import json
import logging
import os
import re
import shutil
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
from accelerate import Accelerator, skip_first_batches
from accelerate.logging import get_logger
from datasets import DatasetDict, load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
)


logger = get_logger(__name__, log_level="INFO")


@dataclass
class ModelArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    model_name_or_path: str = field(
        metadata={"help": "The name of the model to use (via the transformers library) for the prompt annotation."},
    )
    per_device_eval_batch_size: int = field(
        metadata={"help": "The per-device batch size to use for inference."},
    )
    model_variant: str = field(
        default=None,
        metadata={"help": "If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. "},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
    )
    torch_dtype: Optional[str] = field(
        default="float16",
        metadata={
            "help": (
                "Floating-point format in which the model weights should be initialized"
                " and the computations run. Choose one of `[float32, float16, bfloat16]`."
            )
        },
    )
    attn_implementation: Optional[str] = field(
        default="sdpa",
        metadata={"help": "Which attn type to use: ['eager', 'sdpa', 'flash_attention_2']"},
    )
    load_in_8bit: Optional[bool] = field(
        default=False, metadata={"help": "Whether to use 8-bit precision for inference."}
    )
    load_in_4bit: Optional[bool] = field(
        default=False, metadata={"help": "Whether to use 4-bit precision for inference."}
    )
    bnb_4bit_quant_type: Optional[str] = field(
        default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"}
    )
    use_bnb_nested_quant: Optional[bool] = field(default=False, metadata={"help": "use nested quantization"})
    trust_remote_code: Optional[bool] = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )
    use_fast_tokenizer: Optional[bool] = field(
        default=True, metadata={"help": "Use fast tokenizer for encoding/decoding input ids"}
    )
    token: Optional[bool] = field(
        default=True,
        metadata={
            "help": "Whether or not to use an authentication token when loading/uploading from the Hugging Face Hub"
        },
    )
    do_sample: Optional[bool] = field(default=True, metadata={"help": "Whether to use sampling mode for generation"})
    temperature: Optional[float] = field(default=0.6, metadata={"help": "Temperature for sampling-based generation"})
    max_new_tokens: Optional[int] = field(
        default=256, metadata={"help": "Maximum number of new tokens during generation"}
    )
    torch_compile: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Whether to compile the forward pass (not sampling) in generate. Only compatible with Gemma and LlaMA."
        },
    )


@dataclass
class DataArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    output_dir: str = field(
        metadata={
            "help": "Where to save the processed dataset to disk. If unspecified, uses a 'pretty' version of the "
            "original dataset name. E.g. 'facebook/voxpopuli' will be saved under 'voxpopuli'."
        },
    )
    dataset_name: str = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)"},
    )
    dataset_config_name: Optional[str] = field(
        default=None,
        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
    )
    dataset_split_name: Optional[str] = field(
        default=None,
        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
    )
    dataset_cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Path to cache directory for saving and loading datasets"},
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={"help": "Maximum number of samples for generation - use for debugging purposes."},
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets"},
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    dataloader_num_workers: Optional[int] = field(
        default=0,
        metadata={"help": "The number of processes to use for the dataloader."},
    )
    push_to_hub: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether or not to push the processed dataset to the Hub."},
    )
    hub_dataset_id: Optional[str] = field(
        default=None,
        metadata={"help": "Repository namespace if pushing to the Hugging Face Hub."},
    )
    overwrite_output_dir: Optional[bool] = field(
        default=False,
        metadata={"help": "Overwrite the content of the output directory each time the script is run."},
    )
    save_steps: Optional[int] = field(
        default=500,
        metadata={"help": "Save the generated prompts every save_steps."},
    )
    save_total_limit: Optional[int] = field(
        default=1, metadata={"help": ("If a value is passed, will limit the total number of saved checkpoints")}
    )

    def __post_init__(self):
        if self.push_to_hub and self.hub_dataset_id is None:
            raise ValueError("You must specify the `hub_dataset_id` when setting `--push_to_hub=True`")


def get_quantization_config(model_args: ModelArguments) -> Union[BitsAndBytesConfig, None]:
    if model_args.load_in_4bit:
        compute_dtype = torch.float16
        if model_args.torch_dtype not in {"auto", None}:
            compute_dtype = getattr(torch, model_args.torch_dtype)

        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=compute_dtype,
            bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
            bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
        )
    elif model_args.load_in_8bit:
        quantization_config = BitsAndBytesConfig(
            load_in_8bit=True,
        )
    else:
        quantization_config = None

    return quantization_config


def get_current_device() -> int:
    """Get the current device. For GPU we return the local process index to enable multiple GPU training."""
    return Accelerator().local_process_index if torch.cuda.is_available() else "cpu"


def get_kbit_device_map() -> Union[Dict[str, int], None]:
    """Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`"""
    return {"": get_current_device()} if torch.cuda.is_available() else None


CHECKPOINT_PREFIX = "checkpoint"
_RE_CHECKPOINT = re.compile(r"^checkpoint-(\d+).json$")


def save_checkpoint(output_dir, all_generated_ids, step):
    checkpoint_path = f"{CHECKPOINT_PREFIX}-{step}.json"
    output_path = os.path.join(output_dir, checkpoint_path)
    all_generated_ids = [ids.tolist() for ids in all_generated_ids]
    with open(output_path, "w") as file:
        json.dump(all_generated_ids, file)


def load_checkpoint(checkpoint_path):
    with open(checkpoint_path, "r") as file:
        all_generated_ids = json.load(file)
    all_generated_ids = [np.array(lst) for lst in all_generated_ids]
    return all_generated_ids


def sorted_checkpoints(output_dir=None) -> List[str]:
    """Helper function to sort saved checkpoints from oldest to newest."""
    ordering_and_checkpoint_path = []

    glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{CHECKPOINT_PREFIX}-*")]

    for path in glob_checkpoints:
        regex_match = re.match(f".*{CHECKPOINT_PREFIX}-([0-9]+)", path)
        if regex_match is not None and regex_match.groups() is not None:
            ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))

    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
    return checkpoints_sorted


def rotate_checkpoints(save_total_limit=None, output_dir=None) -> None:
    """Helper function to delete old checkpoints."""
    if save_total_limit is None or save_total_limit <= 0:
        return
    # Check if we should delete older checkpoint(s)
    checkpoints_sorted = sorted_checkpoints(output_dir=output_dir)
    if len(checkpoints_sorted) <= save_total_limit:
        return

    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
    for checkpoint in checkpoints_to_be_deleted:
        logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
        os.remove(checkpoint)


def get_last_checkpoint(folder) -> Tuple[List, int]:
    if not os.path.exists(folder) or not os.path.isdir(folder):
        os.makedirs(folder, exist_ok=True)
        return [], 0
    content = os.listdir(folder)
    checkpoints = [path for path in content if _RE_CHECKPOINT.search(path) is not None]
    if len(checkpoints) == 0:
        return [], 0
    last_checkpoint = os.path.join(folder, max(checkpoints, key=lambda x: int(_RE_CHECKPOINT.search(x).groups()[0])))
    # Find num steps saved state string pattern
    pattern = r"checkpoint-(\d+).json"
    match = re.search(pattern, last_checkpoint)
    cur_step = int(match.group(1))
    # load corresponding generated ids
    all_generated_ids = load_checkpoint(last_checkpoint)
    return all_generated_ids, cur_step


@dataclass
class DataCollatorWithPadding:
    """
    Data collator that will dynamically pad the inputs received to the longest sequence in the batch.
    """

    tokenizer: Any

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        # split inputs and labels since they have to be of different lengths and need
        # different padding methods
        input_ids = {"input_ids": [feature["input_ids"] for feature in features]}
        batch = self.tokenizer.pad(input_ids, return_tensors="pt", padding="longest", return_attention_mask=True)
        return batch

id_to_name = {
    "ex01": "Jerry",
    "ex02": "Elisabeth",
    "ex03": "Thomas",
    "ex04": "Talia"
}

PROMPT = """You will be given a name and an enunciation style related to an audio sample of someone speaking.
1. The name will be one of: Jerry, Elisabeth, Thomas, Talia.
2. The enunciation style will be one of: 'enunciated', 'happy', 'confused', 'default' (meaning no particular emotion conveyed), 'laughing', 'sad', 'whisper', 'emphasis'.
3. The pace of the speaker's delivery (e.g., very slowly, quite slowly, slightly slowly, moderate speed, slightly fast, quite fast, very fast)

Your task is to create a simple text description using these keywords that accurately describes the audio sample. Ensure that the generated description is grammatically correct, easy to understand, and most importantly, concise.

For example, given the following keywords: 'Talia', 'happy', 'quite slowly', a valid description would be: 'Talia speaks happily and quite slowly with high quality.'. Another valid description would be: 'Talia delivers her words happily and quite slowly with high quality audio.'. Another example, given the following keywords: 'Jerry', 'emphasis', 'slightly slowly': 'Jerry speaks with emphasis on certain words and slightly slowly with high quality audio.'

Each description is appended with 'with high quality'.

You are free to change the order of the information, and replace synonymous terms. Give one description and nothing else. No alternatives or repeating the task. Remember to prioritise conciseness and simplicity.

For the information: '[speaker_id]', '[style]', '[speaking_rate]' the corresponding description is:"""


def main():
    # 1. Parse input arguments
    parser = HfArgumentParser((ModelArguments, DataArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args = parser.parse_args_into_dataclasses()

    # 2. Setup logging
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    
    accelerator = Accelerator()

    if data_args.overwrite_output_dir and os.path.exists(data_args.output_dir) and os.path.isdir(data_args.output_dir):
        logger.info("Cleaning output dir from previous run...")
        shutil.rmtree(data_args.output_dir)

    # 3. Load annotated dataset
    logger.info("*** Load annotated dataset ***")
    if data_args.dataset_split_name is not None:
        raw_datasets = DatasetDict()
        data_splits = data_args.dataset_split_name.split("+")
        # load on a split-wise basis
        for split in data_splits:
            with accelerator.local_main_process_first():
                raw_datasets[split] = load_dataset(
                    data_args.dataset_name,
                    data_args.dataset_config_name,
                    split=split,
                    cache_dir=model_args.cache_dir,
                    token=model_args.token,
                    num_proc=data_args.preprocessing_num_workers,
                )
    else:
        with accelerator.local_main_process_first():
            # load all splits for annotation
            raw_datasets = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                num_proc=data_args.preprocessing_num_workers,
            )

    raw_datasets_features = set(raw_datasets[next(iter(raw_datasets))].features.keys())

    if data_args.max_eval_samples is not None:
        for split in raw_datasets:
            raw_datasets[split] = raw_datasets[split].select(range(data_args.max_eval_samples))

    # TODO(SG): add accent
    EXPECTED_COLUMNS = {"speaker_id", "style", "speaking_rate"}
    if not EXPECTED_COLUMNS.issubset(raw_datasets_features):
        missing_columns = EXPECTED_COLUMNS - raw_datasets_features
        raise ValueError(
            f"Missing columns {missing_columns} from the dataset features. Got dataset features {raw_datasets_features}"
        )

    # 4. Load pre-trained model
    logger.info("*** Load pretrained model ***")
    torch_dtype = (
        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
    )
    quantization_config = get_quantization_config(model_args)

    model = AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        revision=model_args.model_revision,
        variant=model_args.model_variant,
        trust_remote_code=model_args.trust_remote_code,
        attn_implementation=model_args.attn_implementation,
        torch_dtype=torch_dtype,
        device_map=get_kbit_device_map() if quantization_config is not None else None,
        quantization_config=quantization_config,
        low_cpu_mem_usage=True,
        token=model_args.token,
    ).eval()

    if model_args.torch_compile:
        # torch compile only compatible with gemma and llama
        if not callable(getattr(model, "_setup_cache", None)):
            raise ValueError(
                f"Static k/v cache is not compatible with the model {model.__class__.__name__}. Set `--torch_compile=False"
                "for dynamic k/v cache"
            )
        model.generation_config.cache_implementation = "static"
        # compile the forward pass (but not the top-{p,k} sampling)
        model = torch.compile(model, mode="reduce-overhead", fullgraph=True)

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        revision=model_args.model_revision,
        trust_remote_code=model_args.trust_remote_code,
        use_fast=model_args.use_fast_tokenizer,
        padding_side="left",
    )
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token_id = tokenizer.bos_token_id
        model.generation_config.pad_token_id = model.generation_config.eos_token_id


    def prepare_dataset(sample):
        sample_prompt = PROMPT
        sample["speaker_id"] = id_to_name[sample["speaker_id"]]
        for key in EXPECTED_COLUMNS:
            sample_prompt = sample_prompt.replace(f"[{key}]", sample[key])
        sample_prompt = [{"role": "user", "content": sample_prompt}]
        token_ids = tokenizer.apply_chat_template(sample_prompt)
        sample["input_ids"] = token_ids
        return sample

    with accelerator.local_main_process_first():
        vectorized_datasets = raw_datasets.map(
            prepare_dataset, num_proc=data_args.preprocessing_num_workers, desc="Preparing prompts"
        )

    # Prepare everything with our `accelerator`
    model = accelerator.prepare(model)
    data_collator = DataCollatorWithPadding(tokenizer)

    def generate_step(batch):
        output_ids = accelerator.unwrap_model(model).generate(
            batch["input_ids"],
            attention_mask=batch["attention_mask"],
            do_sample=model_args.do_sample,
            temperature=model_args.temperature,
            max_new_tokens=model_args.max_new_tokens,
        )
        output_ids = accelerator.pad_across_processes(output_ids, dim=1, pad_index=tokenizer.pad_token_id)
        return output_ids

    def postprocess_dataset(sample):
        prompt_text = tokenizer.decode(sample["input_ids"], skip_special_tokens=True)
        generated_text = tokenizer.decode(sample["generated_ids"], skip_special_tokens=True)
        sample["text_description"] = generated_text[len(prompt_text) :]
        return sample

    for split in vectorized_datasets:
        data_loader = DataLoader(
            vectorized_datasets[split],
            batch_size=model_args.per_device_eval_batch_size,
            collate_fn=data_collator,
            num_workers=data_args.dataloader_num_workers,
            pin_memory=True,
        )
        data_loader = accelerator.prepare(data_loader)
        total_inference_steps = len(data_loader)
        progress_bar = tqdm(
            range(total_inference_steps), desc=" ... ", position=0, disable=not accelerator.is_local_main_process
        )

        split_output_dir = os.path.join(data_args.output_dir, split)
        all_generated_ids, cur_step = get_last_checkpoint(split_output_dir)

        if cur_step > 0:
            logger.info(f"Resuming {split} from step {cur_step}")
            # efficiently skip the first n batches
            data_loader = skip_first_batches(data_loader, cur_step)
            progress_bar.update(cur_step)

        while cur_step < total_inference_steps:
            for batch in data_loader:
                generated_ids = generate_step(batch)
                generated_ids = accelerator.gather_for_metrics(generated_ids)
                all_generated_ids.extend(generated_ids.cpu().numpy())

                cur_step += 1
                progress_bar.update(1)

                if (cur_step % data_args.save_steps == 0) or (cur_step == total_inference_steps):
                    save_checkpoint(split_output_dir, all_generated_ids, cur_step)
                    rotate_checkpoints(data_args.save_total_limit, output_dir=split_output_dir)

        vectorized_datasets[split] = vectorized_datasets[split].add_column("generated_ids", all_generated_ids)

        if accelerator.is_main_process:
            vectorized_datasets[split] = vectorized_datasets[split].map(
                postprocess_dataset,
                num_proc=data_args.preprocessing_num_workers,
                desc="Postprocessing dataset",
                remove_columns=["input_ids", "generated_ids"],
            )
        accelerator.wait_for_everyone()

    if accelerator.is_main_process:
        vectorized_datasets.save_to_disk(data_args.output_dir)
        if data_args.push_to_hub:
            vectorized_datasets.push_to_hub(
                data_args.hub_dataset_id,
                config_name=data_args.dataset_config_name if data_args.dataset_config_name is not None else "default",
                token=model_args.token,
            )
    accelerator.wait_for_everyone()
    accelerator.end_training()


if __name__ == "__main__":
    main()