import os
import gradio as gr
import random
import time
from datasets import load_dataset, Dataset

"""
This code is designed to read in the ImageNet 1K ILSVRC dataset from the Hugging Face Hub, 
then create a new version of this dataset with {percentage} lines with random labels between 0-9,
then upload this new version of the Hugging Face Hub, in the Data Composition organization:
https://huggingface.co/datasets/datacomp
"""

# The number of examples/instances in this dataset is copied from the model card:
# https://huggingface.co/datasets/ILSVRC/imagenet-1k
NUM_EXAMPLES = 1281167
# Arbitrary small number, only using in devv'ing (uncomment #.take(DEV_AMOUNT) below to use it).
DEV_AMOUNT = 10
GATED_IMAGENET = os.environ.get("GATED_IMAGENET")


def main(percentage=10):
    global randomize_subset
    # Just for timing how long this takes.
    start = time.time()

    percentage = float(percentage)
    print("Randomizing %d percent of the data." % percentage)
    # Set the random seed, based on the percentage, so that our random changes are reproducible.
    random.seed(percentage)

    # Load the dataset from the HF hub. Use streaming so as not to load the entire dataset at once.
    # Use the .take(DEV_AMOUNT) to only grab a small chunk of instances to develop with.
    dataset = load_dataset("ILSVRC/imagenet-1k", split="train", streaming=True,
                           trust_remote_code=True, token=GATED_IMAGENET).take(DEV_AMOUNT)

    # Create a set of indices that are randomly chosen, to change their labels.
    # Specifically, randomly choose NUM_EXAMPLES/percentage indices.
    randomize_subset = set(random.sample(range(0, NUM_EXAMPLES), round(
        NUM_EXAMPLES / float(percentage))))

    # Update the dataset so that the labels are randomized
    updated_dataset = dataset.map(randomize_labels, with_indices=True,
                                  features=dataset.features, batched=True)

    # Upload the new version of the dataset (this will take awhile)
    Dataset.from_generator(updated_dataset.__iter__).push_to_hub(
        "datacomp/imagenet-1k-random" + str(percentage), token=GATED_IMAGENET)

    end = time.time()
    print("That took %d seconds" % (end - start))


def randomize_labels(examples, indices):
    # What set of examples should be randomized in this batch?
    # This is the intersection of the batch indices and the indices we randomly selected to change the labels of.
    batch_subset = list(set(indices) & randomize_subset)
    # If this batch has indices that we're changing the label of....
    if batch_subset != []:
        # Change the label to a random integer between 0 and 9
        for n in range(len(indices)):
            index = indices[n]
            examples["label"][n] = random.randint(0,
                                                  9) if index in batch_subset else \
            examples["label"][n]
    return examples

demo = gr.Interface(fn=main, inputs="text", outputs="text")
demo.launch()