from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
from transformers import pipeline, AutoConfig
import os
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict
import numpy as np
import torch

from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info

# Disable torch compile
os.environ["TORCH_COMPILE_DISABLE"] = "1"

router = APIRouter()

DESCRIPTION = "Random Baseline"
ROUTE = "/text"

class TextClassifier:
    def __init__(self):
        self.config = AutoConfig.from_pretrained("camillebrl/ModernBERT-envclaims-overfit")
        self.label2id = self.config.label2id
        self.classifier = pipeline(
            "text-classification",
            "camillebrl/ModernBERT-envclaims-overfit",
            device="cpu",
            batch_size=16
        )

    def process_batch(self, batch: List[str]) -> List[int]:
        """
        Process a batch of texts and return their predictions
        """
        try:
            batch_preds = self.classifier(list(batch))
            return [self.label2id[pred[0]["label"]] for pred in batch_preds]
        except Exception as e:
            print(f"Error processing batch: {e}")
            return []

@router.post(ROUTE, tags=["Text Task"], 
             description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
    """
    Evaluate text classification for climate disinformation detection.
    
    Current Model: Random Baseline
    - Makes random predictions from the label space (0-7)
    - Used as a baseline for comparison
    """
    # Get space info
    username, space_url = get_space_info()

    # Define the label mapping
    LABEL_MAPPING = {
        "0_not_relevant": 0,
        "1_not_happening": 1,
        "2_not_human": 2,
        "3_not_bad": 3,
        "4_solutions_harmful_unnecessary": 4,
        "5_science_unreliable": 5,
        "6_proponents_biased": 6,
        "7_fossil_fuels_needed": 7
    }

    # Load and prepare the dataset
    dataset = load_dataset(request.dataset_name)

    # Convert string labels to integers
    dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})

    # Split dataset
    train_test = dataset["train"]
    test_dataset = dataset["test"]
    
    # Start tracking emissions
    tracker.start()
    tracker.start_task("inference")

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE CODE HERE
    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
    #--------------------------------------------------------------------------------------------   
    
    # Make random predictions (placeholder for actual model inference)
    true_labels = test_dataset["label"]
    config = AutoConfig.from_pretrained("camillebrl/ModernBERT-envclaims-overfit")
    label2id = config.label2id
    # classifier = pipeline(
    #         "text-classification",
    #         "camillebrl/ModernBERT-envclaims-overfit",
    #         device="cpu"
    #         )
    # print("len dataset : ", len(test_dataset["quote"]))
    # predictions = []
    # for batch in range(0, len(test_dataset["quote"]), 32):  # Ajustez la taille des batchs
    #     batch_quotes = test_dataset["quote"][batch:batch + 32]
    #     batch_predictions = classifier(batch_quotes)
    #     predictions.extend([label2id[pred["label"]] for pred in batch_predictions])
    #     print(predictions)
    # print("final predictions : ", predictions)
    # Initialize the model once
    classifier = TextClassifier()

    # Prepare batches
    batch_size = 32
    quotes = test_dataset["quote"]
    num_batches = len(quotes) // batch_size + (1 if len(quotes) % batch_size != 0 else 0)
    batches = [
        quotes[i * batch_size:(i + 1) * batch_size]
        for i in range(num_batches)
    ]

    # Process batches in parallel
    max_workers = min(os.cpu_count(), 4)  # Limit to 4 workers or CPU count
    print(f"Processing with {max_workers} workers")
    
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # Submit all batches for processing
            futures = [
                executor.submit(classifier.process_batch, batch)
                for batch in batches
            ]

            # Collect results in order
            for future in futures:
                try:
                    batch_preds = future.result()
                    predictions.extend(batch_preds)
                except Exception as e:
                    print(f"Batch processing failed: {e}")

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE STOPS HERE
    #--------------------------------------------------------------------------------------------   

    
    # Stop tracking emissions
    emissions_data = tracker.stop_task()
    
    # Calculate accuracy
    accuracy = accuracy_score(true_labels, predictions)
    print("accuracy : ", accuracy)
    
    # Prepare results dictionary
    results = {
        "username": username,
        "space_url": space_url,
        "submission_timestamp": datetime.now().isoformat(),
        "model_description": DESCRIPTION,
        "accuracy": float(accuracy),
        "energy_consumed_wh": emissions_data.energy_consumed * 1000,
        "emissions_gco2eq": emissions_data.emissions * 1000,
        "emissions_data": clean_emissions_data(emissions_data),
        "api_route": ROUTE,
        "dataset_config": {
            "dataset_name": request.dataset_name,
            "test_size": request.test_size,
            "test_seed": request.test_seed
        }
    }

    print("results : ", results)
    
    return results