llama_finetune/unsloth_compiled_cache/UnslothBCOTrainer.py

"""
2025.6.1
2025.6.2
4.52.4
0.18.2
__UNSLOTH_VERSIONING__
"""
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from trl.trainer.bco_trainer import (Any, AutoModelForCausalLM, BCOConfig, BCOTrainer, BaseImageProcessor, CLF_NAME, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RUNNING_NAME, RunningMoments, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _process_tokens, _tokenize, amp, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_joblib_available, is_peft_available, is_sklearn_available, is_wandb_available, itemgetter, log_table_to_comet_experiment, logger, maybe_apply_chat_template, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_deepspeed, prepare_model_for_kbit_training, random, textwrap, torch, tqdm, transformers, version, warnings, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch)


import os
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling

torch_compile_options = {
    "epilogue_fusion"   : True,
    "max_autotune"      : False,
    "shape_padding"     : True,
    "trace.enabled"     : False,
    "triton.cudagraphs" : False,
}

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def selective_log_softmax(logits, index):
    logits = logits.to(torch.float32)
    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
    # loop to reduce peak mem consumption
    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
    logsumexp_values = torch.logsumexp(logits, dim = -1)
    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
    return per_token_logps
@dataclass
class UnslothBCOConfig(BCOConfig):
    """
    
    Configuration class for the [`BCOTrainer`].

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
            to use the default data collator.
        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
            Maximum length of the prompt. This argument is required if you want to use the default data collator.
        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
            Maximum length of the completion. This argument is required if you want to use the default data collator
            and your model is an encoder-decoder.
        beta (`float`, *optional*, defaults to `0.1`):
            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
            reference model.
        label_pad_token_id (`int`,  *optional*, defaults to `-100`):
            Label pad token id. This argument is required if you want to use the default data collator.
        padding_value (`int` or `None`, *optional*, defaults to `None`):
            Padding value to use. If `None`, the padding value of the tokenizer is used.
        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
            This argument is required if you want to use the default data collator.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model and reference model.
        generate_during_eval (`bool`, *optional*, defaults to `False`):
            If `True`, generates and logs completions from both the model and the reference model to W&B or Comet during
            evaluation.
        is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
            you need to specify if the model returned by the callable is an encoder-decoder model.
        precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
            Whether to precompute reference model log probabilities for training and evaluation datasets. This is
            useful when training without the reference model to reduce the total GPU memory needed.
        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
            string.
        ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model
            from a string.
        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
            Number of processes to use for processing the dataset.
        prompt_sample_size (`int`, *optional*, defaults to `1024`):
            Number of prompts that are fed to density ratio classifier.
        min_density_ratio (`float`, *optional*, defaults to `0.5`):
            Minimum value of the density ratio. The estimated density ratio is clamped to this value.
        max_density_ratio (`float`, *optional*, defaults to `10.0`):
            Maximum value of the density ratio. The estimated density ratio is clamped to this value.
    
    """
    vllm_sampling_params: Optional[Any] = field(
        default = None,
        metadata = {'help': 'vLLM SamplingParams'},
    )
    unsloth_num_chunks : Optional[int] = field(
        default = -1,
        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
    )
    def __init__(
        self,
        output_dir = None,
        overwrite_output_dir = None,
        do_train = False,
        do_eval = False,
        do_predict = False,
        eval_strategy = 'no',
        prediction_loss_only = False,
        per_device_train_batch_size = 4,
        per_device_eval_batch_size = 4,
        per_gpu_train_batch_size = None,
        per_gpu_eval_batch_size = None,
        gradient_accumulation_steps = 2,
        eval_accumulation_steps = 2,
        eval_delay = 0,
        torch_empty_cache_steps = 250,
        learning_rate = 5e-05,
        weight_decay = 0.01,
        adam_beta1 = 0.9,
        adam_beta2 = 0.999,
        adam_epsilon = 1e-08,
        max_grad_norm = 1.0,
        num_train_epochs = 3.0,
        max_steps = -1,
        lr_scheduler_type = 'linear',
        warmup_ratio = 0.1,
        warmup_steps = 0,
        log_level = 'passive',
        log_level_replica = 'warning',
        log_on_each_node = True,
        logging_dir = None,
        logging_strategy = 'steps',
        logging_first_step = False,
        logging_steps = 1,
        logging_nan_inf_filter = False,
        save_strategy = 'steps',
        save_steps = 500,
        save_total_limit = None,
        save_safetensors = True,
        save_on_each_node = False,
        save_only_model = False,
        restore_callback_states_from_checkpoint = False,
        no_cuda = False,
        use_cpu = False,
        use_mps_device = False,
        seed = 3407,
        data_seed = 3407,
        jit_mode_eval = False,
        use_ipex = False,
        bf16 = False,
        fp16 = False,
        fp16_opt_level = 'O1',
        half_precision_backend = 'auto',
        bf16_full_eval = False,
        fp16_full_eval = False,
        tf32 = None,
        local_rank = -1,
        ddp_backend = None,
        tpu_num_cores = None,
        tpu_metrics_debug = False,
        debug = '',
        dataloader_drop_last = False,
        eval_steps = None,
        dataloader_num_workers = 0,
        dataloader_prefetch_factor = None,
        past_index = -1,
        run_name = None,
        disable_tqdm = None,
        remove_unused_columns = True,
        label_names = None,
        load_best_model_at_end = False,
        metric_for_best_model = None,
        greater_is_better = None,
        ignore_data_skip = False,
        fsdp = '',
        fsdp_min_num_params = 0,
        fsdp_config = None,
        fsdp_transformer_layer_cls_to_wrap = None,
        accelerator_config = None,
        deepspeed = None,
        label_smoothing_factor = 0.0,
        optim = 'adamw_8bit',
        optim_args = None,
        adafactor = False,
        group_by_length = False,
        length_column_name = 'length',
        report_to = None,
        ddp_find_unused_parameters = None,
        ddp_bucket_cap_mb = None,
        ddp_broadcast_buffers = None,
        dataloader_pin_memory = True,
        dataloader_persistent_workers = False,
        skip_memory_metrics = True,
        use_legacy_prediction_loop = False,
        push_to_hub = False,
        resume_from_checkpoint = None,
        hub_model_id = None,
        hub_strategy = 'every_save',
        hub_token = None,
        hub_private_repo = None,
        hub_always_push = False,
        gradient_checkpointing = False,
        gradient_checkpointing_kwargs = None,
        include_inputs_for_metrics = False,
        eval_do_concat_batches = True,
        fp16_backend = 'auto',
        push_to_hub_model_id = None,
        push_to_hub_organization = None,
        push_to_hub_token = None,
        mp_parameters = '',
        auto_find_batch_size = False,
        full_determinism = False,
        torchdynamo = None,
        ray_scope = 'last',
        ddp_timeout = 1800,
        torch_compile = False,
        torch_compile_backend = None,
        torch_compile_mode = None,
        include_tokens_per_second = False,
        include_num_input_tokens_seen = False,
        neftune_noise_alpha = None,
        optim_target_modules = None,
        batch_eval_metrics = False,
        eval_on_start = False,
        use_liger_kernel = False,
        eval_use_gather_object = False,
        average_tokens_across_devices = False,
        max_length = 1024,
        max_prompt_length = 512,
        max_completion_length = None,
        beta = 0.1,
        label_pad_token_id = -100,
        padding_value = None,
        truncation_mode = 'keep_end',
        disable_dropout = True,
        generate_during_eval = False,
        is_encoder_decoder = None,
        precompute_ref_log_probs = False,
        model_init_kwargs = None,
        ref_model_init_kwargs = None,
        dataset_num_proc = None,
        prompt_sample_size = 1024,
        min_density_ratio = 0.5,
        max_density_ratio = 10.0,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        **kwargs,
    ):
        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
            output_dir = 'unsloth_training_checkpoints'
            save_strategy = 'no'
        if dataset_num_proc is None:
            from multiprocessing import cpu_count
            dataset_num_proc = cpu_count()
        
        super().__init__(
            output_dir = output_dir,
            overwrite_output_dir = overwrite_output_dir,
            do_train = do_train,
            do_eval = do_eval,
            do_predict = do_predict,
            eval_strategy = eval_strategy,
            prediction_loss_only = prediction_loss_only,
            per_device_train_batch_size = per_device_train_batch_size,
            per_device_eval_batch_size = per_device_eval_batch_size,
            per_gpu_train_batch_size = per_gpu_train_batch_size,
            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
            gradient_accumulation_steps = gradient_accumulation_steps,
            eval_accumulation_steps = eval_accumulation_steps,
            eval_delay = eval_delay,
            torch_empty_cache_steps = torch_empty_cache_steps,
            learning_rate = learning_rate,
            weight_decay = weight_decay,
            adam_beta1 = adam_beta1,
            adam_beta2 = adam_beta2,
            adam_epsilon = adam_epsilon,
            max_grad_norm = max_grad_norm,
            num_train_epochs = num_train_epochs,
            max_steps = max_steps,
            lr_scheduler_type = lr_scheduler_type,
            warmup_ratio = warmup_ratio,
            warmup_steps = warmup_steps,
            log_level = log_level,
            log_level_replica = log_level_replica,
            log_on_each_node = log_on_each_node,
            logging_dir = logging_dir,
            logging_strategy = logging_strategy,
            logging_first_step = logging_first_step,
            logging_steps = logging_steps,
            logging_nan_inf_filter = logging_nan_inf_filter,
            save_strategy = save_strategy,
            save_steps = save_steps,
            save_total_limit = save_total_limit,
            save_safetensors = save_safetensors,
            save_on_each_node = save_on_each_node,
            save_only_model = save_only_model,
            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
            no_cuda = no_cuda,
            use_cpu = use_cpu,
            use_mps_device = use_mps_device,
            seed = seed,
            data_seed = data_seed,
            jit_mode_eval = jit_mode_eval,
            use_ipex = use_ipex,
            bf16 = bf16,
            fp16 = fp16,
            fp16_opt_level = fp16_opt_level,
            half_precision_backend = half_precision_backend,
            bf16_full_eval = bf16_full_eval,
            fp16_full_eval = fp16_full_eval,
            tf32 = tf32,
            local_rank = local_rank,
            ddp_backend = ddp_backend,
            tpu_num_cores = tpu_num_cores,
            tpu_metrics_debug = tpu_metrics_debug,
            debug = debug,
            dataloader_drop_last = dataloader_drop_last,
            eval_steps = eval_steps,
            dataloader_num_workers = dataloader_num_workers,
            dataloader_prefetch_factor = dataloader_prefetch_factor,
            past_index = past_index,
            run_name = run_name,
            disable_tqdm = disable_tqdm,
            remove_unused_columns = remove_unused_columns,
            label_names = label_names,
            load_best_model_at_end = load_best_model_at_end,
            metric_for_best_model = metric_for_best_model,
            greater_is_better = greater_is_better,
            ignore_data_skip = ignore_data_skip,
            fsdp = fsdp,
            fsdp_min_num_params = fsdp_min_num_params,
            fsdp_config = fsdp_config,
            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
            accelerator_config = accelerator_config,
            deepspeed = deepspeed,
            label_smoothing_factor = label_smoothing_factor,
            optim = optim,
            optim_args = optim_args,
            adafactor = adafactor,
            group_by_length = group_by_length,
            length_column_name = length_column_name,
            report_to = report_to,
            ddp_find_unused_parameters = ddp_find_unused_parameters,
            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
            ddp_broadcast_buffers = ddp_broadcast_buffers,
            dataloader_pin_memory = dataloader_pin_memory,
            dataloader_persistent_workers = dataloader_persistent_workers,
            skip_memory_metrics = skip_memory_metrics,
            use_legacy_prediction_loop = use_legacy_prediction_loop,
            push_to_hub = push_to_hub,
            resume_from_checkpoint = resume_from_checkpoint,
            hub_model_id = hub_model_id,
            hub_strategy = hub_strategy,
            hub_token = hub_token,
            hub_private_repo = hub_private_repo,
            hub_always_push = hub_always_push,
            gradient_checkpointing = gradient_checkpointing,
            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
            include_inputs_for_metrics = include_inputs_for_metrics,
            eval_do_concat_batches = eval_do_concat_batches,
            fp16_backend = fp16_backend,
            push_to_hub_model_id = push_to_hub_model_id,
            push_to_hub_organization = push_to_hub_organization,
            push_to_hub_token = push_to_hub_token,
            mp_parameters = mp_parameters,
            auto_find_batch_size = auto_find_batch_size,
            full_determinism = full_determinism,
            torchdynamo = torchdynamo,
            ray_scope = ray_scope,
            ddp_timeout = ddp_timeout,
            torch_compile = torch_compile,
            torch_compile_backend = torch_compile_backend,
            torch_compile_mode = torch_compile_mode,
            include_tokens_per_second = include_tokens_per_second,
            include_num_input_tokens_seen = include_num_input_tokens_seen,
            neftune_noise_alpha = neftune_noise_alpha,
            optim_target_modules = optim_target_modules,
            batch_eval_metrics = batch_eval_metrics,
            eval_on_start = eval_on_start,
            use_liger_kernel = use_liger_kernel,
            eval_use_gather_object = eval_use_gather_object,
            average_tokens_across_devices = average_tokens_across_devices,
            max_length = max_length,
            max_prompt_length = max_prompt_length,
            max_completion_length = max_completion_length,
            beta = beta,
            label_pad_token_id = label_pad_token_id,
            padding_value = padding_value,
            truncation_mode = truncation_mode,
            disable_dropout = disable_dropout,
            generate_during_eval = generate_during_eval,
            is_encoder_decoder = is_encoder_decoder,
            precompute_ref_log_probs = precompute_ref_log_probs,
            model_init_kwargs = model_init_kwargs,
            ref_model_init_kwargs = ref_model_init_kwargs,
            dataset_num_proc = dataset_num_proc,
            prompt_sample_size = prompt_sample_size,
            min_density_ratio = min_density_ratio,
            max_density_ratio = max_density_ratio,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
pass

class _UnslothBCOTrainer(Trainer):
    r""""""

    _tag_names = ["trl", "bco"]

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module, str] = None,
        ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
        args: BCOConfig = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        data_collator: Optional[DataCollator] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional[dict] = None,
        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
        model_adapter_name: Optional[str] = None,
        ref_adapter_name: Optional[str] = None,
        embedding_func: Optional[Callable] = None,
        embedding_tokenizer: Optional[PreTrainedTokenizerBase] = None,
    ):
        if embedding_func is not None and not (is_sklearn_available() and is_joblib_available()):
            raise ImportError(
                "BCOTrainer with UDM requires the scikit-learn and joblib libraries. Please install it with `pip install scikit-learn joblib`."
            )

        if type(args) is TrainingArguments:
            raise ValueError("Please use `BCOConfig` instead `TrainingArguments`.")

        if not isinstance(model, str) and model is not None and ref_model is model:
            raise ValueError(
                "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
                "same as `model`, you must mass a copy of it, or `None` if you use peft."
            )

        if args.model_init_kwargs is None:
            model_init_kwargs = {}
        elif not isinstance(model, str):
            raise ValueError("You passed model_kwargs to the BCOTrainer. But your model is already instantiated.")
        else:
            model_init_kwargs = args.model_init_kwargs
            torch_dtype = model_init_kwargs.get("torch_dtype")
            if torch_dtype is not None:
                # Convert to `torch.dtype` if an str is passed
                if isinstance(torch_dtype, str) and torch_dtype != "auto":
                    torch_dtype = getattr(torch, torch_dtype)
                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
                    raise ValueError(
                        f"Invalid `torch_dtype` passed to the BCOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
                    )
                model_init_kwargs["torch_dtype"] = torch_dtype

        if args.ref_model_init_kwargs is None:
            ref_model_init_kwargs = {}
        elif not isinstance(ref_model, str):
            raise ValueError(
                "You passed ref_model_kwargs to the BCOTrainer. But your ref_model is already instantiated."
            )
        else:
            ref_model_init_kwargs = args.ref_model_init_kwargs
            torch_dtype = ref_model_init_kwargs.get("torch_dtype")
            if torch_dtype is not None:
                # Convert to `torch.dtype` if an str is passed
                if isinstance(torch_dtype, str) and torch_dtype != "auto":
                    torch_dtype = getattr(torch, torch_dtype)
                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
                    raise ValueError(
                        f"Invalid `torch_dtype` passed to the BCOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
                    )
                ref_model_init_kwargs["torch_dtype"] = torch_dtype

        if isinstance(model, str):
            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)

        if isinstance(ref_model, str):
            ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)

        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
        # has been called in order to properly call autocast if needed.
        self._peft_has_been_casted_to_bf16 = False

        if not is_peft_available() and peft_config is not None:
            raise ValueError(
                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
            )
        elif is_peft_available() and peft_config is not None:
            # if model is a peft model and we have a peft_config, we merge and unload it first
            if isinstance(model, PeftModel):
                model = model.merge_and_unload()

            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
                _support_gc_kwargs = hasattr(
                    args, "gradient_checkpointing_kwargs"
                ) and "gradient_checkpointing_kwargs" in list(
                    inspect.signature(prepare_model_for_kbit_training).parameters
                )

                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}

                if _support_gc_kwargs:
                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs

                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
            elif getattr(args, "gradient_checkpointing", False):
                # For backward compatibility with older versions of transformers
                if hasattr(model, "enable_input_require_grads"):
                    model.enable_input_require_grads()
                else:

                    def make_inputs_require_grad(module, input, output):
                        output.requires_grad_(True)

                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

            # get peft model with the given config
            model = model
            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
                peft_module_casting_to_bf16(model)
                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
                self._peft_has_been_casted_to_bf16 = True

        # For models that use gradient_checkpointing, we need to attach a hook that enables input
        # to explicitly have `requires_grad=True`, otherwise training will either silently
        # fail or completely fail.
        elif getattr(args, "gradient_checkpointing", False):
            # For backward compatibility with older versions of transformers
            if hasattr(model, "enable_input_require_grads"):
                model.enable_input_require_grads()
            else:

                def make_inputs_require_grad(module, input, output):
                    output.requires_grad_(True)

                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
            raise ValueError(
                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
                " Please install `wandb` or `comet-ml` to resolve."
            )

        if model is not None:
            self.is_encoder_decoder = model.config.is_encoder_decoder
        elif args.is_encoder_decoder is None:
            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
        else:
            self.is_encoder_decoder = args.is_encoder_decoder

        self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
        self.model_adapter_name = model_adapter_name
        self.ref_adapter_name = ref_adapter_name

        if ref_model:
            self.ref_model = ref_model
        elif self.is_peft_model or args.precompute_ref_log_probs:
            # The `model` with adapters turned off will be used as the reference model
            self.ref_model = None
        else:
            self.ref_model = create_reference_model(model)

        if processing_class is None:
            raise ValueError(
                "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
            )
        if args.max_length is None:
            warnings.warn(
                "When using DPODataCollatorWithPadding, you should set `max_length` in the `BCOConfig`. "
                "It will be set to `512` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_length = 512
        if args.max_length is not None:
            max_length = args.max_length

        if args.max_prompt_length is None:
            warnings.warn(
                "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the `BCOConfig`. "
                "It will be set to `128` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_prompt_length = 128
        if args.max_prompt_length is not None:
            max_prompt_length = args.max_prompt_length

        max_completion_length = None
        if args.max_completion_length is None and self.is_encoder_decoder:
            warnings.warn(
                "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the BCOTrainer's init"
                " it will be set to `128` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_completion_length = 128
        if args.max_completion_length is not None and self.is_encoder_decoder:
            max_completion_length = args.max_completion_length

        if data_collator is None:
            data_collator = DPODataCollatorWithPadding(
                pad_token_id=processing_class.pad_token_id,
                label_pad_token_id=args.label_pad_token_id,
                is_encoder_decoder=self.is_encoder_decoder,
            )

            if args.remove_unused_columns:
                args.remove_unused_columns = False
                # warn users
                warnings.warn(
                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your BCOConfig"
                    " we have set it for you, but you should do it yourself in the future.",
                    UserWarning,
                )

            self.use_dpo_data_collator = True
        else:
            self.use_dpo_data_collator = False

        # Disable dropout in the model and reference model
        if args.disable_dropout:
            disable_dropout_in_model(model)
            if self.ref_model is not None:
                disable_dropout_in_model(self.ref_model)

        self.max_length = max_length
        self.generate_during_eval = args.generate_during_eval
        self.label_pad_token_id = args.label_pad_token_id
        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
        self.max_prompt_length = max_prompt_length
        self.truncation_mode = args.truncation_mode
        self.max_completion_length = max_completion_length
        self.precompute_ref_log_probs = args.precompute_ref_log_probs

        # Since ref_logs are precomputed on the first call to get_train/eval_dataloader
        # keep track of first called to avoid computation of future calls
        self._precomputed_train_ref_log_probs = False
        self._precomputed_eval_ref_log_probs = False

        # metric
        self._stored_metrics = defaultdict(lambda: defaultdict(list))

        # BCO parameter
        self.beta = args.beta
        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
            warnings.warn(
                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
                "loss.",
                UserWarning,
            )

        # Underlying Distribution Matching argument
        self.embedding_func = embedding_func
        self.embedding_tokenizer = embedding_tokenizer

        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
        # input tensor associated with the key "input_ids". However, in BCO, the sampled data does not include the
        # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
        # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
        # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
        # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
        # issued.
        model.warnings_issued["estimate_tokens"] = True

        with PartialState().main_process_first():
            # Apply the chat template if needed
            train_dataset = train_dataset.map(
                maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc
            )
            if eval_dataset is not None:
                eval_dataset = eval_dataset.map(
                    maybe_apply_chat_template,
                    fn_kwargs={"tokenizer": processing_class},
                    num_proc=args.dataset_num_proc,
                )

            # Tokenize and prepare the training datasets
            train_dataset = train_dataset.map(
                _tokenize,
                batched=True,
                fn_kwargs={"tokenizer": processing_class, "embedding_tokenizer": self.embedding_tokenizer},
                num_proc=args.dataset_num_proc,
                desc="Tokenizing train dataset",
            )

            # Prepare the datasets
            fn_kwargs = {
                "prefix": "",
                "is_encoder_decoder": self.is_encoder_decoder,
                "tokenizer": processing_class,
                "max_length": self.max_length,
                "truncation_mode": self.truncation_mode,
                "label_pad_token_id": self.label_pad_token_id,
                "max_prompt_length": self.max_prompt_length,
                "max_completion_length": self.max_completion_length,
            }
            train_dataset = train_dataset.map(
                _process_tokens,
                fn_kwargs=fn_kwargs,
                num_proc=args.dataset_num_proc,
                desc="Processing tokenized train dataset",
            )

            if eval_dataset is not None:
                # Tokenize
                eval_dataset = eval_dataset.map(
                    _tokenize,
                    fn_kwargs={"tokenizer": processing_class, "embedding_tokenizer": self.embedding_tokenizer},
                    batched=True,
                    num_proc=args.dataset_num_proc,
                    desc="Tokenizing eval dataset",
                )

                # Process
                fn_kwargs = {
                    "prefix": "",
                    "is_encoder_decoder": self.is_encoder_decoder,
                    "tokenizer": processing_class,
                    "max_length": self.max_length,
                    "truncation_mode": self.truncation_mode,
                    "label_pad_token_id": self.label_pad_token_id,
                    "max_prompt_length": self.max_prompt_length,
                    "max_completion_length": self.max_completion_length,
                }
                eval_dataset = eval_dataset.map(
                    _process_tokens,
                    fn_kwargs=fn_kwargs,
                    num_proc=args.dataset_num_proc,
                    desc="Processing tokenized eval dataset",
                )

            desirable = train_dataset.filter(
                lambda x: x["label"], num_proc=args.dataset_num_proc, desc="Filtering desirable examples"
            )
            undesirable = train_dataset.filter(
                lambda x: not x["label"], num_proc=args.dataset_num_proc, desc="Filtering undesirable examples"
            )

        super().__init__(
            model=model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            model_init=model_init,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
        # self.model_accepts_loss_kwargs to False to enable scaling.
        self.model_accepts_loss_kwargs = False

        # Add tags for models that have been loaded with the correct transformers version
        if hasattr(self.model, "add_model_tags"):
            self.model.add_model_tags(self._tag_names)

        if not hasattr(self, "accelerator"):
            raise AttributeError(
                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
            )

        # Deepspeed Zero-3 does not support precompute_ref_log_probs
        if self.is_deepspeed_enabled:
            if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
                raise ValueError(
                    "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
                )

        if self.ref_model is None:
            if not (self.is_peft_model or self.precompute_ref_log_probs):
                raise ValueError(
                    "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
                )
        else:
            if self.is_deepspeed_enabled:
                self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
            else:
                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

        self.running = RunningMoments(accelerator=self.accelerator)

        if self.embedding_func is None or args.resume_from_checkpoint:
            return

        chosen_embeddings = self._get_sample_prompt_embeddings(desirable, sample_size=self.args.prompt_sample_size)
        rejected_embeddings = self._get_sample_prompt_embeddings(undesirable, sample_size=self.args.prompt_sample_size)

        embeddings = torch.cat((chosen_embeddings, rejected_embeddings), dim=0)
        labels = torch.cat(
            (torch.ones_like(chosen_embeddings[:, 0]), torch.zeros_like(rejected_embeddings[:, 0])), dim=0
        )

        self.clf = LogisticRegression(class_weight="balanced").fit(
            embeddings.cpu().float().numpy(), labels.cpu().numpy()
        )
        chosen_mean = self.clf.score(
            chosen_embeddings.cpu().float().numpy(), torch.ones_like(chosen_embeddings[:, 0]).cpu().numpy()
        )
        rejected_mean = self.clf.score(
            rejected_embeddings.cpu().float().numpy(), torch.zeros_like(rejected_embeddings[:, 0]).cpu().numpy()
        )
        logger.info(f"UDM classifier training scores: chosen: {chosen_mean}, rejected: {rejected_mean}")

    @property
    def match_underlying_distribution(self):
        return self.embedding_func is not None and self.embedding_tokenizer is not None

    def _get_chosen_prob(self, prompt_embeddings: torch.FloatTensor) -> torch.FloatTensor:
        """
        Calculates the probability if the given prompt embedding is from desirable dataset.
        This function calculates the probability in the process and ensemble across processes.
        """
        dtype = prompt_embeddings.dtype
        device = prompt_embeddings.device
        rank = self.accelerator.process_index

        padded_prompt_embeddings = self.accelerator.pad_across_processes(
            prompt_embeddings, pad_index=self.embedding_tokenizer.pad_token_id
        )
        sample_size = padded_prompt_embeddings.shape[0]
        nonzero = padded_prompt_embeddings.mean(dim=1) != self.embedding_tokenizer.pad_token_id
        prompt_embeddings = self.accelerator.gather(padded_prompt_embeddings)

        # cannot predict for all empty values
        if prompt_embeddings.shape[0] == 0:
            return torch.tensor([], device=device, dtype=dtype)

        prob = self.clf.predict_proba(prompt_embeddings.cpu().float().numpy())[:, 1]
        prob = torch.as_tensor(prob, dtype=dtype, device=device)
        prob = self.accelerator.reduce(prob, reduction="mean")

        prob = prob[sample_size * rank : sample_size * (rank + 1)]
        prob = prob[nonzero]

        return prob

    def _vectorize_prompt(self, input_ids: torch.LongTensor, attention_mask: torch.LongTensor) -> torch.FloatTensor:
        """
        Replaces processing_class.pad_token_id to embedding_tokenizer.pad_token_id
        and applies self.embedding_func
        """
        input_ids = torch.where(
            input_ids == self.processing_class.pad_token_id,
            self.embedding_tokenizer.pad_token_id,
            input_ids,
        )

        with torch.no_grad():
            embeddings = self.embedding_func(
                input_ids=input_ids,
                attention_mask=attention_mask,
            )

        return embeddings

    def _get_prompt_embeddings(
        self, batch: dict[str, Union[list, torch.LongTensor]]
    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
        """Extract embeddings from frozen embedding model"""

        if not self.match_underlying_distribution:
            return None, None

        embeddings = self._vectorize_prompt(
            input_ids=batch["embedding_input_ids"],
            attention_mask=batch["embedding_attention_mask"],
        )

        chosen_idx = [i for i in range(len(batch["label"])) if batch["label"][i] is True]
        rejected_idx = [i for i in range(len(batch["label"])) if batch["label"][i] is False]

        chosen_embeddings = embeddings[chosen_idx, ...]
        rejected_embeddings = embeddings[rejected_idx, ...]

        return (chosen_embeddings, rejected_embeddings)

    def _get_sample_prompt_embeddings(self, dataset: Dataset, sample_size: int = 512) -> torch.FloatTensor:
        """
        Sample instances from dataset and get prompt embeddings.
        Used for density ratio classifier training.
        """
        n_samples = min(len(dataset), sample_size)
        rand_indices = np.random.choice(len(dataset), size=(n_samples,))

        embedding_dataset = dataset.select(rand_indices)

        dataloader_params = {
            "batch_size": self.args.per_device_train_batch_size,
            "collate_fn": self.data_collator,
            "num_workers": self.args.dataloader_num_workers,
            "pin_memory": self.args.dataloader_pin_memory,
            "shuffle": False,
        }

        # prepare dataloader
        data_loader = self.accelerator.prepare(DataLoader(embedding_dataset, **dataloader_params))

        with torch.no_grad():
            all_embeddings = torch.empty(0)
            for padded_batch in tqdm(iterable=data_loader, desc="Building sample prompt embeddings"):
                embeddings = self._vectorize_prompt(
                    input_ids=padded_batch["embedding_input_ids"],
                    attention_mask=padded_batch["embedding_attention_mask"],
                )
                embeddings = self.accelerator.gather_for_metrics(embeddings)
                all_embeddings = torch.cat((all_embeddings, embeddings.cpu()))

        return all_embeddings

    def _save_optimizer_and_scheduler(self, output_dir):
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        super()._save_optimizer_and_scheduler(output_dir)

        if self.accelerator.is_main_process:
            # When saving optimizer and scheduler to checkpoint, save also the running delta object.
            self.running.save_to_json(os.path.join(output_dir, RUNNING_NAME))

            if self.match_underlying_distribution:
                joblib.dump(self.clf, os.path.join(output_dir, CLF_NAME), compress=True)

    def _load_optimizer_and_scheduler(self, checkpoint):
        if checkpoint is None:
            logger.warning_once(f"Missing Checkpoint {checkpoint}")
            return

        super()._load_optimizer_and_scheduler(checkpoint)

        # when loading optimizer and scheduler from checkpoint, also load the running delta object.
        running_file = os.path.join(checkpoint, RUNNING_NAME)
        if os.path.isfile(running_file):
            self.running = RunningMoments.load_from_json(self.accelerator, running_file)

        if self.match_underlying_distribution:
            clf_file = os.path.join(checkpoint, CLF_NAME)
            if os.path.isfile(clf_file):
                self.clf = joblib.load(clf_file)

    @contextmanager
    def null_ref_context(self):
        """Context manager for handling null reference model (that is, peft adapter manipulation)."""
        with (
            self.accelerator.unwrap_model(self.model).disable_adapter()
            if self.is_peft_model and not self.ref_adapter_name
            else nullcontext()
        ):
            if self.ref_adapter_name:
                self.model.set_adapter(self.ref_adapter_name)
            yield
            if self.ref_adapter_name:
                self.model.set_adapter(self.model_adapter_name or "default")

    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training [`~torch.utils.data.DataLoader`].

        Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`.
        """

        if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs:
            dataloader_params = {
                "batch_size": self.args.per_device_train_batch_size,
                "collate_fn": self.data_collator,
                "num_workers": self.args.dataloader_num_workers,
                "pin_memory": self.args.dataloader_pin_memory,
                "shuffle": False,
            }

            # prepare dataloader
            data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params))
            reference_completion_logps = []

            for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"):
                reference_completion_logp = self.compute_reference_log_probs(padded_batch)

                reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
                reference_completion_logps.append(reference_completion_logp.cpu())

            self.train_dataset = self.train_dataset.add_column(
                name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
            )

            self._precomputed_train_ref_log_probs = True

        return super().get_train_dataloader()

    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
        """
        Returns the evaluation [`~torch.utils.data.DataLoader`].

        Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`.

        Args:
            eval_dataset (`torch.utils.data.Dataset`, *optional*):
                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
                by the `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        if eval_dataset is None and self.eval_dataset is None:
            raise ValueError("Trainer: evaluation requires an eval_dataset.")
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset

        if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs:
            dataloader_params = {
                "batch_size": self.args.per_device_eval_batch_size,
                "collate_fn": self.data_collator,
                "num_workers": self.args.dataloader_num_workers,
                "pin_memory": self.args.dataloader_pin_memory,
                "shuffle": False,
            }

            # prepare dataloader
            data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params))

            reference_completion_logps = []

            for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"):
                reference_completion_logp = self.compute_reference_log_probs(padded_batch)

                reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
                reference_completion_logps.append(reference_completion_logp.cpu())

            eval_dataset = eval_dataset.add_column(
                name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
            )

            # Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs
            if self.eval_dataset is not None:
                self.eval_dataset = eval_dataset
            self._precomputed_eval_ref_log_probs = True

        return super().get_eval_dataloader(eval_dataset=eval_dataset)

    def compute_reference_log_probs(self, padded_batch: dict) -> dict:
        """Computes log probabilities of the reference model for a single padded batch of a BCO specific dataset."""
        with torch.no_grad():
            if self.ref_model is None:
                with self.null_ref_context():
                    if self.is_encoder_decoder:
                        completion_logits = self.model(
                            padded_batch["prompt_input_ids"],
                            attention_mask=padded_batch["prompt_attention_mask"],
                            decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
                            labels=padded_batch["completion_labels"],
                        ).logits

                    else:
                        completion_logits = self.model(
                            padded_batch["completion_input_ids"],
                            attention_mask=padded_batch["completion_attention_mask"],
                        ).logits

            else:
                if self.is_encoder_decoder:
                    completion_logits = self.ref_model(
                        padded_batch["prompt_input_ids"],
                        attention_mask=padded_batch["prompt_attention_mask"],
                        decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
                        labels=padded_batch["completion_labels"],
                    ).logits

                else:
                    completion_logits = self.ref_model(
                        padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"]
                    ).logits

        completion_logps = self.get_batch_logps(
            completion_logits,
            padded_batch["completion_labels"],
            average_log_prob=False,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
        )

        return completion_logps

    @staticmethod
    def get_batch_logps(
        logits: torch.FloatTensor,
        labels: torch.LongTensor,
        average_log_prob: bool = False,
        label_pad_token_id: int = -100,
        is_encoder_decoder: bool = False,
    ) -> torch.FloatTensor:
        """Compute the log probabilities of the given labels under the given logits.

        Args:
            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
            labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.

        Returns:
            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
        """
        if logits.shape[:-1] != labels.shape:
            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")

        if not is_encoder_decoder:
            labels = labels[:, 1:].clone()
            logits = logits[:, :-1, :]
        else:
            # Fixes end-dec RuntimeError
            labels = labels.clone()

        loss_mask = labels != label_pad_token_id

        # dummy token; we'll ignore the losses on these tokens later
        labels[labels == label_pad_token_id] = 0

        per_token_logps = selective_log_softmax(logits, labels)

        if average_log_prob:
            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
        else:
            return (per_token_logps * loss_mask).sum(-1)

    def forward(
        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        model_kwargs = (
            {
                "labels": batch["completion_labels"],
                "decoder_input_ids": batch.get("completion_decoder_input_ids"),
            }
            if self.is_encoder_decoder
            else {}
        )
        if self.aux_loss_enabled:
            model_kwargs["output_router_logits"] = True

        outputs = model(
            batch["completion_input_ids"],
            attention_mask=batch["completion_attention_mask"],
            **model_kwargs,
        )
        completion_logits = outputs.logits

        completion_logps = self.get_batch_logps(
            completion_logits,
            batch["completion_labels"],
            average_log_prob=False,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
        )

        if completion_logps.shape[0] != len(batch["label"]):
            raise ValueError(
                "There is a mismatch between the number of examples in this batch and the number of "
                "examples for which an output sequence was predicted."
            )

        chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True]
        rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False]

        chosen_logps = completion_logps[chosen_idx, ...]
        rejected_logps = completion_logps[rejected_idx, ...]

        chosen_logits = completion_logits[chosen_idx, ...]
        rejected_logits = completion_logits[rejected_idx, ...]

        if self.aux_loss_enabled:
            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, outputs.aux_loss)
        else:
            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits)

    def _get_udm_weight(self, rejected_embeddings: torch.FloatTensor) -> torch.FloatTensor:
        prob_desirable = self._get_chosen_prob(rejected_embeddings)
        min_ratio = self.args.min_density_ratio
        max_ratio = self.args.max_density_ratio

        weight = (prob_desirable / (1 - prob_desirable + 1e-8)).clamp(min=min_ratio, max=max_ratio)

        return weight

    def bco_loss(
        self,
        policy_chosen_logps: torch.FloatTensor,
        policy_rejected_logps: torch.FloatTensor,
        reference_chosen_logps: torch.FloatTensor,
        reference_rejected_logps: torch.FloatTensor,
        chosen_embeddings: Optional[torch.FloatTensor],
        rejected_embeddings: Optional[torch.FloatTensor],
        do_train: bool = True,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Compute the BCO loss for a batch of policy and reference model log probabilities.

        Args:
            policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,)
            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,)
            reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,)
            reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in batch_size,)
            chosen_embeddings: embeddings of desirable prompts
            rejected_embeddings: embeddings of undesirable prompts

        Returns:
            A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, delta).
            The losses tensor contains the BCO loss for each example in the batch.
            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
            The delta value contains the moving average of all implicit rewards.
        """

        chosen_logratios = policy_chosen_logps - reference_chosen_logps
        chosen_rewards = self.beta * chosen_logratios

        rejected_logratios = policy_rejected_logps - reference_rejected_logps
        rejected_rewards = self.beta * rejected_logratios

        if do_train:
            self.running.update(torch.cat((chosen_rewards, rejected_rewards), 0).detach())
        delta = torch.as_tensor(self.running.mean, device=chosen_rewards.device)

        chosen_losses = -F.logsigmoid(chosen_rewards - delta)
        rejected_losses = -F.logsigmoid(-(rejected_rewards - delta))

        if self.match_underlying_distribution:
            chosen_weight = torch.ones_like(chosen_losses)
            rejected_weight = self._get_udm_weight(rejected_embeddings)

            losses = torch.cat((chosen_weight * chosen_losses, rejected_weight * rejected_losses), dim=0)
        else:
            losses = torch.cat((chosen_losses, rejected_losses), dim=0)

        return losses, chosen_rewards, rejected_rewards, delta

    def get_batch_loss_metrics(
        self,
        model,
        batch: dict[str, Union[list, torch.LongTensor]],
        do_train: bool = True,
    ):
        """Compute the BCO loss and other metrics for the given batch of inputs for train or test."""
        metrics = {}
        batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}

        forward_output = self.forward(model, batch)
        (
            policy_chosen_logps,
            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
        ) = forward_output[:4]
        if self.aux_loss_enabled:
            aux_loss = forward_output[4]

        # if reference_logps in batch use them, otherwise use the reference model
        if "reference_logps" in batch:
            chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True]
            rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False]

            reference_chosen_logps = batch["reference_logps"][chosen_idx, ...]
            reference_rejected_logps = batch["reference_logps"][rejected_idx, ...]
        else:
            with torch.no_grad():
                if self.ref_model is None:
                    with self.null_ref_context():
                        (
                            reference_chosen_logps,
                            reference_rejected_logps,
                            _,
                            _,
                        ) = self.forward(self.model, batch)[:4]
                else:
                    (
                        reference_chosen_logps,
                        reference_rejected_logps,
                        _,
                        _,
                    ) = self.forward(self.ref_model, batch)[:4]

        chosen_embeddings, rejected_embeddings = self._get_prompt_embeddings(batch)

        losses, chosen_rewards, rejected_rewards, delta = self.bco_loss(
            policy_chosen_logps,
            policy_rejected_logps,
            reference_chosen_logps,
            reference_rejected_logps,
            chosen_embeddings,
            rejected_embeddings,
            do_train=do_train,
        )
        metrics["delta"] = self.accelerator.gather_for_metrics(delta).mean().item()

        num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
        num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)

        all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item()
        all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item()

        if all_num_chosen > 0:
            metrics["rewards/chosen_sum"] = (
                self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item()
            )
            metrics["logps/chosen_sum"] = (
                self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item()
            )
            metrics["logits/chosen_sum"] = (
                self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item()
            )
            metrics["count/chosen"] = all_num_chosen

        if all_num_rejected > 0:
            metrics["rewards/rejected_sum"] = (
                self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item()
            )
            metrics["logps/rejected_sum"] = (
                self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item()
            )
            metrics["logits/rejected_sum"] = (
                self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item()
            )
            metrics["count/rejected"] = all_num_rejected

        loss = losses.nanmean()
        if self.aux_loss_enabled:
            loss += self.aux_loss_coef * aux_loss

        return loss, metrics

    def compute_loss(
        self,
        model: Union[PreTrainedModel, nn.Module],
        inputs: dict[str, Union[torch.Tensor, Any]],
        return_outputs=False,
        num_items_in_batch=None,
    ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
        compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()

        with compute_loss_context_manager:
            loss, metrics = self.get_batch_loss_metrics(model, inputs)

        # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
        loss = loss.to(self.args.device)
        # force log the metrics
        if self.accelerator.is_main_process:
            self.store_metrics(metrics, train_eval="train")

        if return_outputs:
            return (loss, metrics)
        return loss

    def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
        for key, value in metrics.items():
            self._stored_metrics[train_eval][key].append(value)

    def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Optional[torch.utils.data.Sampler]:
        if dataset is None:
            dataset = self.train_dataset
        if dataset is None or not has_length(dataset):
            return None
        return SequentialSampler(dataset)

    def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]:
        """Generate samples from the model and reference model for the given batch of inputs."""

        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
        generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
        with generate_context_manager:
            policy_output = model.generate(
                input_ids=batch["prompt_input_ids"],
                attention_mask=batch["prompt_attention_mask"],
                max_length=self.max_length,
                do_sample=True,
                pad_token_id=self.processing_class.pad_token_id,
            )

            # if reference_output in batch use that otherwise use the reference model
            if "reference_output" in batch:
                reference_output = batch["reference_output"]
            else:
                if self.ref_model is None:
                    with self.null_ref_context():
                        reference_output = self.model.generate(
                            input_ids=batch["prompt_input_ids"],
                            attention_mask=batch["prompt_attention_mask"],
                            max_length=self.max_length,
                            do_sample=True,
                            pad_token_id=self.processing_class.pad_token_id,
                        )
                else:
                    reference_output = self.ref_model.generate(
                        input_ids=batch["prompt_input_ids"],
                        attention_mask=batch["prompt_attention_mask"],
                        max_length=self.max_length,
                        do_sample=True,
                        pad_token_id=self.processing_class.pad_token_id,
                    )

        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)

        reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id)
        reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True)

        return policy_output_decoded, reference_output_decoded

    def prediction_step(
        self,
        model: Union[PreTrainedModel, nn.Module],
        inputs: dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[list[str]] = None,
    ):
        if ignore_keys is None:
            if hasattr(model, "config"):
                ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
            else:
                ignore_keys = []

        prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
        with torch.no_grad(), prediction_context_manager:
            loss, metrics = self.get_batch_loss_metrics(model, inputs, do_train=False)

        # force log the metrics
        if self.accelerator.is_main_process:
            self.store_metrics(metrics, train_eval="eval")

        if prediction_loss_only:
            return (loss.detach(), None, None)

        # logits for the chosen and rejected samples from model
        logits_dict = {}
        if "logits/chosen_sum" in metrics:
            logits_dict["eval_logits/chosen"] = metrics["logits/chosen_sum"]
        if "logits/rejected_sum" in metrics:
            logits_dict["eval_logits/rejected"] = metrics["logits/rejected_sum"]
        logits = [v for k, v in logits_dict.items() if k not in ignore_keys]
        logits = torch.tensor(logits, device=self.accelerator.device)
        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)

        return (loss.detach(), logits, labels)

    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[list[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Overriding built-in evaluation loop to store metrics for each batch.
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """

        # Sample and save to game log if requested (for one batch to save time)
        if self.generate_during_eval:
            # Generate random indices within the range of the total number of samples
            num_samples = len(dataloader.dataset)
            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)

            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
            random_batch_dataset = dataloader.dataset.select(random_indices)
            random_batch = self.data_collator(random_batch_dataset)
            random_batch = self._prepare_inputs(random_batch)

            target_indicies = [i for i in range(len(random_batch["label"])) if random_batch["label"][i] is False]
            target_batch = {
                "prompt_input_ids": random_batch["prompt_input_ids"][target_indicies],
                "prompt_attention_mask": random_batch["prompt_attention_mask"][target_indicies],
                "prompt": itemgetter(*target_indicies)(random_batch["prompt"]),
            }
            policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch)

            table = pd.DataFrame(
                columns=["Prompt", "Policy", "Ref Model"],
                data=[
                    [prompt, pol[len(prompt) :], ref[len(prompt) :]]
                    for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded)
                ],
            )
            if "wandb" in self.args.report_to:
                wandb.log({"game_log": wandb.Table(data=table)})

            if "comet_ml" in self.args.report_to:
                log_table_to_comet_experiment(
                    name="game_log.csv",
                    table=table,
                )

        # Base evaluation
        initial_output = super().evaluation_loop(
            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
        )

        return initial_output

    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
        """
        Log `logs` on the various objects watching training, including stored metrics.

        Args:
            logs (`dict[str, float]`):
                The values to log.
            start_time (`float` or `None`, *optional*, defaults to `None`):
                Start time of the training.
        """
        # logs either has 'loss' or 'eval_loss'
        train_eval = "train" if "loss" in logs else "eval"
        # train metrics should have no prefix, eval should have 'eval_'
        prefix = "eval_" if train_eval == "eval" else ""
        # accumulate average metrics from sums and lengths
        for split in ["chosen", "rejected"]:
            if f"count/{split}" in self._stored_metrics[train_eval]:
                count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item()
                for metric in ["rewards", "logps", "logits"]:
                    logs[f"{prefix}{metric}/{split}"] = (
                        torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item()
                        / count_sum
                    )
                    # delete obsolete metric
                    del self._stored_metrics[train_eval][f"{metric}/{split}_sum"]
                del self._stored_metrics[train_eval][f"count/{split}"]
        # calculate reward margin
        if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs:
            logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
        # Add averaged stored metrics to logs
        for key, metrics in self._stored_metrics[train_eval].items():
            logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item()
        del self._stored_metrics[train_eval]

        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
            return super().log(logs, start_time)
        else:  # transformers<=4.46
            return super().log(logs)

    def create_model_card(
        self,
        model_name: Optional[str] = None,
        dataset_name: Optional[str] = None,
        tags: Union[str, list[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            model_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the model.
            dataset_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the dataset used for training.
            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
                Tags to be associated with the model card.
        """
        if not self.is_world_process_zero():
            return

        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
            base_model = self.model.config._name_or_path
        else:
            base_model = None

        tags = tags or []
        if isinstance(tags, str):
            tags = [tags]

        if hasattr(self.model.config, "unsloth_version"):
            tags.append("unsloth")

        citation = textwrap.dedent("""\
        @article{jung2024binary,
            title        = {{Binary Classifier Optimization for Large Language Model Alignment}},
            author       = {Seungjae Jung and Gunsoo Han and Daniel Wontae Nam and Kyoung{-}Woon On},
            year         = 2024,
            eprint       = {arXiv:2404.04656}
        }""")

        model_card = generate_model_card(
            base_model=base_model,
            model_name=model_name,
            hub_model_id=self.hub_model_id,
            dataset_name=dataset_name,
            tags=tags,
            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="BCO",
            trainer_citation=citation,
            paper_title="Binary Classifier Optimization for Large Language Model Alignment",
            paper_id="2404.04656",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothBCOTrainer(_UnslothBCOTrainer):
    """
    
    Initialize BCOTrainer from [BCO](https://huggingface.co/papers/2404.04656) paper.

    Args:
        model (`transformers.PreTrainedModel`):
            The model to train, preferably an `AutoModelForSequenceClassification`.
        ref_model (`PreTrainedModelWrapper`):
            Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no
            reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.
        args (`BCOConfig`):
            The arguments to use for training.
        train_dataset (`datasets.Dataset`):
            The dataset to use for training.
        eval_dataset (`datasets.Dataset`):
            The dataset to use for evaluation.
        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
            Processing class used to process the data. If provided, will be used to automatically process the inputs
            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
            reuse the fine-tuned model.
        data_collator (`transformers.DataCollator`, *optional*, defaults to `None`):
            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
        model_init (`Callable[[], transformers.PreTrainedModel]`):
            The model initializer to use for training. If None is specified, the default model initializer will be used.
        callbacks (`list[transformers.TrainerCallback]`):
            The callbacks to use for training.
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.
        peft_config (`dict`, defaults to `None`):
            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function to use to compute the metrics. Must take a `EvalPrediction` and return
            a dictionary string to metric values.
        model_adapter_name (`str`, defaults to `None`):
            Name of the train target PEFT adapter, when using LoRA with multiple adapters.
        ref_adapter_name (`str`, defaults to `None`):
            Name of the reference PEFT adapter, when using LoRA with multiple adapters.
    
    """
    def __init__(
        self,
        model = None,
        ref_model = None,
        args = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        data_collator = None,
        model_init = None,
        callbacks = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        compute_metrics = None,
        model_adapter_name = None,
        ref_adapter_name = None,
        embedding_func = None,
        embedding_tokenizer = None,
        **kwargs
    ):
        if args is None: args = UnslothBCOConfig()
        use_bf16 = getattr(args, 'bf16', False)
        use_fp16 = getattr(args, 'fp16', False)
        force_float32 = False
        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
            print('Unsloth: Switching to float32 training since model cannot work with float16')
            force_float32 = True
        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
        dtype = getattr(model.config, 'torch_dtype', None)
        if dtype is None: dtype = model.get_input_embeddings().dtype
        from unsloth_zoo.utils import _get_dtype
        dtype = _get_dtype(dtype)
        float16 = dtype == torch.float16
        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
        if force_float32:
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
            args.fp16 = float16
            args.bf16 = not float16
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
            args.eval_strategy = 'steps'
            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
        if ga_steps is not None and ga_steps > 1:
            from transformers import __version__ as transformers_version
            if Version(transformers_version) <= Version('4.45.2'):
                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
        if getattr(args, 'eval_strategy', 'no') != 'no':
            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
        if force_float32:
            args.bf16_full_eval = False
            args.fp16_full_eval = False
        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
            args.bf16_full_eval = True
            args.fp16_full_eval = False
        elif not bf16_full_eval and not fp16_full_eval:
            args.bf16_full_eval = args.bf16
            args.fp16_full_eval = args.fp16
        _output_logits = False
        if locals().get('compute_metrics', None) is not None: _output_logits = True
        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
        if _output_logits:
            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
            pass
        else:
            model_max_seq_length = getattr(model, 'max_seq_length', None)
            args_max_seq_length  = getattr(args,  'max_seq_length', None)
            if args_max_seq_length is None and model_max_seq_length is not None:
                max_seq_length = model.max_seq_length
                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
        if model is not None and hasattr(model, 'for_training'):
            model.for_training()
        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
        if 'processing_class' in locals():
            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
        if not isinstance(data_collator, UnslothVisionDataCollator):
            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
                data_collator = TransformersDataCollatorForLanguageModeling(__tokenizer, mlm = False, mlm_probability = 0.0)
            elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
                data_collator = DataCollatorForSeq2Seq(__tokenizer)
        else:
            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
        if not isinstance(data_collator, UnslothVisionDataCollator):
            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
                if isinstance(data_collator, DataCollatorForSeq2Seq):
                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
                else:
                    data_collator = TransformersDataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False, mlm_probability = 0.0)
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('bco_trainer', other_metrics)
        
        super().__init__(
            model = model,
            ref_model = ref_model,
            args = args,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            data_collator = data_collator,
            model_init = model_init,
            callbacks = callbacks,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            peft_config = peft_config,
            compute_metrics = compute_metrics,
            model_adapter_name = model_adapter_name,
            ref_adapter_name = ref_adapter_name,
            embedding_func = embedding_func,
            embedding_tokenizer = embedding_tokenizer,**kwargs)
        if hasattr(self, 'neftune_hook_handle'):
            self.neftune_hook_handle.remove()
            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
        if getattr(args, 'neftune_noise_alpha', None) is not None:
            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
        pass
        
pass