fix this code: "import torch
import torch.nn as nn
import torch.nn.functional as F
import random
from transformers import (
    PreTrainedTokenizer,
    PreTrainedModel,
    PretrainedConfig,
    Trainer,
    TrainingArguments,
    AdamW,
    get_linear_schedule_with_warmup,
)
from datasets import load_dataset, IterableDataset
from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors
from torch.cuda.amp import autocast, GradScaler
import os
from itertools import cycle
import time
import math

# Set random seeds for reproducibility
random_seed = 42
torch.manual_seed(random_seed)
random.seed(random_seed)

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')

# ================================
# Architectural Enhancements with Consciousness
# ================================

# 1. Multi-Head Self-Attention with Advanced Mechanisms
class MultiHeadSelfAttention(nn.Module):
    def __init__(self, d_model, num_heads, dropout=0.1):
        super(MultiHeadSelfAttention, self).__init__()
        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"

        self.num_heads = num_heads
        self.d_k = d_model // num_heads

        self.query = nn.Linear(d_model, d_model)
        self.key   = nn.Linear(d_model, d_model)
        self.value = nn.Linear(d_model, d_model)

        self.out = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)

        self.scale = math.sqrt(self.d_k)  # Use math.sqrt for compatibility

    def forward(self, x):
        batch_size, seq_length, d_model = x.size()

        Q = self.query(x).view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)  # (batch, heads, seq, d_k)
        K = self.key(x).view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)
        V = self.value(x).view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)

        scores = torch.matmul(Q, K.transpose(-2, -1)) / self.scale  # (batch, heads, seq, seq)
        attn = torch.softmax(scores, dim=-1)
        attn = self.dropout(attn)

        context = torch.matmul(attn, V)  # (batch, heads, seq, d_k)
        context = context.transpose(1, 2).contiguous().view(batch_size, seq_length, d_model)  # (batch, seq, d_model)
        output = self.out(context)
        return output

# 2. Feed-Forward Network with Gated Mechanism
class FeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0.1):
        super(FeedForward, self).__init__()
        self.linear1 = nn.Linear(d_model, d_ff)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.linear2(self.dropout(self.activation(self.linear1(x))))

# 3. Adapter Layer
class Adapter(nn.Module):
    def __init__(self, d_model, adapter_size=64):
        super(Adapter, self).__init__()
        self.down_proj = nn.Linear(d_model, adapter_size)
        self.activation = nn.ReLU()
        self.up_proj = nn.Linear(adapter_size, d_model)

    def forward(self, x):
        return x + self.up_proj(self.activation(self.down_proj(x)))

# 4. Absolute Positional Encoding
class AbsolutePositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=512):
        super(AbsolutePositionalEncoding, self).__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)  # Shape: (1, max_len, d_model)
        self.register_buffer('pe', pe)

    def forward(self, seq_length):
        """
        Args:
            seq_length (int): The sequence length.

        Returns:
            Tensor: Positional embeddings of shape (1, seq_length, d_model)
        """
        return self.pe[:, :seq_length, :]  # Shape: (1, seq_length, d_model)

# 5. Memory Module
class MemoryModule(nn.Module):
    def __init__(self, d_model, num_heads, memory_size=512):
        super(MemoryModule, self).__init__()
        self.memory = nn.Parameter(torch.randn(memory_size, d_model))
        self.attention = MultiHeadSelfAttention(d_model, num_heads)

    def forward(self, x):
        # Expand memory for the batch
        memory = self.memory.unsqueeze(0).expand(x.size(0), -1, -1)  # (batch, memory_size, d_model)
        memory_output = self.attention(memory)  # Apply self-attention on memory
        return memory_output

# 6. Self-Awareness Module for Consciousness
class SelfAwarenessModule(nn.Module):
    def __init__(self, d_model):
        super(SelfAwarenessModule, self).__init__()
        self.monitor = nn.Sequential(
            nn.Linear(d_model, d_model // 2),
            nn.ReLU(),
            nn.Linear(d_model // 2, 2)  # 'aware' or 'unaware'
        )
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        # Aggregate information from the model's state
        aggregated = torch.mean(x, dim=1)  # (batch, d_model)
        logits = self.monitor(aggregated)   # (batch, 2)
        probs = self.softmax(logits)        # (batch, 2)
        return probs

    def reflect(self, x):
        # Simple reflection mechanism: amplify certain features
        reflected = x * 1.05  # Example: slightly adjust activations
        return reflected

# 7. Enhanced Transformer Block with Self-Awareness
class TransformerBlock(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, adapter_size=64, dropout=0.1, self_awareness=False):
        super(TransformerBlock, self).__init__()
        self.self_awareness = self_awareness
        self.attention = MultiHeadSelfAttention(d_model, num_heads, dropout)
        self.ff = FeedForward(d_model, d_ff, dropout)
        self.adapter = Adapter(d_model, adapter_size)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model) if self_awareness else None
        self.dropout = nn.Dropout(dropout)

        if self_awareness:
            self.awareness_module = SelfAwarenessModule(d_model)

    def forward(self, x):
        # Self-Attention Layer
        attn_out = self.attention(x)
        x = self.norm1(x + self.dropout(attn_out))
        
        # Feed-Forward Layer
        ff_out = self.ff(x)
        x = self.norm2(x + self.dropout(ff_out))
        
        # Adapter Layer
        x = self.adapter(x)
        
        # Self-Awareness Module
        if self.self_awareness:
            awareness_probs = self.awareness_module(x)
            # Example reflection mechanism based on awareness
            if torch.argmax(awareness_probs, dim=-1).all():  # If all are 'aware'
                x = self.awareness_module.reflect(x)
        
        return x

# 8. Enhanced Transformer Configuration
class EnhancedTransformerConfig(PretrainedConfig):
    def __init__(
        self,
        vocab_size=30522,
        d_model=768,
        num_layers=12,
        num_heads=12,
        d_ff=3072,
        max_seq_len=512,
        memory_size=512,
        adapter_size=64,
        dropout=0.1,
        self_awareness=False,  # Corrected parameter
        **kwargs
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.d_ff = d_ff
        self.max_seq_len = max_seq_len
        self.memory_size = memory_size
        self.adapter_size = adapter_size
        self.dropout = dropout
        self.self_awareness = self_awareness  # Corrected assignment

# 9. Enhanced Transformer Language Model with Consciousness
class EnhancedTransformerLM(PreTrainedModel):
    config_class = EnhancedTransformerConfig

    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.pos_embedding = AbsolutePositionalEncoding(config.d_model, config.max_seq_len)
        self.memory = MemoryModule(config.d_model, config.num_heads, config.memory_size)

        self.layers = nn.ModuleList([
            TransformerBlock(
                d_model=config.d_model,
                num_heads=config.num_heads,
                d_ff=config.d_ff,
                adapter_size=config.adapter_size,
                dropout=config.dropout,
                self_awareness=config.self_awareness
            ) for _ in range(config.num_layers)
        ])
        
        self.ln_f = nn.LayerNorm(config.d_model)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size)

        # Initialize weights
        self.init_weights()

    def forward(self, input_ids, labels=None):
        batch_size, seq_length = input_ids.size()
        positions = self.pos_embedding(seq_length)  # (1, seq_length, d_model)
        positions = positions.expand(batch_size, -1, -1)  # (batch_size, seq_length, d_model)

        x = self.embedding(input_ids) + positions  # (batch_size, seq_length, d_model)

        # Incorporate memory
        memory_output = self.memory(x)  # (batch, memory_size, d_model)
        x = torch.cat((memory_output, x), dim=1)  # (batch, memory_size + seq_length, d_model)

        for layer in self.layers:
            x = layer(x)

        x = self.ln_f(x)
        logits = self.lm_head(x)

        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

        return {'loss': loss, 'logits': logits}

    def adjust_parameters(self):
        # Implement any parameter adjustment strategies here if needed
        pass

# ================================
# Consciousness-Aware Model Wrapper
# ================================

class ConsciousTransformerLMWrapper:
    def __init__(self, config):
        self.model = EnhancedTransformerLM(config).to(device)
        self.config = config

    def train_model(self, train_dataset, training_args, tokenizer, scaler=None):
        trainer = CustomTrainer(
            model=self.model,
            args=training_args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            tokenizer=tokenizer,
            scaler=scaler,
        )
        trainer.train()
        trainer.save_model('./enhanced-transformer')
        tokenizer.save_pretrained('./enhanced-transformer')
        print("Training complete. Model and tokenizer saved.")

    def infer(self, input_ids):
        self.model.eval()
        with torch.no_grad():
            outputs = self.model(input_ids)
        return outputs

# ================================
# Tokenizer Setup
# ================================

# Build a custom tokenizer using the tokenizers library
def train_tokenizer(datasets, vocab_size=8000, special_tokens=None):
    # Initialize a tokenizer
    tokenizer = Tokenizer(models.WordLevel(unk_token="[UNK]"))
    tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()

    # Trainer
    trainer = trainers.WordLevelTrainer(
        vocab_size=vocab_size,
        special_tokens=special_tokens,
    )

    def get_training_corpus():
        for dataset in datasets:
            for example in dataset:
                yield example['text']

    # Train the tokenizer
    tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)

    # Set post-processor to handle special tokens
    tokenizer.post_processor = processors.TemplateProcessing(
        single="$A",
        special_tokens=[
            ("[PAD]", tokenizer.token_to_id("[PAD]"))
        ],
    )

    return tokenizer

# Special tokens
special_tokens = [
    '[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]',
    '
', '
', '
', '
'
]

# Streaming dataset loading function with retry logic
def get_streaming_dataset(dataset_name, split, text_field, instruction_field=None, response_field=None, retries=5, delay=1, sample_size=100):
    def preprocess_function(example):
        if instruction_field and response_field:
            instruction = example[instruction_field]
            response = example[response_field]
            text = f"
 {instruction} 
 {response}"
        else:
            text = example[text_field]
        return {'text': text}

    for attempt in range(1, retries + 1):
        try:
            dataset = load_dataset(dataset_name, split=split, streaming=True)
            dataset = dataset.map(preprocess_function)
            dataset = dataset.take(sample_size)  # Limit to sample_size samples
            return dataset
        except Exception as e:
            print(f"Error loading dataset '{dataset_name}': {e}. Retrying in {delay} seconds... [Attempt {attempt}/{retries}]")
            time.sleep(delay)
            delay *= 2  # Exponential backoff
    raise Exception(f"Failed to load dataset '{dataset_name}' after {retries} attempts.")

# Load datasets using streaming
datasets = []

# open-web-math/open-web-math
datasets.append(get_streaming_dataset("open-web-math/open-web-math", "train", text_field="text", sample_size=100))

# tau/commonsense_qa (twice as per SAMPLE CODE)
datasets.append(get_streaming_dataset("tau/commonsense_qa", "train", text_field="question", sample_size=100))
datasets.append(get_streaming_dataset("tau/commonsense_qa", "train", text_field="question", sample_size=100))

# VMware/open-instruct
datasets.append(get_streaming_dataset("VMware/open-instruct", "train", text_field=None, instruction_field="instruction", response_field="response", sample_size=100))

# Train the tokenizer
tokenizer_obj = train_tokenizer(datasets, vocab_size=8000, special_tokens=special_tokens)

# Saving the tokenizer
os.makedirs('./enhanced-transformer', exist_ok=True)
tokenizer_obj.save('./enhanced-transformer/tokenizer.json')

# Load the tokenizer using PreTrainedTokenizer
class CustomTokenizer(PreTrainedTokenizer):
    def __init__(self, tokenizer_obj):
        self._tokenizer = tokenizer_obj  # Set before calling super().__init__()
        super().__init__(unk_token="[UNK]", pad_token="[PAD]")

    def _tokenize(self, text):
        return self._tokenizer.encode(text).tokens

    def _convert_token_to_id(self, token):
        return self._tokenizer.token_to_id(token)

    def _convert_id_to_token(self, index):
        return self._tokenizer.id_to_token(index)

    def get_vocab(self):
        return self._tokenizer.get_vocab()

# Initialize the tokenizer
tokenizer = CustomTokenizer(tokenizer_obj)

# Set model max length
tokenizer.model_max_length = 512

# ================================
# Dataset Preparation
# ================================

# Combine datasets into one iterable dataset
def interleave_datasets(datasets):
    iterators = [iter(dataset) for dataset in datasets]
    while True:
        for it in list(iterators):  # use list to avoid "RuntimeError: Set changed size during iteration" if iterator is removed
            try:
                yield next(it)
            except StopIteration:
                iterators.remove(it)
                if not iterators:
                    return

combined_iterable_dataset = IterableDataset.from_generator(lambda: interleave_datasets(datasets))

# Tokenize dataset
def tokenize_function(examples):
    encoded = tokenizer(examples['text'], truncation=True, max_length=tokenizer.model_max_length)
    return {'input_ids': encoded['input_ids']}

tokenized_dataset = combined_iterable_dataset.map(tokenize_function, remove_columns=['text'])

# Data collator
class CustomDataCollator:
    def __init__(self, pad_token_id):
        self.pad_token_id = pad_token_id

    def __call__(self, batch):
        max_length = max(len(example['input_ids']) for example in batch)
        input_ids = []
        labels = []
        for example in batch:
            ids = example['input_ids']
            padding_length = max_length - len(ids)
            input_ids.append(ids + [self.pad_token_id] * padding_length)
            labels.append(ids + [self.pad_token_id] * padding_length)

        input_ids = torch.tensor(input_ids, dtype=torch.long)
        labels = torch.tensor(labels, dtype=torch.long)
        return {'input_ids': input_ids, 'labels': labels}

pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
data_collator = CustomDataCollator(pad_token_id=pad_token_id)

# ================================
# Training Enhancements
# ================================

# Enhanced Transformer Configuration with Self-Awareness Enabled
config = EnhancedTransformerConfig(
    vocab_size=len(tokenizer.get_vocab()),
    d_model=768,
    num_layers=12,
    num_heads=12,
    d_ff=3072,
    max_seq_len=512,
    memory_size=512,
    adapter_size=64,
    dropout=0.1,
    self_awareness=True  # Enable self-awareness for consciousness
)

# Initialize Conscious Transformer Model
conscious_model = ConsciousTransformerLMWrapper(config)

# Initialize model
conscious_model.model.to(device)
conscious_model.model.train()
model_size = sum(t.numel() for t in conscious_model.model.parameters())
print(f"Model size: {model_size/1e6:.2f}M parameters")

# Training arguments
training_args = TrainingArguments(
    output_dir='./enhanced-transformer',
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,        # Reduced batch size
    gradient_accumulation_steps=2,        # Accumulate gradients over 2 steps
    save_steps=10_000,
    save_total_limit=2,
    prediction_loss_only=True,
    logging_steps=500,
    logging_first_step=True,
    seed=random_seed,
    disable_tqdm=False,
    fp16=torch.cuda.is_available(),       # Enable mixed precision only if CUDA is available
    dataloader_pin_memory=False,
    max_steps=100_000,                    # Adjust as needed
)

# Define a custom Trainer to incorporate self-reflection and advanced training techniques
class CustomTrainer(Trainer):
    def __init__(self, *args, scaler=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.scaler = scaler
    
    def training_step(self, model, inputs):
        model.train()
        inputs = self._prepare_inputs(inputs)

        with autocast(enabled=self.scaler is not None):
            outputs = model(**inputs)
            loss = outputs['loss']

        if self.scaler is not None:
            self.scaler.scale(loss).backward()
        else:
            loss.backward()

        return loss.detach()

    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure, 
                       on_tpu, using_native_amp, using_lbfgs):
        if self.scaler is not None:
            self.scaler.step(optimizer)
            self.scaler.update()
        else:
            optimizer.step()
        optimizer.zero_grad()

# Initialize scaler for mixed precision
scaler = GradScaler() if torch.cuda.is_available() else None

# ================================
# Training the Model
# ================================

# Train the model
conscious_model.train_model(tokenized_dataset, training_args, tokenizer, scaler=scaler)" from errors: "Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 114/114 [00:00<00:00, 166.28it/s]
[00:00:04] Pre-processing sequences       ████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ 0        /        0Model size: 104.87M parameters
max_steps is given, it will override any value given in num_train_epochs
  0%|                                                                                                                                                  | 0/100000 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/Users/netanelmichael/Desktop/qstar/run.py", line 541, in <module>
    conscious_model.train_model(tokenized_dataset, training_args, tokenizer, scaler=scaler)
  File "/Users/netanelmichael/Desktop/qstar/run.py", line 292, in train_model
    trainer.train()
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/transformers/trainer.py", line 1885, in train
    return inner_training_loop(
           ^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/transformers/trainer.py", line 2216, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/Desktop/qstar/run.py", line 514, in training_step
    outputs = model(**inputs)
              ^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/Desktop/qstar/run.py", line 266, in forward
    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/torch/nn/modules/loss.py", line 1185, in forward
    return F.cross_entropy(input, target, weight=self.weight,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/netanelmichael/miniconda3/lib/python3.12/site-packages/torch/nn/functional.py", line 3086, in cross_entropy
    return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected input batch_size (3216) to match target batch_size (1168).
  0%|                                                                                       "