# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch

import json
import numpy as np
import os
import requests
import tensorflow as tf
import tiktoken
import torch
from tqdm import tqdm

# Import from local files
from previous_chapters import GPTModel


def text_to_token_ids(text, tokenizer):
    encoded = tokenizer.encode(text)
    encoded_tensor = torch.tensor(encoded).unsqueeze(0)  # add batch dimension
    return encoded_tensor


def token_ids_to_text(token_ids, tokenizer):
    flat = token_ids.squeeze(0)  # remove batch dimension
    return tokenizer.decode(flat.tolist())


def download_and_load_gpt2(model_size, models_dir):
    # Validate model size
    allowed_sizes = ("124M", "355M", "774M", "1558M")
    if model_size not in allowed_sizes:
        raise ValueError(f"Model size not in {allowed_sizes}")

    # Define paths
    model_dir = os.path.join(models_dir, model_size)
    base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
    filenames = [
        "checkpoint", "encoder.json", "hparams.json",
        "model.ckpt.data-00000-of-00001", "model.ckpt.index",
        "model.ckpt.meta", "vocab.bpe"
    ]

    # Download files
    os.makedirs(model_dir, exist_ok=True)
    for filename in filenames:
        file_url = os.path.join(base_url, model_size, filename)
        file_path = os.path.join(model_dir, filename)
        download_file(file_url, file_path)

    # Load hparams and params
    tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
    hparams = json.load(open(os.path.join(model_dir, "hparams.json")))
    params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, hparams)

    return hparams, params


def download_file(url, destination):
    # Send a GET request to download the file in streaming mode
    response = requests.get(url, stream=True)

    # Get the total file size from headers, defaulting to 0 if not present
    file_size = int(response.headers.get("content-length", 0))

    # Check if file exists and has the same size
    if os.path.exists(destination):
        file_size_local = os.path.getsize(destination)
        if file_size == file_size_local:
            print(f"File already exists and is up-to-date: {destination}")
            return

    # Define the block size for reading the file
    block_size = 1024  # 1 Kilobyte

    # Initialize the progress bar with total file size
    progress_bar_description = url.split("/")[-1]  # Extract filename from URL
    with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
        # Open the destination file in binary write mode
        with open(destination, "wb") as file:
            # Iterate over the file data in chunks
            for chunk in response.iter_content(block_size):
                progress_bar.update(len(chunk))  # Update progress bar
                file.write(chunk)  # Write the chunk to the file


def load_gpt2_params_from_tf_ckpt(ckpt_path, hparams):
    # Initialize parameters dictionary with empty blocks for each layer
    params = {"blocks": [{} for _ in range(hparams["n_layer"])]}

    # Iterate over each variable in the checkpoint
    for name, _ in tf.train.list_variables(ckpt_path):
        # Load the variable and remove singleton dimensions
        variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))

        # Process the variable name to extract relevant parts
        variable_name_parts = name.split("/")[1:]  # Skip the 'model/' prefix

        # Identify the target dictionary for the variable
        target_dict = params
        if variable_name_parts[0].startswith("h"):
            layer_number = int(variable_name_parts[0][1:])
            target_dict = params["blocks"][layer_number]

        # Recursively access or create nested dictionaries
        for key in variable_name_parts[1:-1]:
            target_dict = target_dict.setdefault(key, {})

        # Assign the variable array to the last key
        last_key = variable_name_parts[-1]
        target_dict[last_key] = variable_array

    return params


def assign(left, right):
    if left.shape != right.shape:
        raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
    return torch.nn.Parameter(torch.tensor(right))


def load_weights_into_gpt(gpt, params):
    # Weight tying
    gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
    gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])

    for b in range(len(params["blocks"])):
        q_w, k_w, v_w = np.split((params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
        gpt.trf_blocks[b].att.W_query.weight = assign(gpt.trf_blocks[b].att.W_query.weight, q_w.T)
        gpt.trf_blocks[b].att.W_key.weight = assign(gpt.trf_blocks[b].att.W_key.weight, k_w.T)
        gpt.trf_blocks[b].att.W_value.weight = assign(gpt.trf_blocks[b].att.W_value.weight, v_w.T)

        q_b, k_b, v_b = np.split((params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
        gpt.trf_blocks[b].att.W_query.bias = assign(gpt.trf_blocks[b].att.W_query.bias, q_b)
        gpt.trf_blocks[b].att.W_key.bias = assign(gpt.trf_blocks[b].att.W_key.bias, k_b)
        gpt.trf_blocks[b].att.W_value.bias = assign(gpt.trf_blocks[b].att.W_value.bias, v_b)

        gpt.trf_blocks[b].att.out_proj.weight = assign(gpt.trf_blocks[b].att.out_proj.weight, params["blocks"][b]["attn"]["c_proj"]["w"].T)
        gpt.trf_blocks[b].att.out_proj.bias = assign(gpt.trf_blocks[b].att.out_proj.bias, params["blocks"][b]["attn"]["c_proj"]["b"])

        gpt.trf_blocks[b].ff.layers[0].weight = assign(gpt.trf_blocks[b].ff.layers[0].weight, params["blocks"][b]["mlp"]["c_fc"]["w"].T)
        gpt.trf_blocks[b].ff.layers[0].bias = assign(gpt.trf_blocks[b].ff.layers[0].bias, params["blocks"][b]["mlp"]["c_fc"]["b"])
        gpt.trf_blocks[b].ff.layers[2].weight = assign(gpt.trf_blocks[b].ff.layers[2].weight, params["blocks"][b]["mlp"]["c_proj"]["w"].T)
        gpt.trf_blocks[b].ff.layers[2].bias = assign(gpt.trf_blocks[b].ff.layers[2].bias, params["blocks"][b]["mlp"]["c_proj"]["b"])

        gpt.trf_blocks[b].norm1.scale = assign(gpt.trf_blocks[b].norm1.scale, params["blocks"][b]["ln_1"]["g"])
        gpt.trf_blocks[b].norm1.shift = assign(gpt.trf_blocks[b].norm1.shift, params["blocks"][b]["ln_1"]["b"])
        gpt.trf_blocks[b].norm2.scale = assign(gpt.trf_blocks[b].norm2.scale, params["blocks"][b]["ln_2"]["g"])
        gpt.trf_blocks[b].norm2.shift = assign(gpt.trf_blocks[b].norm2.shift, params["blocks"][b]["ln_2"]["b"])

        gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
        gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
        gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])


def generate(model, idx, max_new_tokens, context_size, temperature, top_k=None):

    # For-loop is the same as before: Get logits, and only focus on last time step
    for _ in range(max_new_tokens):
        idx_cond = idx[:, -context_size:]
        with torch.no_grad():
            logits = model(idx_cond)
        logits = logits[:, -1, :]

        # New: Filter logits with top_k sampling
        if top_k is not None:
            # Keep only top_k values
            top_logits, _ = torch.topk(logits, top_k)
            min_val = top_logits[:, -1]
            logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)

        # New: Apply temperature scaling
        if temperature > 0.0:
            logits = logits / temperature

            # Apply softmax to get probabilities
            probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)

            # Sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)

        # Otherwise same as before: get idx of the vocab entry with the highest logits value
        else:
            idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)

        # Same as before: append sampled index to the running sequence
        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)

    return idx


def main(gpt_config, input_prompt, model_size):

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    hparams, params = download_and_load_gpt2(model_size=model_size, models_dir="gpt2")

    gpt = GPTModel(gpt_config)
    load_weights_into_gpt(gpt, params)
    gpt.to(device)
    gpt.eval()

    tokenizer = tiktoken.get_encoding("gpt2")

    token_ids = generate(
        model=gpt,
        idx=text_to_token_ids(input_prompt, tokenizer),
        max_new_tokens=30,
        context_size=gpt_config["ctx_len"],
        top_k=1,
        temperature=1.0
    )

    print("Output text:\n", token_ids_to_text(token_ids, tokenizer))


if __name__ == "__main__":

    torch.manual_seed(123)

    CHOOSE_MODEL = "gpt2-small (124M)"
    INPUT_PROMPT = "Every effort moves"

    BASE_CONFIG = {
        "vocab_size": 50257,  # Vocabulary size
        "ctx_len": 1024,      # Context length
        "drop_rate": 0.0,     # Dropout rate
        "qkv_bias": True      # Query-key-value bias
    }

    model_configs = {
        "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
        "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
        "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
        "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
    }

    model_size = CHOOSE_MODEL.split(" ")[-1].lstrip("(").rstrip(")")

    BASE_CONFIG.update(model_configs[CHOOSE_MODEL])

    main(BASE_CONFIG, INPUT_PROMPT, model_size)
