import json
import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union

import torch

from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
    resize_pos_embed, get_cast_dtype
from .coca_model import CoCa
from .loss import ClipLoss, DistillClipLoss, CoCaLoss
from .openai import load_openai_model
from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained,\
    list_pretrained_tags_by_model, download_pretrained_from_hf
from .transform import image_transform, AugmentationCfg
from .tokenizer import HFTokenizer, tokenize


HF_HUB_PREFIX = 'hf-hub:'
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs


def _natural_key(string_):
    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]


def _rescan_model_configs():
    global _MODEL_CONFIGS

    config_ext = ('.json',)
    config_files = []
    for config_path in _MODEL_CONFIG_PATHS:
        if config_path.is_file() and config_path.suffix in config_ext:
            config_files.append(config_path)
        elif config_path.is_dir():
            for ext in config_ext:
                config_files.extend(config_path.glob(f'*{ext}'))

    for cf in config_files:
        with open(cf, 'r') as f:
            model_cfg = json.load(f)
            if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
                _MODEL_CONFIGS[cf.stem] = model_cfg

    _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}


_rescan_model_configs()  # initial populate of model config registry


def list_models():
    """ enumerate available model architectures based on config files """
    return list(_MODEL_CONFIGS.keys())


def add_model_config(path):
    """ add model config path or file and update registry """
    if not isinstance(path, Path):
        path = Path(path)
    _MODEL_CONFIG_PATHS.append(path)
    _rescan_model_configs()


def get_model_config(model_name):
    if model_name in _MODEL_CONFIGS:
        return deepcopy(_MODEL_CONFIGS[model_name])
    else:
        return None


def get_tokenizer(model_name):
    if model_name.startswith(HF_HUB_PREFIX):
        tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])
    else:
        config = get_model_config(model_name)
        tokenizer = HFTokenizer(
            config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize
    return tokenizer


def load_state_dict(checkpoint_path: str, map_location='cpu'):
    checkpoint = torch.load(checkpoint_path, map_location=map_location)
    if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
        state_dict = checkpoint['state_dict']
    else:
        state_dict = checkpoint
    if next(iter(state_dict.items()))[0].startswith('module'):
        state_dict = {k[7:]: v for k, v in state_dict.items()}
    return state_dict


def load_checkpoint(model, checkpoint_path, strict=True):
    state_dict = load_state_dict(checkpoint_path)
    # detect old format and make compatible with new format
    if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
        state_dict = convert_to_custom_text_state_dict(state_dict)
    resize_pos_embed(state_dict, model)
    incompatible_keys = model.load_state_dict(state_dict, strict=strict)
    return incompatible_keys


def create_model(
        model_name: str,
        pretrained: Optional[str] = None,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_text: bool = False,
        force_patch_dropout: Optional[float] = None,
        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
        pretrained_image: bool = False,
        pretrained_hf: bool = True,
        cache_dir: Optional[str] = None,
        output_dict: Optional[bool] = None,
        require_pretrained: bool = False,
        logger: logging.Logger = logging,
):
    has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
    if has_hf_hub_prefix:
        model_id = model_name[len(HF_HUB_PREFIX):]
        checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
        config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)

        with open(config_path, 'r', encoding='utf-8') as f:
            config = json.load(f)
        pretrained_cfg = config['preprocess_cfg']
        model_cfg = config['model_cfg']
    else:
        model_name = model_name.replace('/', '-')  # for callers using old naming with / in ViT names
        checkpoint_path = None
        pretrained_cfg = {}
        model_cfg = None

    if isinstance(device, str):
        device = torch.device(device)

    if pretrained and pretrained.lower() == 'openai':
        logger.info(f'Loading pretrained {model_name} from OpenAI.')
        model = load_openai_model(
            model_name,
            precision=precision,
            device=device,
            cache_dir=cache_dir,
        )
    else:
        model_cfg = model_cfg or get_model_config(model_name)
        if model_cfg is not None:
            logger.info(f'Loaded {model_name} model config.')
        else:
            logger.error(f'Model config for {model_name} not found; available models {list_models()}.')
            raise RuntimeError(f'Model config for {model_name} not found.')

        if force_quick_gelu:
            # override for use of QuickGELU on non-OpenAI transformer models
            model_cfg["quick_gelu"] = True

        if force_patch_dropout is not None:
            # override the default patch dropout value
            model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout

        if force_image_size is not None:
            # override model config's image size
            model_cfg["vision_cfg"]["image_size"] = force_image_size

        is_timm_model = 'timm_model_name' in model_cfg.get('vision_cfg', {})
        if pretrained_image:
            if is_timm_model:
                # pretrained weight loading for timm models set via vision_cfg
                model_cfg['vision_cfg']['timm_model_pretrained'] = True
            else:
                assert False, 'pretrained image towers currently only supported for timm models'

        # cast_dtype set for fp16 and bf16 (manual mixed-precision), not set for 'amp' or 'pure' modes
        cast_dtype = get_cast_dtype(precision)
        is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
        custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model

        if custom_text:
            if is_hf_model:
                model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
            if "coca" in model_name:
                model = CoCa(**model_cfg, cast_dtype=cast_dtype)
            else:
                model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
        else:
            model = CLIP(**model_cfg, cast_dtype=cast_dtype)

        if precision in ("fp16", "bf16"):
            dtype = torch.float16 if 'fp16' in precision else torch.bfloat16
            # manual mixed precision that matches original OpenAI behaviour
            if is_timm_model:
                # FIXME this is a bit janky, create timm based model in low-precision and
                # then cast only LayerNormFp32 instances back to float32 so they don't break.
                # Why? The convert_weights_to_lp fn only works with native models.
                model.to(device=device, dtype=dtype)
                from .transformer import LayerNormFp32
                def _convert_ln(m):
                    if isinstance(m, LayerNormFp32):
                        m.weight.data = m.weight.data.to(torch.float32)
                        m.bias.data = m.bias.data.to(torch.float32)
                model.apply(_convert_ln)
            else:
                model.to(device=device)
                convert_weights_to_lp(model, dtype=dtype)
        elif precision in ("pure_fp16", "pure_bf16"):
            dtype = torch.float16 if 'fp16' in precision else torch.bfloat16
            model.to(device=device, dtype=dtype)
        else:
            model.to(device=device)

        pretrained_loaded = False
        if pretrained:
            checkpoint_path = ''
            pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
            if pretrained_cfg:
                checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
            elif os.path.exists(pretrained):
                checkpoint_path = pretrained

            if checkpoint_path:
                logger.info(f'Loading pretrained {model_name} weights ({pretrained}).')
                load_checkpoint(model, checkpoint_path)
            else:
                error_str = (
                    f'Pretrained weights ({pretrained}) not found for model {model_name}.'
                    f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
                logger.warning(error_str)
                raise RuntimeError(error_str)
            pretrained_loaded = True
        elif has_hf_hub_prefix:
            logger.info(f'Loading pretrained {model_name} weights ({pretrained}).')
            load_checkpoint(model, checkpoint_path)
            pretrained_loaded = True

        if require_pretrained and not pretrained_loaded:
            # callers of create_model_from_pretrained always expect pretrained weights
            raise RuntimeError(
                f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.')

        # set image / mean metadata from pretrained_cfg if available, or use default
        model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
        model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD

    if output_dict and hasattr(model, "output_dict"):
        model.output_dict = True

    if jit:
        model = torch.jit.script(model)

    return model


def create_loss(args):
    if args.distill:
        return DistillClipLoss(
            local_loss=args.local_loss,
            gather_with_grad=args.gather_with_grad,
            cache_labels=True,
            rank=args.rank,
            world_size=args.world_size,
            use_horovod=args.horovod,
        )
    elif "coca" in args.model.lower():
        return CoCaLoss(
            caption_loss_weight=args.coca_caption_loss_weight,
            clip_loss_weight=args.coca_contrastive_loss_weight,
            local_loss=args.local_loss,
            gather_with_grad=args.gather_with_grad,
            cache_labels=True,
            rank=args.rank,
            world_size=args.world_size,
            use_horovod=args.horovod,
        )
    return ClipLoss(
        local_loss=args.local_loss,
        gather_with_grad=args.gather_with_grad,
        cache_labels=True,
        rank=args.rank,
        world_size=args.world_size,
        use_horovod=args.horovod,
    )


def create_model_and_transforms(
        model_name: str,
        pretrained: Optional[str] = None,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_text: bool = False,
        force_patch_dropout: Optional[float] = None,
        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
        pretrained_image: bool = False,
        pretrained_hf: bool = True,
        image_mean: Optional[Tuple[float, ...]] = None,
        image_std: Optional[Tuple[float, ...]] = None,
        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
        cache_dir: Optional[str] = None,
        output_dict: Optional[bool] = None,
        logger: logging.Logger = logging,
):
    model = create_model(
        model_name,
        pretrained,
        precision=precision,
        device=device,
        jit=jit,
        force_quick_gelu=force_quick_gelu,
        force_custom_text=force_custom_text,
        force_patch_dropout=force_patch_dropout,
        force_image_size=force_image_size,
        pretrained_image=pretrained_image,
        pretrained_hf=pretrained_hf,
        cache_dir=cache_dir,
        output_dict=output_dict,
        logger=logger,
    )

    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
    image_std = image_std or getattr(model.visual, 'image_std', None)
    preprocess_train = image_transform(
        model.visual.image_size,
        is_train=True,
        mean=image_mean,
        std=image_std,
        aug_cfg=aug_cfg,
    )
    preprocess_val = image_transform(
        model.visual.image_size,
        is_train=False,
        mean=image_mean,
        std=image_std,
    )

    return model, preprocess_train, preprocess_val


def create_model_from_pretrained(
        model_name: str,
        pretrained: Optional[str] = None,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_text: bool = False,
        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
        return_transform: bool = True,
        image_mean: Optional[Tuple[float, ...]] = None,
        image_std: Optional[Tuple[float, ...]] = None,
        cache_dir: Optional[str] = None,
        logger: logging.Logger = logging,
):
    model = create_model(
        model_name,
        pretrained,
        precision=precision,
        device=device,
        jit=jit,
        force_quick_gelu=force_quick_gelu,
        force_custom_text=force_custom_text,
        force_image_size=force_image_size,
        cache_dir=cache_dir,
        require_pretrained=True,
        logger=logger,
    )

    if not return_transform:
        return model

    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
    image_std = image_std or getattr(model.visual, 'image_std', None)
    preprocess = image_transform(
        model.visual.image_size,
        is_train=False,
        mean=image_mean,
        std=image_std,
    )

    return model, preprocess