# 🧠 Geniusrise
# Copyright (C) 2023  geniusrise.ai
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#  http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import glob
import json
import os
import sqlite3
import uuid
import xml.etree.ElementTree as ET
from typing import Any, Dict, List, Optional

import pandas as pd
import torch
import yaml  # type: ignore
from datasets import Dataset, load_from_disk
from geniusrise import BatchInput, BatchOutput, State
from pyarrow import feather
from pyarrow import parquet as pq

from geniusrise_text.base import TextBulk


class TextClassificationBulk(TextBulk):
    r"""
    TextClassificationBulk is designed to handle bulk text classification tasks using Hugging Face models efficiently and
    effectively. It allows for processing large datasets, utilizing state-of-the-art machine learning models to provide
    accurate classification of text data into predefined labels.

    Args:
        input (BatchInput): Configuration and data inputs for the batch process.
        output (BatchOutput): Configurations for output data handling.
        state (State): State management for the classification task.
        **kwargs: Arbitrary keyword arguments for extended configurations.

    Example CLI Usage:
    ```bash
    genius TextClassificationBulk rise \
        batch \
            --input_folder ./input \
        batch \
            --output_folder ./output \
        none \
        --id cardiffnlp/twitter-roberta-base-hate-multiclass-latest-lol \
        classify \
            --args \
                model_name="cardiffnlp/twitter-roberta-base-hate-multiclass-latest" \
                model_class="AutoModelForSequenceClassification" \
                tokenizer_class="AutoTokenizer" \
                use_cuda=True \
                precision="bfloat16" \
                quantization=0 \
                device_map="auto" \
                max_memory=None \
                torchscript=False
    ```
    """

    def __init__(self, input: BatchInput, output: BatchOutput, state: State, **kwargs) -> None:
        """
        Initializes the TextClassificationBulk class with input, output, and state configurations.

        Args:
            input (BatchInput): Configuration for the input data.
            output (BatchOutput): Configuration for the output data.
            state (State): State management for the classification task.
            **kwargs: Additional keyword arguments for extended functionality.
        """
        super().__init__(input, output, state, **kwargs)

    def load_dataset(self, dataset_path: str, max_length: int = 512, **kwargs) -> Optional[Dataset]:
        r"""
        Load a classification dataset from a directory.

        Args:
            dataset_path (str): The path to the dataset directory.
            max_length (int, optional): The maximum length for tokenization. Defaults to 512.

        Returns:
            Dataset: The loaded dataset.

        Raises:
            Exception: If there was an error loading the dataset.

        ## Supported Data Formats and Structures:

        ### JSONL
        Each line is a JSON object representing an example.
        ```json
        {"text": "The text content"}
        ```

        ### CSV
        Should contain 'text' columns.
        ```csv
        text
        "The text content"
        ```

        ### Parquet
        Should contain 'text' columns.

        ### JSON
        An array of dictionaries with 'text' keys.
        ```json
        [{"text": "The text content"}]
        ```

        ### XML
        Each 'record' element should contain 'text' child elements.
        ```xml
        <record>
            <text>The text content</text>
        </record>
        ```

        ### YAML
        Each document should be a dictionary with 'text' keys.
        ```yaml
        - text: "The text content"
        ```

        ### TSV
        Should contain 'text' columns separated by tabs.

        ### Excel (.xls, .xlsx)
        Should contain 'text' columns.

        ### SQLite (.db)
        Should contain a table with 'text' columns.

        ### Feather
        Should contain 'text' columns.
        """
        self.max_length = max_length

        self.label_to_id = self.model.config.label2id if self.model and self.model.config.label2id else {}  # type: ignore

        try:
            self.log.info(f"Loading dataset from {dataset_path}")
            if os.path.isfile(os.path.join(dataset_path, "dataset_info.json")):
                # Load dataset saved by Hugging Face datasets library
                return load_from_disk(dataset_path)
            else:
                data = []
                for filename in glob.glob(f"{dataset_path}/**/*", recursive=True):
                    filepath = os.path.join(dataset_path, filename)
                    if filename.endswith(".jsonl"):
                        with open(filepath, "r") as f:
                            for line in f:
                                example = json.loads(line)
                                data.append(example)

                    elif filename.endswith(".csv"):
                        df = pd.read_csv(filepath)
                        data.extend(df.to_dict("records"))

                    elif filename.endswith(".parquet"):
                        df = pq.read_table(filepath).to_pandas()
                        data.extend(df.to_dict("records"))

                    elif filename.endswith(".json"):
                        with open(filepath, "r") as f:
                            json_data = json.load(f)
                            data.extend(json_data)

                    elif filename.endswith(".xml"):
                        tree = ET.parse(filepath)
                        root = tree.getroot()
                        for record in root.findall("record"):
                            text = record.find("text").text  # type: ignore
                            data.append({"text": text})

                    elif filename.endswith(".yaml") or filename.endswith(".yml"):
                        with open(filepath, "r") as f:
                            yaml_data = yaml.safe_load(f)
                            data.extend(yaml_data)

                    elif filename.endswith(".tsv"):
                        df = pd.read_csv(filepath, sep="\t")
                        data.extend(df.to_dict("records"))

                    elif filename.endswith((".xls", ".xlsx")):
                        df = pd.read_excel(filepath)
                        data.extend(df.to_dict("records"))

                    elif filename.endswith(".db"):
                        conn = sqlite3.connect(filepath)
                        query = "SELECT text FROM dataset_table;"
                        df = pd.read_sql_query(query, conn)
                        data.extend(df.to_dict("records"))

                    elif filename.endswith(".feather"):
                        df = feather.read_feather(filepath)
                        data.extend(df.to_dict("records"))

                if hasattr(self, "map_data") and self.map_data:
                    fn = eval(self.map_data)  # type: ignore
                    data = [fn(d) for d in data]
                else:
                    data = data

                return Dataset.from_pandas(pd.DataFrame(data))
        except Exception as e:
            self.log.exception(f"Error occurred when loading dataset from {dataset_path}. Error: {e}")
            raise

    def classify(
        self,
        model_name: str,
        model_class: str = "AutoModelForSequenceClassification",
        tokenizer_class: str = "AutoTokenizer",
        use_cuda: bool = False,
        precision: str = "float",
        quantization: int = 0,
        device_map: str | Dict | None = "auto",
        max_memory={0: "24GB"},
        torchscript: bool = False,
        compile: bool = False,
        awq_enabled: bool = False,
        flash_attention: bool = False,
        batch_size: int = 32,
        notification_email: Optional[str] = None,
        **kwargs: Any,
    ) -> None:
        """
        Perform bulk classification using the specified model and tokenizer. This method handles the entire classification
        process including loading the model, processing input data, predicting classifications, and saving the results.

        Args:
            model_name (str): Name or path of the model.
            model_class (str): Class name of the model (default "AutoModelForSequenceClassification").
            tokenizer_class (str): Class name of the tokenizer (default "AutoTokenizer").
            use_cuda (bool): Whether to use CUDA for model inference (default False).
            precision (str): Precision for model computation (default "float").
            quantization (int): Level of quantization for optimizing model size and speed (default 0).
            device_map (str | Dict | None): Specific device to use for computation (default "auto").
            max_memory (Dict): Maximum memory configuration for devices.
            torchscript (bool, optional): Whether to use a TorchScript-optimized version of the pre-trained language model. Defaults to False.
            compile (bool, optional): Whether to compile the model before fine-tuning. Defaults to True.
            awq_enabled (bool): Whether to enable AWQ optimization (default False).
            flash_attention (bool): Whether to use flash attention optimization (default False).
            batch_size (int): Number of classifications to process simultaneously (default 32).
            **kwargs: Arbitrary keyword arguments for model and generation configurations.
        """
        if ":" in model_name:
            model_revision = model_name.split(":")[1]
            tokenizer_revision = model_name.split(":")[1]
            model_name = model_name.split(":")[0]
            tokenizer_name = model_name
        else:
            model_revision = None
            tokenizer_revision = None
            tokenizer_name = model_name

        self.model_name = model_name
        self.tokenizer_name = tokenizer_name
        self.model_revision = model_revision
        self.tokenizer_revision = tokenizer_revision
        self.model_class = model_class
        self.tokenizer_class = tokenizer_class
        self.use_cuda = use_cuda
        self.precision = precision
        self.quantization = quantization
        self.device_map = device_map
        self.max_memory = max_memory
        self.torchscript = torchscript
        self.awq_enabled = awq_enabled
        self.flash_attention = flash_attention
        self.batch_size = batch_size
        self.notification_email = notification_email
        self.compile = compile

        model_args = {k.replace("model_", ""): v for k, v in kwargs.items() if "model_" in k}
        self.model_args = model_args

        generation_args = {k.replace("generation_", ""): v for k, v in kwargs.items() if "generation_" in k}
        self.generation_args = generation_args

        self.model, self.tokenizer = self.load_models(
            model_name=self.model_name,
            tokenizer_name=self.tokenizer_name,
            model_revision=self.model_revision,
            tokenizer_revision=self.tokenizer_revision,
            model_class=self.model_class,
            tokenizer_class=self.tokenizer_class,
            use_cuda=self.use_cuda,
            precision=self.precision,
            quantization=self.quantization,
            device_map=self.device_map,
            max_memory=self.max_memory,
            torchscript=self.torchscript,
            awq_enabled=self.awq_enabled,
            flash_attention=self.flash_attention,
            compile=self.compile,
            **self.model_args,
        )

        dataset_path = self.input.input_folder
        output_path = self.output.output_folder

        # Load dataset
        _dataset = self.load_dataset(dataset_path)
        if _dataset is None:
            self.log.error("Failed to load dataset.")
            return
        dataset = _dataset["text"]

        # Process data in batches
        for i in range(0, len(dataset), batch_size):
            batch = dataset[i : i + batch_size]
            inputs = self.tokenizer(batch, return_tensors="pt", padding=True, truncation=True)

            if next(self.model.parameters()).is_cuda:
                inputs = {k: v.cuda() for k, v in inputs.items()}

            predictions = self.model(**inputs)
            predictions = predictions[0] if isinstance(predictions, tuple) else predictions.logits
            predictions = torch.argmax(predictions, dim=-1).cpu().numpy()

            self._save_predictions(predictions, batch, output_path, i)
        self.done()

    def _save_predictions(
        self,
        predictions: torch.Tensor,
        input_batch: List[str],
        output_path: str,
        batch_idx: int,
    ) -> None:
        """
        Saves the classification predictions to a specified output path. This method is called internally by the classify method
        to persist the classification results.

        Args:
            predictions (torch.Tensor): Tensor of label indices predicted by the model.
            input_batch (List[str]): List of original texts that were classified.
            output_path (str): Path to save the classification results.
            batch_idx (int): Index of the current batch (for naming files).
        """
        id_to_label = dict(enumerate(self.model.config.id2label.values()))  # type: ignore
        label_predictions = [id_to_label[label_id] for label_id in predictions.tolist()]

        # Prepare data for saving
        data_to_save = [
            {"input": input_text, "prediction": label} for input_text, label in zip(input_batch, label_predictions)
        ]
        with open(
            os.path.join(output_path, f"predictions-{batch_idx}-{str(uuid.uuid4())}.json"),
            "w",
        ) as f:
            json.dump(data_to_save, f)
