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import numpy as np

import matplotlib.pyplot as plt

from sklearn.decomposition import PCA

import torch

# plot embedding vectors using PCA

def plot_embedding_pca(embedding_vectors, labels=None):

    # embedding_vectors: list of embedding vectors

    # labels: list of labels (e.g. token name) for each embedding vector

    if labels is None:

        labels = range(len(embedding_vectors))

    reduced_vectors = PCA(n_components=2, random_state=42).fit_transform(

        embedding_vectors

    )  # Apply dimensionality reduction

    # Create the plot

    plt.figure(figsize=(10, 6))

    origin = np.zeros((len(embedding_vectors), 2))

    for i, label in enumerate(labels):

        plt.quiver(

            origin[:, 0],

            origin[:, 1],

            reduced_vectors[i, 0],

            reduced_vectors[i, 1],

            angles="xy",

            scale_units="xy",

            scale=1,

            width=0.002,

        )

        plt.scatter(reduced_vectors[i, 0], reduced_vectors[i, 1])

        plt.annotate(label, (reduced_vectors[i, 0], reduced_vectors[i, 1]))

    plt.title("Embedding Visualization (using 2 Principal Components)")

    plt.show()

# plot torch model

# from https://github.com/Atcold/NYU-DLSP20/blob/master/res/plot_lib.py

def plot_model(X, y, model):

    model.cpu()

    mesh = np.arange(-1.1, 1.1, 0.01)

    xx, yy = np.meshgrid(mesh, mesh)

    with torch.no_grad():

        data = torch.from_numpy(np.vstack((xx.reshape(-1), yy.reshape(-1))).T).float()

        Z = model(data).detach()

    Z = np.argmax(Z, axis=1).reshape(xx.shape)

    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.3)

    plot_data(X, y)

##############################################################################

## UNUSED

##############################################################################

# # plot embeddings as linechart

# def plot_embeddings_linechart(embedding_vectors, labels=None):

#     """

#     Plots the embeddings for the given indices.

#     Args:

#     - embeddings: The embeddings tensor.

#     - indices: List of indices to plot.

#     Example usage:

#     plot_embeddings_linechart(position_embeddings, [1, 2])

#     """

#     colors = [

#         "blue",

#         "orange",

#         "green",

#         "red",

#         "purple",

#         "brown",

#     ]  # Define colors to use for each plot

#     if labels is None:

#         labels = range(len(embedding_vectors))

#     plt.figure(figsize=(10, 6))  # Set the figure size

#     # Plot each embedding based on the index

#     for i, vector in enumerate(embedding_vectors):

#         plt.plot(

#             vector,

#             label=f"Position {i}",

#             color=colors[i % len(colors)],

#         )

#     # Add legend

#     plt.legend()

#     # Add title and axis labels for clarity

#     plt.title("Position Embeddings")

#     plt.xlabel("Embedding Dimension")

#     plt.ylabel("Embedding Value")

#     # Display the plot

#     plt.show()