# 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

from llms_from_scratch.ch02 import create_dataloader_v1

import os

import requests
import pytest
import torch


@pytest.mark.parametrize("file_name", ["the-verdict.txt"])
def test_dataloader(tmp_path, file_name):

    if not os.path.exists("the-verdict.txt"):
        url = (
            "https://raw.githubusercontent.com/rasbt/"
            "LLMs-from-scratch/main/ch02/01_main-chapter-code/"
            "the-verdict.txt"
        )
        file_path = "the-verdict.txt"

        response = requests.get(url, timeout=30)
        response.raise_for_status()
        with open(file_path, "wb") as f:
            f.write(response.content)

    with open("the-verdict.txt", "r", encoding="utf-8") as f:
        raw_text = f.read()

    vocab_size = 50257
    output_dim = 256
    context_length = 1024

    token_embedding_layer = torch.nn.Embedding(vocab_size, output_dim)
    pos_embedding_layer = torch.nn.Embedding(context_length, output_dim)

    batch_size = 8
    max_length = 4
    dataloader = create_dataloader_v1(
        raw_text,
        batch_size=batch_size,
        max_length=max_length,
        stride=max_length
    )

    for batch in dataloader:
        x, y = batch

        token_embeddings = token_embedding_layer(x)
        pos_embeddings = pos_embedding_layer(torch.arange(max_length))

        input_embeddings = token_embeddings + pos_embeddings

        break

    input_embeddings.shape == torch.Size([8, 4, 256])