import logging
import os
import warnings
from io import BytesIO
from typing import Dict, List, Optional, Union

import numpy as np
import requests
from PIL import Image

# importing this without pytorch or others causes a warning
# https://github.com/huggingface/transformers/issues/27214
# suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1
from transformers import AutoFeatureExtractor, AutoTokenizer
from transformers.utils.logging import disable_progress_bar

from frigate.comms.inter_process import InterProcessRequestor
from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
from frigate.types import ModelStatusTypesEnum
from frigate.util.downloader import ModelDownloader
from frigate.util.model import ONNXModelRunner

warnings.filterwarnings(
    "ignore",
    category=FutureWarning,
    message="The class CLIPFeatureExtractor is deprecated",
)

# disables the progress bar for downloading tokenizers and feature extractors
disable_progress_bar()
logger = logging.getLogger(__name__)


class GenericONNXEmbedding:
    """Generic embedding function for ONNX models (text and vision)."""

    def __init__(
        self,
        model_name: str,
        model_file: str,
        download_urls: Dict[str, str],
        model_size: str,
        model_type: str,
        requestor: InterProcessRequestor,
        tokenizer_file: Optional[str] = None,
        device: str = "AUTO",
    ):
        self.model_name = model_name
        self.model_file = model_file
        self.tokenizer_file = tokenizer_file
        self.requestor = requestor
        self.download_urls = download_urls
        self.model_type = model_type  # 'text' or 'vision'
        self.model_size = model_size
        self.device = device
        self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
        self.tokenizer = None
        self.feature_extractor = None
        self.runner = None
        files_names = list(self.download_urls.keys()) + (
            [self.tokenizer_file] if self.tokenizer_file else []
        )

        if not all(
            os.path.exists(os.path.join(self.download_path, n)) for n in files_names
        ):
            logger.debug(f"starting model download for {self.model_name}")
            self.downloader = ModelDownloader(
                model_name=self.model_name,
                download_path=self.download_path,
                file_names=files_names,
                download_func=self._download_model,
            )
            self.downloader.ensure_model_files()
        else:
            self.downloader = None
            ModelDownloader.mark_files_state(
                self.requestor,
                self.model_name,
                files_names,
                ModelStatusTypesEnum.downloaded,
            )
            self._load_model_and_tokenizer()
            logger.debug(f"models are already downloaded for {self.model_name}")

    def _download_model(self, path: str):
        try:
            file_name = os.path.basename(path)
            if file_name in self.download_urls:
                ModelDownloader.download_from_url(self.download_urls[file_name], path)
            elif file_name == self.tokenizer_file and self.model_type == "text":
                if not os.path.exists(path + "/" + self.model_name):
                    logger.info(f"Downloading {self.model_name} tokenizer")
                tokenizer = AutoTokenizer.from_pretrained(
                    self.model_name,
                    trust_remote_code=True,
                    cache_dir=f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer",
                    clean_up_tokenization_spaces=True,
                )
                tokenizer.save_pretrained(path)

            self.downloader.requestor.send_data(
                UPDATE_MODEL_STATE,
                {
                    "model": f"{self.model_name}-{file_name}",
                    "state": ModelStatusTypesEnum.downloaded,
                },
            )
        except Exception:
            self.downloader.requestor.send_data(
                UPDATE_MODEL_STATE,
                {
                    "model": f"{self.model_name}-{file_name}",
                    "state": ModelStatusTypesEnum.error,
                },
            )

    def _load_model_and_tokenizer(self):
        if self.runner is None:
            if self.downloader:
                self.downloader.wait_for_download()
            if self.model_type == "text":
                self.tokenizer = self._load_tokenizer()
            else:
                self.feature_extractor = self._load_feature_extractor()
            self.runner = ONNXModelRunner(
                os.path.join(self.download_path, self.model_file),
                self.device,
                self.model_size,
            )

    def _load_tokenizer(self):
        tokenizer_path = os.path.join(f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer")
        return AutoTokenizer.from_pretrained(
            self.model_name,
            cache_dir=tokenizer_path,
            trust_remote_code=True,
            clean_up_tokenization_spaces=True,
        )

    def _load_feature_extractor(self):
        return AutoFeatureExtractor.from_pretrained(
            f"{MODEL_CACHE_DIR}/{self.model_name}",
        )

    def _process_image(self, image):
        if isinstance(image, str):
            if image.startswith("http"):
                response = requests.get(image)
                image = Image.open(BytesIO(response.content)).convert("RGB")

        return image

    def __call__(
        self, inputs: Union[List[str], List[Image.Image], List[str]]
    ) -> List[np.ndarray]:
        self._load_model_and_tokenizer()
        if self.runner is None or (
            self.tokenizer is None and self.feature_extractor is None
        ):
            logger.error(
                f"{self.model_name} model or tokenizer/feature extractor is not loaded."
            )
            return []

        if self.model_type == "text":
            max_length = max(len(self.tokenizer.encode(text)) for text in inputs)
            processed_inputs = [
                self.tokenizer(
                    text,
                    padding="max_length",
                    truncation=True,
                    max_length=max_length,
                    return_tensors="np",
                )
                for text in inputs
            ]
        else:
            processed_images = [self._process_image(img) for img in inputs]
            processed_inputs = [
                self.feature_extractor(images=image, return_tensors="np")
                for image in processed_images
            ]

        input_names = self.runner.get_input_names()
        onnx_inputs = {name: [] for name in input_names}
        input: dict[str, any]
        for input in processed_inputs:
            for key, value in input.items():
                if key in input_names:
                    onnx_inputs[key].append(value[0])

        for key in input_names:
            if onnx_inputs.get(key):
                onnx_inputs[key] = np.stack(onnx_inputs[key])
            else:
                logger.warning(f"Expected input '{key}' not found in onnx_inputs")

        embeddings = self.runner.run(onnx_inputs)[0]
        return [embedding for embedding in embeddings]