import torch

from bitnet import BitLinear

# Input
x = torch.randn(10, 512)

# BitLinear layer
layer = BitLinear(512, 400)

# Output
y = layer(x)

print(y)

import torch

from bitnet import BitNetTransformer

bitnet = BitNetTransformer(
    num_tokens=20000,
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    ff_mult=4,
)

tokens = torch.randint(0, 20000, (1, 512))
logits = bitnet(tokens)
print(logits.shape)

import torch

from bitnet.bitffn import BitFeedForward

# Random input
x = torch.randn(10, 512)

# FFN
ff = BitFeedForward(512)

# Apply FFN
y = ff(x)

print(y.shape)
# torch.Size([10, 512])

from bitnet import BitNetInference

bitnet = BitNetInference()
bitnet.load_model("../model_checkpoint.pth")  # Download model
output_str = bitnet.generate("The dog jumped over the ", 512)
print(output_str)

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from bitnet import replace_linears_in_hf

# Load a model from Hugging Face's Transformers
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Replace Linear layers with BitLinear
replace_linears_in_hf(model)

# Example text to classify
text = "Replace this with your text"
inputs = tokenizer(
    text, return_tensors="pt", padding=True, truncation=True, max_length=512
)

# Perform inference
model.eval()  # Set the model to evaluation mode
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    print(predictions)

# Process predictions
predicted_class_id = predictions.argmax().item()
print(f"Predicted class ID: {predicted_class_id}")

# Optionally, map the predicted class ID to a label, if you know the classification labels
# labels = ["Label 1", "Label 2", ...]  # Define your labels corresponding to the model's classes
# print(f"Predicted label: {labels[predicted_class_id]}")