import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging

import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm

from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer

try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC


if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
    warnings.warn("PyTorch version 1.7.1 or higher is recommended")


__all__ = ["available_models", "load", "tokenize"]
# _tokenizer = _Tokenizer()

_MODELS = {
    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
}


def _download(url: str, root: str):
    os.makedirs(root, exist_ok=True)
    filename = os.path.basename(url)

    expected_sha256 = url.split("/")[-2]
    download_target = os.path.join(root, filename)

    if os.path.exists(download_target) and not os.path.isfile(download_target):
        raise RuntimeError(f"{download_target} exists and is not a regular file")

    if os.path.isfile(download_target):
        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
            return download_target
        else:
            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")

    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
            while True:
                buffer = source.read(8192)
                if not buffer:
                    break

                output.write(buffer)
                loop.update(len(buffer))

    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
        raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")

    return download_target


def _convert_image_to_rgb(image):
    return image.convert("RGB")


def _transform(n_px):
    return Compose([
        Resize(n_px, interpolation=BICUBIC),
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
    ])


def available_models() -> List[str]:
    """Returns the names of available CLIP models"""
    return list(_MODELS.keys())


def load(
    name: str, 
    device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", 
    jit: bool = False, download_root: str = None,
    # evl
    n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
    mlp_dropout=[0.0, 0.0, 0.0, 0.0], cls_dropout=0.5, t_size=8, spatial_size=14,
    use_t_conv=True, use_image_attnmap=True, use_t_pos_embed=True, dropout=0.0, no_pretrain=False, init_zero=True,
    use_checkpoint=False, checkpoint_num=[0, 0, 0],
    ):
    """Load a CLIP model

    Parameters
    ----------
    name : str
        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict

    device : Union[str, torch.device]
        The device to put the loaded model

    jit : bool
        Whether to load the optimized JIT model or more hackable non-JIT model (default).

    download_root: str
        path to download the model files; by default, it uses "~/.cache/clip"

    Returns
    -------
    model : torch.nn.Module
        The CLIP model

    preprocess : Callable[[PIL.Image], torch.Tensor]
        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
    """
    if name in _MODELS:
        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
    elif os.path.isfile(name):
        model_path = name
    else:
        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
    
    with open(model_path, 'rb') as opened_file:
        try:
            # loading JIT archive
            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
            state_dict = None
        except RuntimeError:
            # loading saved state dict
            if jit:
                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
                jit = False
            state_dict = torch.load(model_path, map_location="cpu")

    if not jit:
        model = build_model(
            state_dict or model.state_dict(),
            n_layers=n_layers, n_dim=n_dim, n_head=n_head, mlp_factor=mlp_factor, drop_path_rate=drop_path_rate,
            mlp_dropout=mlp_dropout, cls_dropout=cls_dropout, t_size=t_size, spatial_size=spatial_size,
            use_t_conv=use_t_conv, use_image_attnmap=use_image_attnmap, use_t_pos_embed=use_t_pos_embed, no_pretrain=no_pretrain,
            init_zero=init_zero, use_checkpoint=use_checkpoint, checkpoint_num=checkpoint_num,
        ).to(device)
        if str(device) == "cpu":
            model.float()
        return model, _transform(model.visual.input_resolution)

    # patch the device names
    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]

    def patch_device(module):
        try:
            graphs = [module.graph] if hasattr(module, "graph") else []
        except RuntimeError:
            graphs = []

        if hasattr(module, "forward1"):
            graphs.append(module.forward1.graph)

        for graph in graphs:
            for node in graph.findAllNodes("prim::Constant"):
                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
                    node.copyAttributes(device_node)

    model.apply(patch_device)
    patch_device(model.encode_image)
    patch_device(model.encode_text)

    # patch dtype to float32 on CPU
    if str(device) == "cpu":
        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
        float_node = float_input.node()

        def patch_float(module):
            try:
                graphs = [module.graph] if hasattr(module, "graph") else []
            except RuntimeError:
                graphs = []

            if hasattr(module, "forward1"):
                graphs.append(module.forward1.graph)

            for graph in graphs:
                for node in graph.findAllNodes("aten::to"):
                    inputs = list(node.inputs())
                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
                        if inputs[i].node()["value"] == 5:
                            inputs[i].node().copyAttributes(float_node)

        model.apply(patch_float)
        patch_float(model.encode_image)
        patch_float(model.encode_text)

        model.float()

    return model, _transform(model.input_resolution.item())


def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False, return_special_tokens_mask: bool = False) -> Union[torch.IntTensor, torch.LongTensor, torch.BoolTensor]:
    """
    Returns the tokenized representation of given input string(s)

    Parameters
    ----------
    texts : Union[str, List[str]]
        An input string or a list of input strings to tokenize

    context_length : int
        The context length to use; all CLIP models use 77 as the context length

    truncate: bool
        Whether to truncate the text in case its encoding is longer than the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder["<|startoftext|>"]
    eot_token = _tokenizer.encoder["<|endoftext|>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
    if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
    else:
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
    
    special_tokens_mask = torch.zeros(len(all_tokens), context_length, dtype=torch.bool)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
        result[i, :len(tokens)] = torch.tensor(tokens)
        special_tokens_mask[i, len(tokens):] = 1
    
    if return_special_tokens_mask:
        return result, special_tokens_mask

    return result
