strength (all three required). Default: empty list [] (no image conditioning). "
            "Can be specified multiple times. Example: --image path/to/image1.jpg 0 0.8 "
            "--image path/to/image2.jpg 160 0.9"
        ),
    )
    parser.add_argument(
        "--lora",
        dest="lora",
        action=LoraAction,
        nargs="+",  # Accept 1-2 arguments per use (path and optional strength); validation is handled in LoraAction
        metavar=("PATH", "STRENGTH"),
        default=[],
        help=(
            "LoRA (Low-Rank Adaptation) model: path to model file and optional strength "
            f"(default strength: {DEFAULT_LORA_STRENGTH}). Can be specified multiple times. "
            "Example: --lora path/to/lora1.safetensors 0.8 --lora path/to/lora2.safetensors"
        ),
    )
    parser.add_argument(
        "--enable-fp8",
        action="store_true",
        help="Enable FP8 mode to reduce memory footprint by keeping model in lower precision. "
        "Note that calculations are still performed in bfloat16 precision.",
    )
    parser.add_argument("--enhance-prompt", action="store_true")
    return parser


def default_1_stage_arg_parser() -> argparse.ArgumentParser:
    parser = basic_arg_parser()
    parser.add_argument(
        "--cfg-guidance-scale",
        type=float,
        default=DEFAULT_CFG_GUIDANCE_SCALE,
        help=(
            f"Classifier-free guidance (CFG) scale controlling how strongly "
            f"the model adheres to the prompt. Higher values increase prompt "
            f"adherence but may reduce diversity (default: {DEFAULT_CFG_GUIDANCE_SCALE})."
        ),
    )
    parser.add_argument(
        "--negative-prompt",
        type=str,
        default=DEFAULT_NEGATIVE_PROMPT,
        help=(
            "Negative prompt describing what should not appear in the generated video, "
            "used to guide the diffusion process away from unwanted content. "
            "Default: a comprehensive negative prompt covering common artifacts and quality issues."
        ),
    )

    return parser


def default_2_stage_arg_parser() -> argparse.ArgumentParser:
    parser = default_1_stage_arg_parser()
    parser.set_defaults(height=DEFAULT_2_STAGE_HEIGHT, width=DEFAULT_2_STAGE_WIDTH)
    # Update help text to reflect 2-stage defaults
    for action in parser._actions:
        if "--height" in action.option_strings:
            action.help = (
                f"Height of the generated video in pixels, should be divisible by 64 "
                f"(default: {DEFAULT_2_STAGE_HEIGHT})."
            )
        if "--width" in action.option_strings:
            action.help = (
                f"Width of the generated video in pixels, should be divisible by 64 (default: {DEFAULT_2_STAGE_WIDTH})."
            )
    parser.add_argument(
        "--distilled-lora",
        dest="distilled_lora",
        action=LoraAction,
        nargs="+",  # Accept 1-2 arguments per use (path and optional strength); validation is handled in LoraAction
        metavar=("PATH", "STRENGTH"),
        required=True,
        help=(
            "Distilled LoRA (Low-Rank Adaptation) model used in the second stage (upscaling and refinement): "
            f"path to model file and optional strength (default strength: {DEFAULT_LORA_STRENGTH}). "
            "The second stage upsamples the video by 2x resolution and refines it using a distilled "
            "denoising schedule (fewer steps, no CFG). The distilled LoRA is specifically trained "
            "for this refinement process to improve quality at higher resolutions. "
            "Example: --distilled-lora path/to/distilled_lora.safetensors 0.8"
        ),
    )
    parser.add_argument(
        "--spatial-upsampler-path",
        type=resolve_path,
        required=True,
        help=(
            "Path to the spatial upsampler model used to increase the resolution "
            "of the generated video in the latent space."
        ),
    )
    return parser


def default_2_stage_distilled_arg_parser() -> argparse.ArgumentParser:
    parser = basic_arg_parser()
    parser.set_defaults(height=DEFAULT_2_STAGE_HEIGHT, width=DEFAULT_2_STAGE_WIDTH)
    # Update help text to reflect 2-stage defaults
    for action in parser._actions:
        if "--height" in action.option_strings:
            action.help = (
                f"Height of the generated video in pixels, should be divisible by 64 "
                f"(default: {DEFAULT_2_STAGE_HEIGHT})."
            )
        if "--width" in action.option_strings:
            action.help = (
                f"Width of the generated video in pixels, should be divisible by 64 (default: {DEFAULT_2_STAGE_WIDTH})."
            )
    parser.add_argument(
        "--spatial-upsampler-path",
        type=resolve_path,
        required=True,
        help=(
            "Path to the spatial upsampler model used to increase the resolution "
            "of the generated video in the latent space."
        ),
    )
    return parser