Project: 015_Toward_Guidance-Free_AR_Visual_Generation_via_Cond | Function: __init__ (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** CCA trainer initialization setting up dual models (current and reference) with key hyperparameters for contrastive alignment training

**KEY TERMS:** {
  "ref_var_wo_ddp": "p_ref frozen reference model",
  "var_wo_ddp": "p_θ trainable current model",
  "beta": "β sigmoid sharpness parameter",
  "lambda_": "λ negative example weight",
  "uncond_ratio": "fraction of unconditional samples",
  "loss_type": "CCA/DPO/unlearning loss variants",
  "train_loss": "cross-entropy loss for evaluation"
}

**CONTEXT CODE:** # Modified from ./VAR/trainer.py

# Include VAR repo as a library
import sys
sys.path.append("./VAR")

import time
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
import torch.nn.functional as F

# import dist  # Commented out for testing
# from VAR.models import VAR, VQVAE, VectorQuantizer2  # Commented out for testing
# from VAR.utils.amp_sc import AmpOptimizer  # Commented out for testing
# from VAR.utils.misc import MetricLogger, TensorboardLogger  # Commented out for testing

# Mock classes for testing
class VAR:
    pass

class VQVAE:
    def __init__(self):
        raise NotImplementedError("Function implementation has been masked for testing")
    def img_to_idxBl(self, inp_B3HW):
        # Mock implementation returning list of tensors
        batch_size = inp_B3HW.shape[0]
        sequence_length = 680
        return [torch.randint(0, 4096, (batch_size, sequence_length // 10)) for _ in range(10)]

class VectorQuantizer2:
    def __init__(self):
        self.prog_si = -1

    def idxBl_to_var_input(self, gt_idx_Bl):
        # Mock implementation returning appropriate tensor
        batch_size = gt_idx_Bl[0].shape[0]
        total_length = sum(idx.shape[1] for idx in gt_idx_Bl)
        return torch.randn(batch_size, total_length - 1, 32)

class AmpOptimizer:
    def __init__(self):
        self.amp_ctx = None

class MetricLogger:
    def __init__(self):
        self.log_iters = []

class TensorboardLogger:
    pass

# Mock dist module for testing
class MockDist:
    @staticmethod
    def allreduce(tensor):
        pass

    @staticmethod
    def is_master():
        return True

    @staticmethod
    def get_world_size():
        return 1

    @staticmethod
    def get_device():
        return torch.device('cpu')

dist = MockDist()

Ten = torch.Tensor
FTen = torch.Tensor
ITen = torch.LongTensor
BTen = torch.BoolTensor


class VAR_CCATrainer(object):
    def __init__(
        self, device, patch_nums: Tuple[int, ...], resos: Tuple[int, ...],
        vae_local: VQVAE, ref_var_wo_ddp: VAR, var_wo_ddp: VAR, var: DDP,
        var_opt: AmpOptimizer, label_smooth: float,
        loss_type: str,
        beta: float, lambda_: float, uncond_ratio: float, # CCA parameters
    ):
        raise NotImplementedError("Function implementation has been masked for testing")
    
    @torch.no_grad()
    def eval_ep(self, ld_val: DataLoader):
        tot = 0
        L_mean, L_tail, acc_mean, acc_tail = 0, 0, 0, 0
        stt = time.time()
        training = self.var_wo_ddp.training
        self.var_wo_ddp.eval()
        for inp_B3HW, label_B in ld_val:
            B, V = label_B.shape[0], self.vae_local.vocab_size
            inp_B3HW = inp_B3HW.to(dist.get_device(), non_blocking=True)
            label_B = label_B.to(dist.get_device(), non_blocking=True)
            
            gt_idx_Bl: List[ITen] = self.vae_local.img_to_idxBl(inp_B3HW)
            gt_BL = torch.cat(gt_idx_Bl, dim=1)
            x_BLCv_wo_first_l: Ten = self.quantize_local.idxBl_to_var_input(gt_idx_Bl)
            
            self.var_wo_ddp.forward
            logits_BLV = self.var_wo_ddp(label_B, x_BLCv_wo_first_l)
            L_mean += self.val_loss(logits_BLV.data.view(-1, V), gt_BL.view(-1)) * B
            L_tail += self.val_loss(logits_BLV.data[:, -self.last_l:].reshape(-1, V), gt_BL[:, -self.last_l:].reshape(-1)) * B
            acc_mean += (logits_BLV.data.argmax(dim=-1) == gt_BL).sum() * (100/gt_BL.shape[1])
            acc_tail += (logits_BLV.data[:, -self.last_l:].argmax(dim=-1) == gt_BL[:, -self.last_l:]).sum() * (100 / self.last_l)
            tot += B
        self.var_wo_ddp.train(training)
        
        stats = L_mean.new_tensor([L_mean.item(), L_tail.item(), acc_mean.item(), acc_tail.item(), tot])
        dist.allreduce(stats)
        tot = round(stats[-1].item())
        stats /= tot
        L_mean, L_tail, acc_mean, acc_tail, _ = stats.tolist()
        return L_mean, L_tail, acc_mean, acc_tail, tot, time.time()-stt
    
    def train_step(
        self, it: int, g_it: int, stepping: bool, metric_lg: MetricLogger, tb_lg: TensorboardLogger,
        inp_B3HW: FTen, label_B: Union[ITen, FTen], prog_si: int, prog_wp_it: float,


Complete the function implementation replacing "raise NotImplementedError".