from __future__ import annotations

import json
from collections import Counter
from pathlib import Path
from typing import Any, Iterator

from .base import BaseModelInspector, InspectorError, ModelInspection, TensorStats, is_cancelled, report
from .statistics import (
    discover_checkpoint_paths,
    discover_config_files,
    step_from_name,
    tensor_stats_from_torch,
)


def _folder_size(path: Path) -> int:
    if path.is_file():
        return path.stat().st_size
    total = 0
    try:
        for item in path.rglob("*"):
            if item.is_file():
                total += item.stat().st_size
    except OSError:
        return total
    return total


def _read_configs(files: list[Path], root: Path) -> dict[str, Any]:
    configs: dict[str, Any] = {}
    for file in files[:40]:
        try:
            key = str(file.relative_to(root if root.is_dir() else root.parent))
        except ValueError:
            key = file.name
        try:
            configs[key] = json.loads(file.read_text(encoding="utf-8"))
        except (OSError, UnicodeDecodeError, json.JSONDecodeError):
            configs[key] = "<unreadable>"
    return configs


def _resolution_from_configs(configs: dict[str, Any], settings: dict[str, Any] | None) -> int | None:
    for source in (settings or {}, *[value for value in configs.values() if isinstance(value, dict)]):
        for key in ("resolution", "sample_size", "image_size", "size"):
            value = source.get(key) if isinstance(source, dict) else None
            if isinstance(value, int):
                return value
            if isinstance(value, (list, tuple)) and value and isinstance(value[0], int):
                return int(value[0])
            try:
                if value:
                    return int(value)
            except (TypeError, ValueError):
                pass
    return None


def _iter_safetensors(file: Path) -> Iterator[tuple[str, Any, dict[str, Any]]]:
    from safetensors import safe_open

    with safe_open(str(file), framework="pt", device="cpu") as handle:
        metadata = handle.metadata() or {}
        for key in handle.keys():
            yield key, handle.get_tensor(key), metadata


def _extract_state_dict(payload: Any) -> dict[str, Any]:
    try:
        import torch
    except Exception:
        torch = None
    if torch is not None and hasattr(payload, "shape"):
        return {"tensor": payload}
    if isinstance(payload, dict):
        for key in ("state_dict", "model_state_dict", "model", "module", "unet", "network"):
            value = payload.get(key)
            if isinstance(value, dict) and any(hasattr(item, "shape") for item in value.values()):
                return value
        if any(hasattr(item, "shape") for item in payload.values()):
            return payload
    return {}


def _iter_torch_checkpoint(file: Path) -> Iterator[tuple[str, Any, dict[str, Any]]]:
    import torch

    try:
        payload = torch.load(str(file), map_location="cpu", weights_only=True)
    except TypeError:
        payload = torch.load(str(file), map_location="cpu")
    except Exception:
        payload = torch.load(str(file), map_location="cpu", weights_only=False)
    state = _extract_state_dict(payload)
    metadata = {key: value for key, value in payload.items() if key not in state} if isinstance(payload, dict) else {}
    for key, tensor in state.items():
        if hasattr(tensor, "shape"):
            yield str(key), tensor, metadata


def _weight_files(path: Path) -> list[Path]:
    if path.is_file():
        return [path]
    ignored_names = {"optimizer.bin", "scheduler.bin", "scaler.pt"}
    names = {
        "diffusion_pytorch_model.safetensors",
        "model.safetensors",
        "pytorch_model.bin",
        "adapter_model.safetensors",
        "adapter_model.bin",
        "checkpoint.pt",
        "best_checkpoint.pt",
    }
    files: list[Path] = []
    try:
        for item in path.rglob("*"):
            if item.is_file() and (item.name in names or item.suffix.casefold() in {".safetensors", ".pt", ".pth", ".bin", ".ckpt"}):
                if item.name.casefold() not in ignored_names:
                    files.append(item)
    except OSError:
        return []
    if (path / "model_index.json").is_file():
        final_files = [
            item for item in files
            if not any(part.startswith("checkpoint-") for part in item.relative_to(path).parts)
        ]
        if final_files:
            files = final_files
    return sorted(files, key=lambda item: (0 if item.name in names else 1, str(item)))


class GenericModelInspector(BaseModelInspector):
    architecture = "Generic / Unknown"

    def inspect(
        self,
        path: str | Path,
        *,
        recorded_architecture: str = "",
        run_settings: dict[str, Any] | None = None,
        progress=None,
        cancelled=None,
    ) -> ModelInspection:
        target = Path(path).expanduser()
        if not target.exists():
            raise InspectorError(f"Model path does not exist: {target}")
        target = target.resolve()
        report(progress, 3, "Finding model files")
        config_files = discover_config_files(target)
        configs = _read_configs(config_files, target)
        files = _weight_files(target)
        if not files:
            message = "Model contains no readable tensor checkpoint"
            return self._empty(target, recorded_architecture, run_settings, config_files, configs, message)

        tensors: list[TensorStats] = []
        dtypes: Counter[str] = Counter()
        components: Counter[str] = Counter()
        health: list[str] = []
        messages: list[str] = []
        metadata: dict[str, Any] = {}
        for file_index, file in enumerate(files):
            if is_cancelled(cancelled):
                raise InspectorError("Inspection cancelled.")
            report(progress, 8 + int(80 * file_index / max(1, len(files))), f"Reading {file.name}")
            try:
                if file.suffix.casefold() == ".safetensors":
                    iterator = _iter_safetensors(file)
                else:
                    iterator = _iter_torch_checkpoint(file)
                for name, tensor, file_metadata in iterator:
                    if is_cancelled(cancelled):
                        raise InspectorError("Inspection cancelled.")
                    prefix = file.parent.name if len(files) > 1 else ""
                    stat = tensor_stats_from_torch(f"{prefix}.{name}" if prefix and not name.startswith(prefix) else name, tensor)
                    tensors.append(stat)
                    dtypes[stat.dtype] += stat.parameter_count
                    components[stat.component] += stat.parameter_count
                    health.extend(f"{stat.name}: {item}" for item in stat.health)
                    if file_metadata:
                        metadata.update(file_metadata)
            except Exception as exc:
                health.append(f"{file.name}: unreadable checkpoint ({exc})")

        if not tensors:
            message = "Model contains no readable tensor checkpoint"
            return self._empty(target, recorded_architecture, run_settings, config_files, configs, message, [*(health or []), message])

        report(progress, 92, "Summarizing model")
        total_parameters = sum(tensor.parameter_count for tensor in tensors)
        parameter_memory = sum(tensor.memory_bytes for tensor in tensors)
        largest = sorted(tensors, key=lambda item: item.parameter_count, reverse=True)[:20]
        architecture, confidence, message = self._architecture_from_signals(
            target, recorded_architecture, configs, [tensor.name for tensor in tensors]
        )
        messages.append(message)
        duplicate_count = len(tensors) - len({tensor.name for tensor in tensors})
        if duplicate_count:
            health.append(f"Unusual: {duplicate_count} duplicate tensor names after folder merging")
        checkpoints = [str(item) for item in discover_checkpoint_paths(target)]
        return ModelInspection(
            path=str(path),
            resolved_path=str(target),
            architecture=architecture,
            confidence=confidence,
            status="ok",
            size_bytes=_folder_size(target),
            config_files=[str(item) for item in config_files],
            resolution=_resolution_from_configs(configs, run_settings),
            epoch=self._number_from_metadata(metadata, "epoch"),
            step=self._number_from_metadata(metadata, "step") or step_from_name(target.name),
            tensor_count=len(tensors),
            total_parameters=total_parameters,
            trainable_parameters=self._trainable_parameters(tensors, architecture),
            parameter_memory_bytes=parameter_memory,
            dtypes=dict(dtypes),
            components=dict(components),
            largest_tensors=largest,
            tensors=tensors,
            health=health or ["No invalid tensor values found in sampled statistics."],
            messages=messages,
            lora=self._lora_info(tensors, configs),
            configs=configs,
            histogram=self._histogram(tensors),
            tensor_size_distribution=[(tensor.name, tensor.parameter_count) for tensor in largest],
            checkpoints=checkpoints,
            loss_history=[],
        )

    def _empty(
        self,
        target: Path,
        recorded_architecture: str,
        run_settings: dict[str, Any] | None,
        config_files: list[Path],
        configs: dict[str, Any],
        message: str,
        health: list[str] | None = None,
    ) -> ModelInspection:
        architecture, confidence, detection_message = self._architecture_from_signals(target, recorded_architecture, configs, [])
        return ModelInspection(
            path=str(target),
            resolved_path=str(target),
            architecture=architecture,
            confidence=confidence,
            status="warning",
            size_bytes=_folder_size(target),
            config_files=[str(item) for item in config_files],
            resolution=_resolution_from_configs(configs, run_settings),
            epoch=None,
            step=step_from_name(target.name),
            tensor_count=0,
            total_parameters=0,
            trainable_parameters=None,
            parameter_memory_bytes=0,
            dtypes={},
            components={},
            largest_tensors=[],
            tensors=[],
            health=health or [message],
            messages=[detection_message, message],
            configs=configs,
            checkpoints=[str(item) for item in discover_checkpoint_paths(target)],
        )

    @staticmethod
    def _number_from_metadata(metadata: dict[str, Any], key: str) -> int | None:
        for candidate in (key, f"global_{key}", f"current_{key}"):
            try:
                value = metadata.get(candidate)
                if value is not None:
                    return int(value)
            except (TypeError, ValueError):
                pass
        return None

    @staticmethod
    def _trainable_parameters(tensors: list[TensorStats], architecture: str) -> int | None:
        if architecture == "LoRA":
            return sum(tensor.parameter_count for tensor in tensors)
        return None

    @staticmethod
    def _architecture_from_signals(
        target: Path,
        recorded_architecture: str,
        configs: dict[str, Any],
        tensor_names: list[str],
    ) -> tuple[str, float, str]:
        recorded = recorded_architecture.casefold()
        joined_names = "\n".join(tensor_names).casefold()
        config_text = json.dumps(configs, default=str).casefold()
        folder_text = str(target).casefold()
        signals = " ".join((joined_names, config_text, folder_text))
        if "lora" in recorded or "lora" in signals or "adapter_config" in signals:
            return "LoRA", 0.92, "Model recognized as LoRA"
        if "maskgit" in recorded or "maskgit" in signals:
            return "MaskGIT", 0.86, "Model recognized as MaskGIT"
        if "flow" in recorded or "rectified_flow" in signals or "flow_model_info" in signals:
            return "Flow Matching", 0.9, "Model recognized as Flow Matching"
        if "ddpm" in recorded or "diffusers" in config_text or "unet" in signals or "scheduler_config" in signals:
            return "DDPM / Diffusers", 0.88, "Model recognized as DDPM"
        return "Generic / Unknown", 0.35, "Model type uncertain - using generic tensor inspection"

    @staticmethod
    def _lora_info(tensors: list[TensorStats], configs: dict[str, Any]) -> dict[str, Any]:
        lora_tensors = [tensor for tensor in tensors if "lora" in tensor.name.casefold()]
        if not lora_tensors:
            return {}
        down = [tensor for tensor in lora_tensors if any(token in tensor.name.casefold() for token in ("down", "lora_a"))]
        up = [tensor for tensor in lora_tensors if any(token in tensor.name.casefold() for token in ("up", "lora_b"))]
        ranks = sorted({tensor.shape[0] for tensor in down if tensor.shape})
        alpha = None
        targets: set[str] = set()
        for config in configs.values():
            if isinstance(config, dict):
                alpha = config.get("lora_alpha", config.get("alpha", alpha))
                modules = config.get("target_modules")
                if isinstance(modules, list):
                    targets.update(str(item) for item in modules)
        if not targets:
            for tensor in lora_tensors:
                parts = tensor.name.split(".")
                if len(parts) > 2:
                    targets.add(parts[-3])
        return {
            "rank": ", ".join(str(item) for item in ranks[:8]) if ranks else "unknown",
            "alpha": alpha if alpha is not None else "unknown",
            "target_modules": sorted(targets)[:20],
            "down_matrices": len(down),
            "up_matrices": len(up),
            "adapter_parameter_count": sum(tensor.parameter_count for tensor in lora_tensors),
            "average_abs_mean": (
                sum(tensor.abs_mean or 0 for tensor in lora_tensors) / max(1, len(lora_tensors))
            ),
        }

    @staticmethod
    def _histogram(tensors: list[TensorStats]) -> dict[str, list[float]]:
        values = [tensor.abs_mean for tensor in tensors if tensor.abs_mean is not None]
        if not values:
            return {}
        buckets = [0.0] * 10
        high = max(values) or 1.0
        for value in values:
            index = min(9, int((value / high) * 10))
            buckets[index] += 1
        return {"abs_mean_bins": [round(high * index / 10, 6) for index in range(11)], "counts": buckets}