--- a
+++ b/reproducibility/embedders/plip.py
@@ -0,0 +1,77 @@
+import clip
+import tqdm
+import numpy as np
+import torch
+from reproducibility.embedders.internal_datasets import *
+from torch.utils.data import DataLoader
+from reproducibility.utils.cacher import cache_hit_or_miss, cache_numpy_object, cache_hit_or_miss_raw_filename, cache_numpy_object_raw_filename
+
+class CLIPEmbedder:
+
+    def __init__(self, model, preprocess, name, backbone):
+        self.model = model
+        self.preprocess = preprocess
+        self.name = name
+        self.backbone = backbone
+        
+    def image_embedder(self, list_of_images, device="cuda", num_workers=1, batch_size=32, additional_cache_name=""):
+        hit_or_miss = cache_hit_or_miss_raw_filename(self.name + "img" + additional_cache_name, self.backbone)
+
+        if hit_or_miss is not None:
+            return hit_or_miss
+        else:
+            hit = self.embed_images(list_of_images, device=device, num_workers=num_workers, batch_size=batch_size)
+            cache_numpy_object_raw_filename(hit, self.name + "img" + additional_cache_name, self.backbone)
+            return hit
+
+    def text_embedder(self, list_of_labels, device="cuda", num_workers=1, batch_size=32, additional_cache_name=""):
+        hit_or_miss = cache_hit_or_miss(self.name + "txt" + additional_cache_name, self.backbone)
+
+        if hit_or_miss is not None:
+            return hit_or_miss
+        else:
+            hit = self.embed_text(list_of_labels, device=device, num_workers=num_workers, batch_size=batch_size)
+            cache_numpy_object(hit, self.name + "txt" + additional_cache_name, self.backbone)
+            return hit
+
+    def embed_images(self, list_of_images, device="cuda", num_workers=1, batch_size=32):
+        train_dataset = CLIPImageDataset(list_of_images, self.preprocess)
+        dataloader = DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers)
+
+        image_embeddings = []
+
+        total = len(list_of_images) // batch_size
+        pbar = tqdm.tqdm(total=total, position=0)
+        with torch.no_grad():
+            for images in dataloader:
+                images = images.to(device)
+                image_embeddings.extend(self.model.encode_image(images).detach().cpu().numpy())
+                pbar.update(1)
+            pbar.close()
+
+        image_embeddings = np.array(image_embeddings)
+        image_embeddings = image_embeddings / np.linalg.norm(image_embeddings, axis=1, keepdims=True)
+        return image_embeddings
+
+    def embed_text(self, list_of_labels, device="cuda", num_workers=1, batch_size=32):
+        train_dataset = CLIPCaptioningDataset(list_of_labels)
+        dataloader = DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers)
+        text_embeddings = []
+        total = len(list_of_labels) // batch_size
+
+        pbar = tqdm.tqdm(total=total, position=0)
+        with torch.no_grad():
+            for captions in dataloader:
+                idx = clip.tokenize(captions, truncate=True).to(device)
+                text_embeddings.extend(self.model.encode_text(idx).detach().cpu().numpy())
+
+                pbar.update(1)
+
+            pbar.close()
+
+        text_embeddings = np.array(text_embeddings)
+        text_embeddings = text_embeddings / np.linalg.norm(text_embeddings, axis=1, keepdims=True)
+
+        return text_embeddings
+
+