# SPDX-License-Identifier: Apache-2.0
"""
Diffusion pipelines for fastvideo.

This package contains diffusion pipelines for generating videos and images.
"""

from typing import cast

from fastvideo.fastvideo_args import FastVideoArgs
from fastvideo.logger import init_logger
from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
from fastvideo.pipelines.lora_pipeline import LoRAPipeline
from fastvideo.pipelines.pipeline_batch_info import ForwardBatch, TrainingBatch
from fastvideo.pipelines.pipeline_registry import PipelineType
from fastvideo.registry import get_model_info
from fastvideo.utils import maybe_download_model

logger = init_logger(__name__)


class PipelineWithLoRA(LoRAPipeline, ComposedPipelineBase):
    """Type for a pipeline that has both ComposedPipelineBase and LoRAPipeline functionality."""
    pass


def build_pipeline(fastvideo_args: FastVideoArgs,
                   pipeline_type: PipelineType | str = PipelineType.BASIC) -> PipelineWithLoRA:
    """
    Only works with valid hf diffusers configs. (model_index.json)
    We want to build a pipeline based on the inference args mode_path:
    1. resolve the pipeline class from the small Hub manifest
    2. download the required model components if needed
    3. verify the selected model components and build the pipeline
    """
    # Resolve the concrete pipeline from the small Hub manifest before
    # downloading large component weights.
    model_path = fastvideo_args.model_path
    logger.info("Building pipeline of type: %s",
                pipeline_type.value if isinstance(pipeline_type, PipelineType) else pipeline_type)

    model_info = get_model_info(
        model_path=model_path,
        pipeline_type=pipeline_type,
        workload_type=fastvideo_args.workload_type,
        override_pipeline_cls_name=fastvideo_args.override_pipeline_cls_name,
        revision=fastvideo_args.revision,
    )
    pipeline_cls = model_info.pipeline_cls

    model_path = maybe_download_model(
        model_path,
        revision=fastvideo_args.revision,
        allow_patterns=pipeline_cls.get_hf_download_allow_patterns(),
    )
    logger.info("Model path: %s", model_path)

    # instantiate the pipelines
    pipeline = pipeline_cls(model_path, fastvideo_args)

    logger.info("Pipelines instantiated")

    return cast(PipelineWithLoRA, pipeline)


__all__ = [
    "build_pipeline",
    "ComposedPipelineBase",
    "ForwardBatch",
    "LoRAPipeline",
    "TrainingBatch",
]