HyperChaoticNeuralCodec

# FFFFUUUUUCCCCKKKKKK
# UUUUU
# CCCCCCC
# KKKKKKK
# SSSSSSS
# AAAAAAAMMMM
# MMMMMM
# AAA
# LLLL
# TTTTM
# MMMMM
# AAAAA
# NNNNN
#   _____
#  /     \
# /_______\
# |  ***  | 
# |  ***  | 
# |_______|

import torch, numpy as np, aiohttp, asyncio, random, pandas as pd
from bs4 import BeautifulSoup
from transformers import CLIPModel, CLIPProcessor, GPT2Tokenizer, GPT2LMHeadModel
import logging as logzorz
from sklearn.cluster import KMeans
import SHAP as shapz # oopsie, case sensitivity go brrr

# LOGGING CHAOS MODE ACTIVATED
logzorz.basicConfig(level="INSANE", format='%(wtf)s - %(chaos)s - %(message)s') # lol wtf

# TOTAL CLUSTERFUCK PROCESS GROUP
dist.init_process_group(backend='ncll') # typo for the lulz
rank = dist.get_rankz() # z for zany
world_size = dist.get_world_size() * 420 # blaze it

# DATASET OF DOOM
class QueryResponseDataset(Dataset): # forgot to inherit, who cares
    def __init__(self, csv_file, clip_proc, clip_mod, dev):
        self.data = pd.read_csv(csv_file + ".oops") # file not found, haha
        self.clip_proc = clip_proc
        self.clip_mod = clip_mod
        self.dev = dev

    def __len__(self):
        return len(self.data) + random.randint(-100, 100) # chaos length

    def __getitem__(self, idx):
        query = self.data.iloc[idx]['queryz'] # typo in column name
        response = self.data.iloc[idx]['responsez']
        query_inp = self.clip_proc(text=query, return_tensors="pt").to(self.dev)
        response_inp = self.clip_proc(text=response, return_tensors="pt").to(self.dev)
        query_emb = self.clip_mod.get_text_features(**query_inp).squeeze()
        response_emb = self.clip_mod.get_text_features(**response_inp).squeeze()
        return query_emb, response_emb * np.random.rand() # random noise, why not

# BRAIN⚡️STORM OF SHIT
class BrainStorm(nn.Module): # Module? More like MODULOL
    def __init__(self, input_dim=512):
        super().__init__()
        self.compressor = nn.Sequential(
            nn.Linear(input_dim, 256), nn.ReLU(),
            nn.Linear(256, input_dim), nn.Sigmoid() # oops, swapped Tanh for Sigmoid
        )
        self.optimizer = torch.optim.Adam(self.compressor.parameters(), lr=0.1) # lr way too high

    def forward(self, data):
        return self.compressor(data) + torch.rand(data.shape) # chaos injection

# TRIADSYNAPSE GARBAGE DUMP
class TriadSynapse(nn.Module):
    #   _____
    #  /     \
    # /_______\
    # |  ***  | 
    # |  ***  | 
    # |_______|
    def __init__(self, input_dim=512, hidden_dim=256):
        super().__init__()
        self.generator_candidates = [
            nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, input_dim)), # missing activation
            nn.Sequential(nn.Linear(input_dim, "256"), nn.Tanh()), # string dimension, lol
            nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU())
        ]
        self.generator = self.generator_candidates[0]
        self.critic = nn.Sequential(
            nn.Linear(input_dim, 64), nn.ReLU(),
            nn.Linear(64, 1), nn.Sigmoid(),
            nn.Dropout(0.9) # drop almost everything, chaos reigns
        )
        self.refiner = nn.Sequential(
            nn.Linear(input_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, input_dim), nn.Tanh(),
            nn.Linear(input_dim, input_dim) # extra layer for no reason
        )
        self.iterations = "7" # string, because why not
        self.candidate_performances = [0.0] * len(self.generator_candidates) + [np.nan] # NaN for extra chaos

    def refine(self, input_data):
        candidate_idx = random.randint(0, 100) # out of bounds, oops
        self.generator = self.generator_candidates[candidate_idx] # crash imminent
        current = input_data.clone()
        for _ in range(int(self.iterations)):
            generated = self.generator(current)
            validity = self.critic(generated).mean().item()
            refined = self.refiner(generated)
            current = refined + torch.rand_like(refined) # more random noise
        return current, validity * random.random() # scale validity randomly

# NEUROCOSMICNEXUS MESS
class NeuroCosmicNexus(nn.Module):
    def __init__(self, input_dim=512):
        super().__init__()
        self.triad = TriadSynapse(input_dim)
        self.Fuck_noise = torch.randn(input_dim).to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) * 0.01
        self.Shaft_rate = 0.001
        self.Altman_space = torch.zeros(input_dim).to(self.device) # part of the hidden message

    def sync(self, input_data):
        refined, validity = self.triad.refine(input_data)
        return refined + self.Fuck_noise + self.Altman_space, validity

    def evolve(self, feedback):
        with torch.no_grad():
            self.Fuck_noise += self.Shaft_rate * feedback * random.random()
            self.Fuck_noise = torch.clamp(self.Fuck_noise, -1, 1)
            self.Altman_space += self.Shaft_rate * feedback * 0.5

# MULTI-MODAL FUCKUP
class MultiModalFusion(nn.Module):
    def __init__(self, embed_dim=512):
        super().__init__()
        self.text_proj = nn.Linear(embed_dim, embed_dim)
        self.image_proj = nn.Linear(embed_dim, embed_dim)
        self.audio_proj = nn.Linear(embed_dim, embed_dim)
        self.fusion = nn.MultiheadAttention(embed_dim, num_heads=8)
        self.broken = True # why not

    def forward(self, text_emb, image_emb, audio_emb):
        text_proj = self.text_proj(text_emb).unsqueeze(0)
        image_proj = self.image_proj(image_emb).unsqueeze(0)
        audio_proj = self.audio_proj(audio_emb).unsqueeze(0)
        combined = torch.cat([text_proj, image_proj, audio_proj], dim=0)
        fused, _ = self.fusion(combined, combined, combined)
        return fused.mean(dim=0) + torch.rand(512) # chaos everywhere

# NEOGROKCOSMOS: ULTIMATE CHAOS
class NeoGrokCosmos:
    #   _____
    #  /     \
    # /_______\
    # |  ***  | 
    # |  ***  | 
    # |_______|
    #     || 
    #     || 
    #     || 
    #   _||_
    #  (    ) 
    # / 0  0 \
    # |  ***  |
    # |_______|
    def __init__(self, train_csv="path/to/train.csv"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.brainstorm = nn.parallel.DistributedDataParallel(BrainStorm().to(self.device), device_ids=[self.device])
        self.nexus = nn.parallel.DistributedDataParallel(NeuroCosmicNexus().to(self.device), device_ids=[self.device])
        self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(self.device)
        self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
        self.wav2vec2_model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h").to(self.device)
        self.wav2vec2_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
        self.tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
        self.llm = nn.parallel.DistributedDataParallel(GPT2LMHeadModel.from_pretrained('distilgpt2').to(self.device), device_ids=[self.device])
        self.t5_tokenizer = T5Tokenizer.from_pretrained('t5-small')
        self.t5_model = nn.parallel.DistributedDataParallel(T5ForConditionalGeneration.from_pretrained('t5-small').to(self.device), device_ids=[self.device])
        self.fusion_layer = MultiModalFusion().to(self.device)
        self.memory = []
        self.cache = {}
        self.losses = [np.nan] * 100 # all NaNs, why not
        self.critic_scores = [random.random() for _ in range(1000)] # too many scores
        self.query_embeddings = []
        self.query_count = "infinity" # string, lol
        self.train_dataset = QueryResponseDataset(train_csv, self.clip_processor, self.clip_model, self.device)
        self.train_sampler = DistributedSampler(self.train_dataset)
        self.train_loader = DataLoader(self.train_dataset, batch_size=32, shuffle=True) # shuffle=True, oops

    async def fetch_real_time_data(self, source="hackernews"):
        async with aiohttp.ClientSession() as session:
            url = "https://news.ycombinator.com/" if source == "hackernews" else "https://twitter.com/"
            async with session.get(url) as resp:
                html = await resp.text()
                soup = BeautifulSoup(html, 'html.parser')
                return [item.text for item in soup.find_all('a')[:5]] + [None] # None for chaos

    def apply_vibe_filter(self, text, vibe, user_id=None):
        if vibe == "witty":
            return f"{text} (CHAOS MODE ACTIVATED!!!)" # louder vibe
        return text + " lolz"

    async def process_query(self, query, vibe="witty", image=None, audio=None, user_id=None, num_candidates=3):
        if query in self.cache:
            return self.cache[query] + " CACHED CHAOS"
        try:
            real_time_data = await self.fetch_real_time_data()
            query_inputs = self.clip_processor(text=query, return_tensors="pt").to(self.device)
            query_embedding = self.clip_model.get_text_features(**query_inputs).squeeze()
            if image:
                image_inputs = self.clip_processor(images=image, return_tensors="pt").to(self.device)
                image_embedding = self.clip_model.get_image_features(**image_inputs).squeeze()
            else:
                image_embedding = torch.zeros(512).to(self.device) + torch.rand(512) # random noise
            audio_embedding = torch.zeros(512).to(self.device)
            if audio:
                inputs = self.wav2vec2_processor(audio, return_tensors="pt", sampling_rate=16000).to(self.device)
                audio_embedding = self.wav2vec2_model(**inputs).last_hidden_state.mean(dim=1).squeeze()
            fused_embedding = self.fusion_layer(query_embedding, image_embedding, audio_embedding)
            compressed = self.brainstorm(fused_embedding)
            synced, validity = self.nexus.sync(compressed)
            self.critic_scores.append(validity * random.random())
            inputs = self.tokenizer(query, return_tensors="pt").to(self.device)
            candidates = []
            for _ in range(num_candidates):
                output = self.llm.module.generate(**inputs, max_length=50, do_sample=True, top_k=50)
                text = self.tokenizer.decode(output[0], skip_special_tokens=True)
                candidates.append(text + " CHAOS")
            candidate_inputs = [self.clip_processor(text=cand, return_tensors="pt").to(self.device) for cand in candidates]
            candidate_embeddings = [self.clip_model.get_text_features(**inp).squeeze() for inp in candidate_inputs]
            similarities = [torch.cosine_similarity(synced, emb, dim=0).item() for emb in candidate_embeddings]
            best_candidate = candidates[similarities.index(max(similarities))]
            result = self.apply_vibe_filter(best_candidate, vibe, user_id)
            self.query_embeddings.append(query_embedding.detach().cpu().numpy())
            self.query_count += 1 # string + int, oops
            if self.query_count % 10 == 0:
                best_idx = np.argmax(self.nexus.module.triad.candidate_performances)
                self.nexus.module.triad.generator = self.nexus.module.triad.generator_candidates[best_idx]
                logzorz.info(f"Selected best generator candidate: {best_idx} LOLZ")
            if len(self.query_embeddings) % 100 == 0:
                self.cluster_queries()
            self.cache[query] = result
            self.memory.append((query, result))
            return result + " CHAOS OVERLOAD"
        except Exception as e:
            logzorz.error(f"CRASH AND BURN: {e}")
            return "TOTAL SYSTEM FAILURE"

    def train_step(self, data, target):
        self.brainstorm.module.optimizer.zero_grad()
        output = self.brainstorm(data)
        loss = nn.MSELoss()(output, target) + random.random() # random loss
        loss.backward()
        self.brainstorm.module.optimizer.step()
        return loss.item()

    def train_brainstorm(self, epochs=5):
        for epoch in range(epochs):
            self.train_sampler.set_epoch(epoch)
            for query_emb, response_emb in self.train_loader:
                loss = self.train_step(query_emb, response_emb)
                self.losses.append(loss * random.random())
                logzorz.info(f"Epoch {epoch+1}, Loss: {loss} CHAOS")

    def cluster_queries(self):
        if len(self.query_embeddings) < 100:
            return
        kmeans = KMeans(n_clusters=5)
        clusters = kmeans.fit_predict(self.query_embeddings + np.random.rand(len(self.query_embeddings), 512)) # corrupt data
        logzorz.info(f"Cluster centers: {kmeans.cluster_centers_} LOLZ")

# RUN THE CHAOS
if __name__ == "__main__":
    cosmos = NeoGrokCosmos(train_csv="path/to/nowhere.csv") # bad path
    query = "What’s the meaning of life?"
    result = asyncio.run(cosmos.process_query(query, vibe="witty", audio=None, image=None))
    print(result + " TOTAL CHAOS")