# Copyright 2026 The HuggingFace Team. 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.

import inspect

import numpy as np
import torch

from ...configuration_utils import FrozenDict
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import logging
from ...utils.torch_utils import randn_tensor
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
from .modular_pipeline import LTXModularPipeline, LTXVideoPachifier


logger = logging.get_logger(__name__)


def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.15,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: int | None = None,
    device: str | torch.device | None = None,
    timesteps: list[int] | None = None,
    sigmas: list[float] | None = None,
    **kwargs,
):
    r"""
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

    Args:
        scheduler (`SchedulerMixin`):
            The scheduler to get timesteps from.
        num_inference_steps (`int`):
            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
            must be `None`.
        device (`str` or `torch.device`, *optional*):
            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        timesteps (`list[int]`, *optional*):
            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
            `num_inference_steps` and `sigmas` must be `None`.
        sigmas (`list[float]`, *optional*):
            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
            `num_inference_steps` and `timesteps` must be `None`.

    Returns:
        `tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
        second element is the number of inference steps.
    """
    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accepts_timesteps:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" timestep schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accept_sigmas:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" sigmas schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


class LTXTextInputStep(ModularPipelineBlocks):
    model_name = "ltx"

    @property
    def description(self) -> str:
        return (
            "Input processing step that:\n"
            "  1. Determines `batch_size` and `dtype` based on `prompt_embeds`\n"
            "  2. Adjusts input tensor shapes based on `batch_size` and `num_videos_per_prompt`"
        )

    @property
    def inputs(self) -> list[InputParam]:
        return [
            InputParam.template("num_images_per_prompt", name="num_videos_per_prompt"),
            InputParam.template("prompt_embeds", required=True),
            InputParam.template("prompt_embeds_mask", name="prompt_attention_mask"),
            InputParam.template("negative_prompt_embeds"),
            InputParam.template("negative_prompt_embeds_mask", name="negative_prompt_attention_mask"),
        ]

    @property
    def intermediate_outputs(self) -> list[OutputParam]:
        return [
            OutputParam("batch_size", type_hint=int),
            OutputParam("dtype", type_hint=torch.dtype),
        ]

    @torch.no_grad()
    def __call__(self, components: LTXModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)

        block_state.batch_size = block_state.prompt_embeds.shape[0]
        block_state.dtype = block_state.prompt_embeds.dtype
        num_videos = block_state.num_videos_per_prompt

        # Repeat prompt_embeds for num_videos_per_prompt
        _, seq_len, _ = block_state.prompt_embeds.shape
        block_state.prompt_embeds = block_state.prompt_embeds.repeat(1, num_videos, 1)
        block_state.prompt_embeds = block_state.prompt_embeds.view(block_state.batch_size * num_videos, seq_len, -1)

        if block_state.prompt_attention_mask is not None:
            block_state.prompt_attention_mask = block_state.prompt_attention_mask.repeat(num_videos, 1)

        if block_state.negative_prompt_embeds is not None:
            _, seq_len, _ = block_state.negative_prompt_embeds.shape
            block_state.negative_prompt_embeds = block_state.negative_prompt_embeds.repeat(1, num_videos, 1)
            block_state.negative_prompt_embeds = block_state.negative_prompt_embeds.view(
                block_state.batch_size * num_videos, seq_len, -1
            )

        if block_state.negative_prompt_attention_mask is not None:
            block_state.negative_prompt_attention_mask = block_state.negative_prompt_attention_mask.repeat(
                num_videos, 1
            )

        self.set_block_state(state, block_state)
        return components, state


class LTXSetTimestepsStep(ModularPipelineBlocks):
    model_name = "ltx"

    @property
    def expected_components(self) -> list[ComponentSpec]:
        return [
            ComponentSpec("scheduler", FlowMatchEulerDiscreteScheduler),
        ]

    @property
    def description(self) -> str:
        return "Step that sets the scheduler's timesteps for inference"

    @property
    def inputs(self) -> list[InputParam]:
        return [
            InputParam.template("num_inference_steps"),
            InputParam.template("timesteps"),
            InputParam.template("sigmas"),
            InputParam.template("height", default=512),
            InputParam.template("width", default=704),
            InputParam("num_frames", type_hint=int, default=161),
            InputParam("frame_rate", type_hint=int, default=25),
        ]

    @property
    def intermediate_outputs(self) -> list[OutputParam]:
        return [
            OutputParam("timesteps", type_hint=torch.Tensor),
            OutputParam("num_inference_steps", type_hint=int),
            OutputParam("rope_interpolation_scale", type_hint=tuple),
        ]

    @torch.no_grad()
    def __call__(self, components: LTXModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)
        device = components._execution_device

        height = block_state.height
        width = block_state.width
        num_frames = block_state.num_frames
        frame_rate = block_state.frame_rate

        latent_num_frames = (num_frames - 1) // components.vae_temporal_compression_ratio + 1
        latent_height = height // components.vae_spatial_compression_ratio
        latent_width = width // components.vae_spatial_compression_ratio
        video_sequence_length = latent_num_frames * latent_height * latent_width

        custom_timesteps = block_state.timesteps
        sigmas = block_state.sigmas

        if custom_timesteps is not None:
            # User provided custom timesteps, don't compute sigmas
            block_state.timesteps, block_state.num_inference_steps = retrieve_timesteps(
                components.scheduler,
                block_state.num_inference_steps,
                device,
                custom_timesteps,
            )
        else:
            if sigmas is None:
                sigmas = np.linspace(1.0, 1 / block_state.num_inference_steps, block_state.num_inference_steps)

            mu = calculate_shift(
                video_sequence_length,
                components.scheduler.config.get("base_image_seq_len", 256),
                components.scheduler.config.get("max_image_seq_len", 4096),
                components.scheduler.config.get("base_shift", 0.5),
                components.scheduler.config.get("max_shift", 1.15),
            )

            block_state.timesteps, block_state.num_inference_steps = retrieve_timesteps(
                components.scheduler,
                block_state.num_inference_steps,
                device,
                sigmas=sigmas,
                mu=mu,
            )

        block_state.rope_interpolation_scale = (
            components.vae_temporal_compression_ratio / frame_rate,
            components.vae_spatial_compression_ratio,
            components.vae_spatial_compression_ratio,
        )

        self.set_block_state(state, block_state)
        return components, state


class LTXPrepareLatentsStep(ModularPipelineBlocks):
    model_name = "ltx"

    @property
    def description(self) -> str:
        return "Prepare latents step that prepares the latents for the text-to-video generation process"

    @property
    def expected_components(self) -> list[ComponentSpec]:
        return [
            ComponentSpec(
                "pachifier",
                LTXVideoPachifier,
                config=FrozenDict({"patch_size": 1, "patch_size_t": 1}),
                default_creation_method="from_config",
            ),
        ]

    @property
    def inputs(self) -> list[InputParam]:
        return [
            InputParam.template("height", default=512),
            InputParam.template("width", default=704),
            InputParam("num_frames", type_hint=int, default=161),
            InputParam.template("latents"),
            InputParam.template("num_images_per_prompt", name="num_videos_per_prompt"),
            InputParam.template("generator"),
            InputParam.template("batch_size", required=True),
        ]

    @property
    def intermediate_outputs(self) -> list[OutputParam]:
        return [
            OutputParam("latents", type_hint=torch.Tensor),
        ]

    @torch.no_grad()
    def __call__(self, components: LTXModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)
        device = components._execution_device

        batch_size = block_state.batch_size * block_state.num_videos_per_prompt
        num_channels_latents = components.transformer.config.in_channels

        if block_state.latents is not None:
            block_state.latents = block_state.latents.to(device=device, dtype=torch.float32)
        else:
            height = block_state.height // components.vae_spatial_compression_ratio
            width = block_state.width // components.vae_spatial_compression_ratio
            num_frames = (block_state.num_frames - 1) // components.vae_temporal_compression_ratio + 1

            shape = (batch_size, num_channels_latents, num_frames, height, width)
            block_state.latents = randn_tensor(
                shape, generator=block_state.generator, device=device, dtype=torch.float32
            )
            block_state.latents = components.pachifier.pack_latents(block_state.latents)

        self.set_block_state(state, block_state)
        return components, state


class LTXImage2VideoPrepareLatentsStep(ModularPipelineBlocks):
    model_name = "ltx"

    @property
    def description(self) -> str:
        return (
            "Prepare image-to-video latents: adds noise to pre-encoded image latents and creates a conditioning mask. "
            "Expects pure noise `latents` from LTXPrepareLatentsStep."
        )

    @property
    def expected_components(self) -> list[ComponentSpec]:
        return [
            ComponentSpec(
                "pachifier",
                LTXVideoPachifier,
                config=FrozenDict({"patch_size": 1, "patch_size_t": 1}),
                default_creation_method="from_config",
            ),
        ]

    @property
    def inputs(self) -> list[InputParam]:
        return [
            InputParam("image_latents", type_hint=torch.Tensor, required=True),
            InputParam.template("latents", required=True),
            InputParam.template("height", default=512),
            InputParam.template("width", default=704),
            InputParam("num_frames", type_hint=int, default=161),
            InputParam.template("num_images_per_prompt", name="num_videos_per_prompt"),
            InputParam.template("batch_size", required=True),
        ]

    @property
    def intermediate_outputs(self) -> list[OutputParam]:
        return [
            OutputParam("latents", type_hint=torch.Tensor),
            OutputParam("conditioning_mask", type_hint=torch.Tensor),
        ]

    @torch.no_grad()
    def __call__(self, components: LTXModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)
        device = components._execution_device

        batch_size = block_state.batch_size * block_state.num_videos_per_prompt

        height = block_state.height // components.vae_spatial_compression_ratio
        width = block_state.width // components.vae_spatial_compression_ratio
        num_frames = (block_state.num_frames - 1) // components.vae_temporal_compression_ratio + 1

        init_latents = block_state.image_latents.to(device=device, dtype=torch.float32)
        if init_latents.shape[0] < batch_size:
            init_latents = init_latents.repeat_interleave(batch_size // init_latents.shape[0], dim=0)
        init_latents = init_latents.repeat(1, 1, num_frames, 1, 1)

        conditioning_mask = torch.zeros(
            init_latents.shape[0],
            1,
            init_latents.shape[2],
            init_latents.shape[3],
            init_latents.shape[4],
            device=device,
            dtype=torch.float32,
        )
        conditioning_mask[:, :, 0] = 1.0

        noise = components.pachifier.unpack_latents(block_state.latents, num_frames, height, width)
        latents = init_latents * conditioning_mask + noise * (1 - conditioning_mask)

        conditioning_mask = components.pachifier.pack_latents(conditioning_mask).squeeze(-1)
        latents = components.pachifier.pack_latents(latents)

        block_state.latents = latents
        block_state.conditioning_mask = conditioning_mask

        self.set_block_state(state, block_state)
        return components, state