# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

from __future__ import annotations

from collections.abc import Callable, Mapping, Sequence
from dataclasses import fields
from typing import TYPE_CHECKING, Any

from prettytable import PrettyTable

from vllm_omni.metrics import definitions as metric_defs

if TYPE_CHECKING:
    from vllm_omni.metrics.modality import OmniModalityMetrics
    from vllm_omni.metrics.prometheus import OmniPrometheusMetrics
    from vllm_omni.metrics.stats import StageRequestStats

FAILURE_REASONS = frozenset({"client_abort", "client_disconnect", "stage_error", "unknown"})
DIFFUSION_METRICS_ONLY_REQUEST_ID = "__vllm_omni_diffusion_metrics__"


def extract_queue_wait_s(pipeline_timings: Mapping[str, float] | None) -> float | None:
    if pipeline_timings is None or "queue_wait_ms" not in pipeline_timings:
        return None
    return float(pipeline_timings["queue_wait_ms"] or 0.0) / 1000.0


def observe_stage_workload_metrics(
    prom_metrics: OmniPrometheusMetrics,
    *,
    stage_type: str,
    stage_metrics: StageRequestStats,
) -> None:
    if stage_type == "diffusion":
        prom_metrics.observe_num_inference_steps(stage_metrics.num_inference_steps)

    if stage_metrics.output_unit_type == "image":
        prom_metrics.observe_image_pixels(stage_metrics.image_pixels)
        prom_metrics.inc_image_count(stage_metrics.output_unit_count)


def observe_audio_finalize(
    mod_metrics: OmniModalityMetrics,
    *,
    stage_id: int,
    replica_id: int,
    stage_metrics: Any,
    engine_outputs: Any,
) -> None:
    stage_label = str(stage_id)
    replica_label = str(replica_id)
    gen_time_s = float(getattr(stage_metrics, "stage_gen_time_ms", 0.0)) / 1000.0
    mm_out = extract_mm_output(engine_outputs)

    sample_rate = metric_defs.resolve_audio_sample_rate(mm_out)
    n_frames = int(getattr(stage_metrics, "audio_generated_frames", 0) or 0)
    if n_frames == 0:
        n_frames = count_audio_frames(mm_out)
    mod_metrics.inc_audio_frames(stage_label, replica_label, n_frames)
    duration_s = n_frames / sample_rate if sample_rate > 0 else 0.0
    if duration_s > 0:
        mod_metrics.observe_audio_duration(stage_label, replica_label, duration_s)
        mod_metrics.observe_audio_rtf(
            stage_label,
            replica_label,
            metric_defs.compute_audio_rtf(gen_time_s, duration_s),
        )
    else:
        mod_metrics.inc_audio_skipped(stage_label, replica_label, "no_audio_data")


def observe_diffusion_finalize(
    mod_metrics: OmniModalityMetrics,
    *,
    stage_id: int,
    replica_id: int,
    stage_metrics: Any,
) -> None:
    diff_metrics = getattr(stage_metrics, "diffusion_metrics", None)
    if not diff_metrics:
        return

    stage_label = str(stage_id)
    replica_label = str(replica_id)

    exec_s = diff_metrics.get("diffusion_engine_exec_time_s")
    if exec_s is not None:
        mod_metrics.observe_diffusion_exec(stage_label, replica_label, float(exec_s))
        num_steps = int(
            getattr(stage_metrics, "num_inference_steps", 0) or diff_metrics.get("num_inference_steps") or 0
        )
        if num_steps > 0:
            mod_metrics.observe_diffusion_exec_per_step(stage_label, replica_label, float(exec_s) / num_steps)

    pre_s = diff_metrics.get("preprocess_time_s")
    if pre_s is not None:
        mod_metrics.observe_diffusion_preprocess(stage_label, replica_label, float(pre_s))

    post_s = diff_metrics.get("postprocess_time_s")
    if post_s is not None:
        mod_metrics.observe_diffusion_postprocess(stage_label, replica_label, float(post_s))

    vae_s = diff_metrics.get("vae_decode_time_s")
    if vae_s is not None:
        mod_metrics.observe_vae_decode(stage_label, replica_label, float(vae_s))

    forward_s = diff_metrics.get("forward_time_s")
    if forward_s is not None:
        mod_metrics.observe_diffusion_forward(stage_label, replica_label, float(forward_s))
        num_steps = int(
            getattr(stage_metrics, "num_inference_steps", 0) or diff_metrics.get("num_inference_steps") or 0
        )
        if num_steps > 0 and getattr(stage_metrics, "output_unit_type", None) == "image":
            mod_metrics.observe_denoise_step_latency(stage_label, replica_label, float(forward_s) / num_steps)

    kv_load_s = diff_metrics.get("kv_recv_time_s")
    if kv_load_s is not None:
        mod_metrics.observe_diffusion_kv_load(stage_label, replica_label, float(kv_load_s))


def normalize_failure_reason(reason: str | None) -> str:
    """Map request failure details to the bounded metrics taxonomy."""
    return reason if reason in FAILURE_REASONS else "unknown"


def diffusion_scheduler_waiting_metrics(n_waiting: int) -> dict[str, int]:
    """Build the diffusion scheduler snapshot consumed by the orchestrator."""
    return {metric_defs.DIFFUSION_SCHEDULER_WAITING_KEY: max(int(n_waiting), 0)}


def diffusion_exception_metrics(exc: BaseException) -> dict[str, Any]:
    """Return metrics attached to a terminal diffusion error."""
    metrics = getattr(exc, "diffusion_metrics", None)
    return dict(metrics) if isinstance(metrics, dict) else {}


def sum_diffusion_stage_durations_ms(output: Any, suffix: str) -> float | None:
    """Sum matching diffusion stage durations in milliseconds."""
    durations = getattr(output, "stage_durations", None)
    if not isinstance(durations, dict):
        return None

    total = 0.0
    found = False
    for key, value in durations.items():
        if not key.endswith(suffix):
            continue
        try:
            total += float(value)
            found = True
        except (TypeError, ValueError):
            continue
    return total * 1000.0 if found else None


def extract_diffusion_vae_decode_ms(output: Any) -> float | None:
    return sum_diffusion_stage_durations_ms(output, ".vae.decode")


def extract_diffusion_denoise_ms(output: Any) -> float | None:
    """Extract denoise-loop time (transformer and per-step scheduler)."""
    return sum_diffusion_stage_durations_ms(output, ".diffuse")


def coerce_bool(value: object, *, default: bool = False) -> bool:
    """Coerce JSON / form / env-like values to bool.

    Accepts ``bool``, numeric 0/1, and common truthy strings
    (``\"1\"`` / ``\"true\"`` / ``\"yes\"`` / ``\"on\"``, case-insensitive).
    ``None`` and unrecognized types return ``default``.
    """
    if value is None:
        return default
    if isinstance(value, bool):
        return value
    if isinstance(value, (int, float)):
        return bool(value)
    if isinstance(value, str):
        return value.strip().lower() in {"1", "true", "yes", "on"}
    return default


def coerce_positive_int_scalar(value: object) -> int | None:
    """Coerce a value to a positive int without importing tensor libs.

    Meant to pull positive integers such as sample rate out of the shapes
    that show up across omni stage outputs / configs:

    - plain ``int``: ``44100``
    - ``torch.Tensor`` / numpy scalar: ``tensor(44100)`` (via ``.item()``)
    - ``list`` / ``tuple`` wrap: ``[tensor(44100)]`` or ``[44100]``
    - Mapping field values: ``{"sr": tensor(44100)}`` → coerce the field
      value (key lookup is done by the caller, e.g.
      :func:`resolve_int_by_sequential_keys`)

    Returns ``None`` when the value is missing, unparsable, or not positive.
    """
    if value is None:
        return None
    if isinstance(value, (list, tuple)):
        for item in value:
            coerced = coerce_positive_int_scalar(item)
            if coerced is not None:
                return coerced
        return None
    item = getattr(value, "item", None)
    if callable(item):
        try:
            value = item()
        except Exception:
            return None
    try:
        value = int(value)
    except (TypeError, ValueError):
        return None
    return value if value > 0 else None


def coerce_positive_float_scalar(value: object) -> float | None:
    """Coerce a scalar-like value to a positive float.

    Handles plain numeric values, one-element tensor/numpy-like values via
    ``.item()``, and list/tuple wrappers. Returns ``None`` when no positive
    float can be extracted.
    """
    if value is None:
        return None
    if isinstance(value, (list, tuple)):
        for item in value:
            coerced = coerce_positive_float_scalar(item)
            if coerced is not None:
                return coerced
        return None
    item = getattr(value, "item", None)
    if callable(item):
        try:
            value = item()
        except Exception:
            return None
    try:
        value = float(value)
    except (TypeError, ValueError):
        return None
    return value if value > 0 else None


def resolve_int_by_sequential_keys(
    source: Mapping[str, object] | object | None,
    keys: Sequence[str],
) -> int | None:
    """Return the first positive int found by trying ``keys`` in order.

    For each key, looks up ``source[key]`` when ``source`` is a Mapping, otherwise
    ``getattr(source, key, None)``. Values are coerced via :func:`coerce_positive_int_scalar`.
    Returns ``None`` when ``source`` is empty/missing or no key yields a usable int.
    """
    if not source:
        return None
    for key in keys:
        raw = source.get(key) if isinstance(source, Mapping) else getattr(source, key, None)
        value = coerce_positive_int_scalar(raw)
        if value is not None:
            return value
    return None


def extract_mm_output(mm_source: object) -> Mapping[str, Any]:
    """Return the first non-empty multimodal_output Mapping on ``mm_source``.

    Lookup order:
      1. ``mm_source.multimodal_output`` — top-level attribute / property
      2. ``mm_source.outputs[0].multimodal_output`` — CompletionOutput nesting
         (typical AR audio path)

    Accepts both plain ``dict`` and ``MultimodalPayload`` (a ``Mapping``).
    Returns ``{}`` when neither location yields a non-empty Mapping.

    Args:
        mm_source: Duck-typed container with optional ``multimodal_output`` and/or
            ``outputs`` (e.g. vLLM ``RequestOutput`` or ``OmniRequestOutput``).

    Returns:
        The first non-empty multimodal Mapping found, or an empty ``dict``.
    """
    mm = getattr(mm_source, "multimodal_output", None)
    if isinstance(mm, Mapping) and mm:
        return mm
    outs = getattr(mm_source, "outputs", None)
    if outs:
        nested = getattr(outs[0], "multimodal_output", None)
        if isinstance(nested, Mapping) and nested:
            return nested
    return {}


def iter_mm_outputs(mm_source: object) -> list[Mapping[str, Any]]:
    """Collect all non-empty multimodal_output Mappings from ``mm_source``.

    Lookup order:
      1. ``mm_source.multimodal_output`` — top-level attribute / property
      2. each ``mm_source.outputs[i].multimodal_output`` — every CompletionOutput
         entry that carries a non-empty multimodal Mapping

    Accepts both plain ``dict`` and ``MultimodalPayload`` (a ``Mapping``).
    Used by stage-pool metrics aggregation that needs to visit every mm payload.

    Args:
        mm_source: Duck-typed container with optional ``multimodal_output`` and/or
            ``outputs`` (e.g. vLLM ``RequestOutput`` or ``OmniRequestOutput``).

    Returns:
        A list of non-empty multimodal Mappings in discovery order. Empty when
        none are present.
    """
    multimodal_outputs: list[Mapping[str, Any]] = []
    outer_mm = getattr(mm_source, "multimodal_output", None)
    if isinstance(outer_mm, Mapping) and outer_mm:
        multimodal_outputs.append(outer_mm)
    for output in getattr(mm_source, "outputs", None) or []:
        inner_mm = getattr(output, "multimodal_output", None)
        if isinstance(inner_mm, Mapping) and inner_mm:
            multimodal_outputs.append(inner_mm)
    return multimodal_outputs


def count_audio_chunk_frames(audio_chunk: object) -> int:
    """Count frames (samples) in one audio tensor / chunk.

    Audio chunks are concatenated on dim=-1 in the output processor, so the
    frame/sample axis is the last dim (e.g. ``[channels, frames]``). Keep this
    aligned with serving_chat.py: audio tensors are consumed as ``(T,)``,
    ``(C, T)``, or ``(B, C, T)``. Flattening would corrupt multi-channel audio.

    Args:
        audio_chunk: A single audio tensor/array-like, or a scalar-like value.

    Returns:
        Frame count for this chunk. Uses ``shape[-1]`` when shaped; otherwise
        ``len(...)``; scalars / unlenable values count as ``1``.
    """
    shape = getattr(audio_chunk, "shape", None)
    if shape is not None and len(shape) > 0:
        return int(shape[-1])
    try:
        return len(audio_chunk)  # type: ignore[arg-type]
    except TypeError:
        return 1


def count_audio_frames(mm_out: Mapping[str, Any]) -> int:
    """Sum frame counts over all audio chunks in ``mm_out["audio"]`` or with other related keys.

    For multi-dim tensors (e.g. shape ``[channels, samples]``) the last axis is
    the sample dim; for 1-D tensors the only axis is the sample dim; scalars
    count as 1. Missing or empty ``audio`` yields ``0``.

    Args:
        mm_out: A multimodal_output Mapping (plain ``dict`` or
            ``MultimodalPayload``) that may contain an ``audio`` or related key whose value
            is one chunk or a list of chunks.

    Returns:
        Total audio frames (samples) across all chunks.
    """
    audio_chunks = None
    if isinstance(mm_out, Mapping):
        for key in ("audio", "model_outputs"):
            audio_chunks = mm_out.get(key)
            if audio_chunks is not None:
                break
    if audio_chunks is None:
        return 0
    chunks = audio_chunks if isinstance(audio_chunks, list) else [audio_chunks]
    return sum(count_audio_chunk_frames(chunk) for chunk in chunks)


def count_image_pixels(value: object) -> int:
    """Count pixels in one image value, or sum over a nested list/tuple.

    Accepts PIL-like objects (``size=(W, H)``), tensors / arrays with a
    ``shape`` attribute, and nested ``list`` / ``tuple`` containers.

    Shape heuristics (aligned with StagePool image metrics):

    - ``ndim >= 4`` (e.g. ``BCHW``): ``B * H * W`` via
      ``dims[0] * dims[-2] * dims[-1]``
    - ``ndim == 3`` and ``dims[0] in (1, 3, 4)``: CHW → ``H * W``
    - ``ndim == 3`` and ``dims[-1] in (1, 3, 4)``: HWC → ``H * W``
    - otherwise: ``dims[-2] * dims[-1]``

    Returns ``0`` when ``value`` is missing or cannot be interpreted.
    """
    if value is None:
        return 0
    if isinstance(value, (list, tuple)):
        return sum(count_image_pixels(item) for item in value)

    size = getattr(value, "size", None)
    if isinstance(size, tuple) and len(size) >= 2:
        try:
            return int(size[0]) * int(size[1])
        except (TypeError, ValueError):
            return 0

    shape = getattr(value, "shape", None)
    if shape is None or len(shape) < 2:
        return 0
    dims = [int(dim) for dim in shape]
    if len(dims) >= 4:
        return dims[0] * dims[-2] * dims[-1]
    if len(dims) == 3 and dims[0] in (1, 3, 4):
        return dims[1] * dims[2]
    if len(dims) == 3 and dims[-1] in (1, 3, 4):
        return dims[0] * dims[1]
    return dims[-2] * dims[-1]


def count_video_frames(video: object) -> int | None:
    """Return the frame count for common nested and tensor video layouts."""
    if isinstance(video, Mapping):
        for key in ("video", "frames", "images"):
            if key in video:
                return count_video_frames(video[key])
        return None

    if isinstance(video, (list, tuple)):
        return len(video)

    shape = getattr(video, "shape", None)
    if shape is None:
        return None
    try:
        dims = tuple(int(dim) for dim in shape)
    except (TypeError, ValueError):
        return None

    if len(dims) == 5:
        # Common layouts: [B, C, F, H, W] and [B, F, H, W, C].
        if dims[-1] in (1, 3, 4):
            return dims[1]
        if dims[1] in (1, 3, 4):
            return dims[2]
        return dims[1]
    if len(dims) == 4:
        # Common layouts: [F, H, W, C] and [C, F, H, W].
        if dims[-1] in (1, 3, 4):
            return dims[0]
        if dims[0] in (1, 3, 4):
            return dims[1]
        return dims[0]

    return None


def _build_field_defs(
    cls: type,
    exclude: set[str],
    transforms: dict[str, tuple[str, Callable]] | None = None,
) -> list[tuple[str, Callable[[Any], Any]]]:
    """Auto-generate field definitions from a dataclass.

    Args:
        cls: The dataclass type to extract fields from.
        exclude: Set of field names to exclude from output.
        transforms: Optional mapping of field transformations.
            Format: {original_name: (display_name, transform_fn)}

    Returns:
        List of (display_name, getter_fn) tuples for table generation.
    """
    transforms = transforms or {}
    result = []
    for f in fields(cls):
        if f.name in exclude:
            continue
        if f.name in transforms:
            display_name, transform_fn = transforms[f.name]
            # Capture variables in closure to avoid late binding issues
            result.append((display_name, lambda e, fn=transform_fn, n=f.name: fn(getattr(e, n))))
        else:
            result.append((f.name, lambda e, n=f.name: getattr(e, n)))
    return result


def _build_row(evt: Any, field_defs: list[tuple[str, Callable]]) -> dict[str, Any]:
    """Build a row dict from an event object using field definitions.

    Args:
        evt:  The event object (dataclass instance).
        field_defs: List of (field_name, getter_fn) tuples.

    Returns:
        Dict mapping field names to their values.
    """
    return {name: getter(evt) for name, getter in field_defs}


def _get_field_names(field_defs: list[tuple[str, Callable]]) -> list[str]:
    """Extract field names from field definitions.

    Args:
        field_defs: List of (field_name, getter_fn) tuples.

    Returns:
        List of field names.
    """
    return [name for name, _ in field_defs]


def _format_table(
    title: str,
    data: dict[str, Any] | list[dict[str, Any]],
    value_fields: list[str],
    column_key: str | None = None,
    column_prefix: str = "",
) -> str:
    """Format a table for display.

    Supports two modes:
    1. Single-column mode:  data is a dict, displays as Field | Value
    2. Multi-column mode: data is a list of dicts, displays as Field | col1 | col2 | ...

    Args:
        title:  Table title.
        data: Either a single dict (single-column) or list of dicts (multi-column).
        value_fields: List of field names to display as rows.
        column_key: Key in each dict used as column header (required for multi-column mode).
        column_prefix: Optional prefix for column headers (multi-column mode only).

    Returns:
        Formatted table string.
    """
    if not data:
        return f"[{title}] <empty>"

    def _format_value(value: Any) -> str:
        if isinstance(value, bool):
            return str(value)
        if isinstance(value, int):
            return f"{value:,}"
        if isinstance(value, float):
            return f"{value:,.3f}"
        if isinstance(value, list):
            if not value:
                return ""
            try:
                avg = sum(float(v) for v in value) / len(value)
                return f"{avg:,.3f} (n={len(value)})"
            except (TypeError, ValueError):
                return ", ".join(str(v) for v in value)
        return str(value)

    table = PrettyTable()

    # Single-column mode:  data is a dict
    if isinstance(data, dict):
        table.field_names = ["Field", "Value"]
        table.align["Field"] = "l"
        table.align["Value"] = "r"
        for field in value_fields:
            if field in data:
                if isinstance(data[field], dict):
                    for sub_key, sub_value in data[field].items():
                        table.add_row([f"{sub_key}", _format_value(sub_value)])
                else:
                    table.add_row([field, _format_value(data[field])])

    # Multi-column mode: data is a list of dicts
    else:
        if column_key is None:
            raise ValueError("column_key is required for multi-column mode")
        col_headers = [f"{column_prefix}{row.get(column_key, '?')}" for row in data]
        # PrettyTable requires unique field names.  When the same column key
        # appears more than once (e.g. two events with the same stage_id),
        # deduplicate by appending _2, _3, … to the later occurrences.
        if len(col_headers) != len(set(col_headers)):
            seen: dict[str, int] = {}
            deduped: list[str] = []
            for h in col_headers:
                n = seen.get(h, 0)
                seen[h] = n + 1
                deduped.append(h if n == 0 else f"{h}_{n + 1}")
            col_headers = deduped
        table.field_names = ["Field"] + col_headers
        table.align["Field"] = "l"
        for col in col_headers:
            table.align[col] = "r"
        for field in value_fields:
            row_values = [_format_value(r.get(field, "")) for r in data]
            table.add_row([field] + row_values)

    return "\n".join([f"[{title}]", table.get_string()])


def count_tokens_from_outputs(engine_outputs: list[Any]) -> int:
    total = 0
    for _ro in engine_outputs:
        try:
            outs = getattr(_ro, "outputs", None)
            if outs and len(outs) > 0:
                for output in outs:
                    # In DELTA mode token_ids only contains the latest chunk.
                    # Omni's output processor attaches the cumulative sequence
                    # for inter-stage routing and accurate metrics.
                    tokens = getattr(output, "cumulative_token_ids", None)
                    if tokens is None:
                        tokens = getattr(output, "token_ids", None)
                    if tokens is not None:
                        total += len(tokens)
        except Exception:
            # Ignore any issues with individual outputs to keep token counting best-effort.
            pass
    return total