"""
2026.2.1
2026.2.1
4.57.6
0.24.0
__UNSLOTH_VERSIONING__
"""

# Unsloth auto generated code
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from unsloth_zoo.temporary_patches.common import torch_compile
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.reward_trainer import (Any, AutoModelForSequenceClassification, AutoTokenizer, BaseTrainer, Callable, DataCollator, DataCollatorForPreference, Dataset, EvalPrediction, IterableDataset, Optional, PartialState, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, RewardConfig, RewardTrainer, TrainerCallback, Union, clone_chat_template, contextlib, dataclass, defaultdict, disable_dropout_in_model, get_act_offloading_ctx_manager, is_conversational, logger, logging, nn, os, pad, re, remove_none_values, suppress_from_pretrained_warning, torch, transformers, Any, AutoModelForSequenceClassification, AutoTokenizer, Callable, DataCollator, DataCollatorForPreference, Dataset, EvalPrediction, IterableDataset, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, RewardConfig, TrainerCallback, Union, clone_chat_template, contextlib, defaultdict, disable_dropout_in_model, get_act_offloading_ctx_manager, logger, os, pad, re, suppress_from_pretrained_warning, torch, transformers, Optional, PreTrainedModel, logger, os, re, 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
import inspect
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
from transformers.training_args import ParallelMode
from unsloth_zoo.device_type import DEVICE_TYPE, device_synchronize

# Wrap trainer with padding to right and enable training mode
# Also patches W&B since multiple runs must use wandb.finish()
import functools
from types import MethodType
try:
    from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers
except:
    def reset_unsloth_gradient_checkpointing_buffers(): pass
def prepare_for_training_mode(f):
    @functools.wraps(f)
    def wrapper(self, *args, **kwargs):
        # Enable training mode
        _was_training = None
        # Get gradient checkpointing setting from training arguments
        use_gc = getattr(self.args, 'gradient_checkpointing', True)
        if hasattr(self, 'model') and hasattr(self.model, "training"):
            _was_training = self.model.training
        if hasattr(self, 'model') and hasattr(self.model, "for_training"):
            self.model.for_training(use_gradient_checkpointing=use_gc)
        output = f(self, *args, **kwargs)
        # Restore previous mode when possible
        if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
            if _was_training is False:
                self.model.for_inference()
            elif _was_training is True and hasattr(self.model, "for_training"):
                self.model.for_training(use_gradient_checkpointing=use_gc)
        # Reset gradient checkpointing buffers to free memory while staying ready for next run
        try:
            reset_unsloth_gradient_checkpointing_buffers()
        except:
            pass
        # Patch W&B to enable logging on future runs, otherwise it'll overwrite the first run
        try:
            import wandb
            wandb.finish()
        except:
            pass
        return output
    return wrapper
pass

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 chunked_hidden_states_selective_log_softmax(
    hidden_states: torch.Tensor,
    lm_head: torch.Tensor,
    index: torch.Tensor,
    chunks: int = 4,
    logit_scale_multiply: float = 0.0,
    logit_scale_divide: float = 0.0,
    logit_softcapping: float = 0.0,
    temperature: float = 1.0,
) -> torch.Tensor:
    # All Unsloth Zoo code licensed under AGPL3
    flat_hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
    flat_index = index.reshape(-1)

    chunked_hidden_states = torch.chunk(flat_hidden_states, chunks=chunks, dim=0)
    chunked_index = torch.chunk(flat_index, chunks=chunks, dim=0)

    all_per_token_logps = []

    for chunk_hidden_states, chunk_index in zip(chunked_hidden_states, chunked_index):
        chunk_logits = chunk_hidden_states.to(lm_head.dtype) @ lm_head.t()

        if logit_scale_multiply != 0.0:
            chunk_logits = chunk_logits * logit_scale_multiply
        if logit_scale_divide != 0.0:
            chunk_logits = chunk_logits / logit_scale_divide
        if logit_softcapping != 0.0:
            chunk_logits = chunk_logits * torch.tanh(chunk_logits / logit_softcapping)

        chunk_logits = chunk_logits.to(torch.float32)

        if temperature != 1.0:
            chunk_logits = chunk_logits / temperature

        selected_logits = torch.gather(chunk_logits, dim=-1, index=chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim=-1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)

    all_per_token_logps = torch.concat(all_per_token_logps)

    all_per_token_logps = all_per_token_logps.reshape((hidden_states.shape[0], hidden_states.shape[1]))
    return all_per_token_logps

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def chunked_selective_log_softmax(logits, index):
    # Split into 4 chunks only
    chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0)
    chunked_index  = torch.chunk(index.reshape(-1), chunks = 4, dim = 0)
    all_per_token_logps = []
    # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index)
    for chunk_logits, chunk_index in zip(chunked_logits, chunked_index):
        chunk_logits = chunk_logits.to(torch.float32)
        selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim = -1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)
    pass
    all_per_token_logps = torch.concat(all_per_token_logps)
    all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1]))
    return all_per_token_logps

def calculate_pad_tokens_in_prompt(
    input_ids: torch.Tensor,
    logits_to_keep: int,
    pad_token_id: int
) -> torch.Tensor:
    """
    Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens
    """
    if logits_to_keep >= input_ids.shape[1]:
        raise ValueError("logits_to_keep must be smaller than the sequence length.")

    prompt_section = input_ids[:, :-logits_to_keep]

    padding_mask = (prompt_section == pad_token_id)

    pad_token_counts = padding_mask.sum(dim=1)

    return pad_token_counts

def create_completion_attention_mask(
    completion_input_ids: torch.Tensor,
    left_pad_tokens_per_prompt: torch.Tensor,
    max_left_pad: int,
    pad_token_id: int
) -> torch.Tensor:
    """
    Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad]

    Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens
    and pad are pad tokens, this function would make a completion mask that would 0 out the pad
    and p tokens. so in this example [0,0,0,1,1,1,0,0,0]
    """
    batch_size, completion_len = completion_input_ids.shape
    device = completion_input_ids.device

    num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt

    indices = torch.arange(completion_len, device=device).unsqueeze(0)
    shift_mask = indices >= num_tokens_to_mask.unsqueeze(1)

    non_padding_mask = (completion_input_ids != pad_token_id)

    final_mask = shift_mask & non_padding_mask

    return final_mask

def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
    """
    Moves all padding tokens in each sequence of a batch to the right.
    """
    mask = (tensor != pad_id)
    # Must do stable=True since binary mark is unordered
    sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
    packed_tensor = torch.gather(tensor, 1, sorted_indices)
    return packed_tensor

def align_logprobs_with_mask(
    logprob_tensor: torch.Tensor,
    attention_mask: torch.Tensor,
    pad_value: float = 0.0
) -> torch.Tensor:
    """
    Aligns a log probability tensor with a given attention mask.
    """

    device = logprob_tensor.device
    batch_size, logprob_seq_len = logprob_tensor.shape
    mask_seq_len = attention_mask.shape[1]

    padded_logprobs = torch.full(
        attention_mask.shape,
        fill_value=pad_value,
        dtype=logprob_tensor.dtype,
        device=device
    )

    left_pad_counts = torch.argmax(attention_mask, dim=1)

    cols = torch.arange(logprob_seq_len, device=device)
    dest_indices = left_pad_counts.unsqueeze(1) + cols

    # Create destination row indices
    # Shape: [batch_size, logprob_seq_len]
    row_indices = torch.arange(batch_size, device=device).unsqueeze(1).expand_as(dest_indices)

    # --- 4. Filter out-of-bounds indices and perform assignment ---
    # Create a mask to identify only the indices that are within the bounds
    # of the target tensor's sequence length.
    valid_mask = dest_indices < mask_seq_len

    # Use this mask to select only the valid row indices, column indices,
    # and the corresponding values from the logprob tensor.
    # This flattens the selected elements into 1D tensors.
    valid_rows = row_indices[valid_mask]
    valid_cols = dest_indices[valid_mask]
    valid_vals = logprob_tensor[valid_mask]

    # Place the valid values into their correct positions in the padded tensor
    # using a single, efficient advanced indexing operation.
    padded_logprobs[valid_rows, valid_cols] = valid_vals

    return padded_logprobs

def autotune_batch_and_chunks(
    total_input_rows,
    seq_len,
    hidden_size,
    vocab_size,
    dtype_bytes=16,
    multiplier=None
):
    if multiplier is None:
        final_m = max(4, seq_len // 4096)
    else:
        final_m = multiplier

    if torch.cuda.is_available():
        free_bytes, _ = torch.cuda.mem_get_info()
        limit_gb = (free_bytes / (1024**3))*.80
    elif hasattr(torch, "xpu") and torch.xpu.is_available():
        # For XPU: estimate free memory from total - reserved
        total_mem = torch.xpu.get_device_properties(0).total_memory
        reserved_mem = torch.xpu.memory_reserved()
        free_bytes = total_mem - reserved_mem
        limit_gb = (free_bytes / (1024**3)) * 0.80
    else:
        # Fallback: assume 8GB available
        limit_gb = 8.0

    bytes_to_gb = 1024**3

    b_vals = torch.arange(total_input_rows, 0, -1, device='cpu', dtype=torch.float32)

    hidden_gb = (b_vals * seq_len * hidden_size * dtype_bytes) / bytes_to_gb

    base_logits = ((b_vals/total_input_rows) * b_vals * seq_len * vocab_size * dtype_bytes) / bytes_to_gb
    logits_gb = base_logits / final_m

    total_mem_gb = hidden_gb + logits_gb

    valid_mask = total_mem_gb <= limit_gb
    valid_indices = torch.nonzero(valid_mask, as_tuple=False)

    if valid_indices.shape[0] == 0:
        #This means your GPU will OOM
        return 4, final_m

    best_idx = valid_indices[0].item()
    final_b = int(b_vals[best_idx].item())

    return final_b, final_m
@dataclass
class UnslothRewardConfig(RewardConfig):
    """
    
    Configuration class for the [`RewardTrainer`].

    This class includes only the parameters that are specific to Reward training. For a full list of training
    arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this
    class may differ from those in [`~transformers.TrainingArguments`].

    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:
        > Parameters that control the model

        model_init_kwargs (`dict[str, Any]`, *optional*):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`RewardTrainer`] is provided as a string. If you're training a MoE architecture and want
            to include the load balancing/auxilliary loss as a part of the final loss, remember to set
            `output_router_logits=True` in this dictionary.
        chat_template_path (`str`, *optional*):
            If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory
            or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must
            ensure that any special tokens referenced in the template are added to the tokenizer and that the model's
            embedding layer is resized accordingly.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model.

        > Parameters that control the data preprocessing

        dataset_num_proc (`int`, *optional*):
            Number of processes to use for processing the dataset.
        eos_token (`str`, *optional*):
            Token used to indicate the end of a turn or sequence. If `None`, it defaults to
            `processing_class.eos_token`.
        pad_token (`str`, *optional*):
            Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`,
            it falls back to `processing_class.eos_token`.
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the tokenized sequence. Samples are filtered out if either chosen or rejected sequence
            exceeds this value. If `None`, no filtering is applied.
        pad_to_multiple_of (`int`, *optional*):
            If set, the sequences will be padded to a multiple of this value.

        > Parameters that control the training

        center_rewards_coefficient (`float`, *optional*):
            Coefficient to incentivize the reward model to output mean-zero rewards (proposed by
            https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`.
        activation_offloading (`bool`, *optional*, defaults to `False`):
            Whether to offload the activations to the CPU.
    
    """
    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.'},
    )
    unsloth_logit_chunk_multiplier : Optional[int] = field(
            default = None,
            metadata = {'help': 'Multiplier for chunked logit computations.'},
        )
    unsloth_grpo_mini_batch : Optional[int] = field(
        default = None,
        metadata = {'help': 'Mini batch size for GRPO hidden state accumulation. Default is None unless user defines it.'},
    )
    max_seq_length : Optional[int] = field(
        default = None,
        metadata = {'help': 'Maximum sequence length to truncate to.'},
    )
    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',
        lr_scheduler_kwargs = None,
        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,
        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 = None,
        fsdp_min_num_params = 0,
        fsdp_config = None,
        fsdp_transformer_layer_cls_to_wrap = None,
        accelerator_config = None,
        parallelism_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',
        project = 'huggingface',
        trackio_space_id = 'trackio',
        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,
        hub_revision = None,
        gradient_checkpointing = True,
        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,
        liger_kernel_config = None,
        eval_use_gather_object = False,
        average_tokens_across_devices = True,
        model_init_kwargs = None,
        chat_template_path = None,
        disable_dropout = True,
        dataset_num_proc = None,
        eos_token = None,
        pad_token = None,
        max_length = 1024,
        pad_to_multiple_of = None,
        center_rewards_coefficient = None,
        activation_offloading = False,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        unsloth_logit_chunk_multiplier = None, 
        unsloth_grpo_mini_batch = None, 
        max_seq_length = None,
        **kwargs,
    ):
        if learning_rate < 1e-7: print(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: print(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 num_train_epochs is None:
            num_train_epochs = 3.0  # Default to 3 epochs if None, max_steps will override
        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
            output_dir = 'unsloth_training_checkpoints'
            save_strategy = 'no'
        import multiprocessing as _mp
        if _mp.get_start_method() != 'fork':
            dataset_num_proc = None
        elif dataset_num_proc is None:
            import psutil
            dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)
            memory_gb_left = psutil.virtual_memory().available / (1024**3)
            if memory_gb_left <= 2: dataset_num_proc = 1
            else: dataset_num_proc = min(dataset_num_proc, int(memory_gb_left))
        if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1':
            from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION
            if HAS_FLEX_ATTENTION and pad_to_multiple_of is None:
                from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE
                pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE
        
        
        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,
            lr_scheduler_kwargs = lr_scheduler_kwargs,
            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,
            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,
            parallelism_config = parallelism_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,
            project = project,
            trackio_space_id = trackio_space_id,
            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,
            hub_revision = hub_revision,
            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,
            liger_kernel_config = liger_kernel_config,
            eval_use_gather_object = eval_use_gather_object,
            average_tokens_across_devices = average_tokens_across_devices,
            model_init_kwargs = model_init_kwargs,
            chat_template_path = chat_template_path,
            disable_dropout = disable_dropout,
            dataset_num_proc = dataset_num_proc,
            eos_token = eos_token,
            pad_token = pad_token,
            max_length = max_length,
            pad_to_multiple_of = pad_to_multiple_of,
            center_rewards_coefficient = center_rewards_coefficient,
            activation_offloading = activation_offloading,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
        if unsloth_grpo_mini_batch is not None:
            if self.generation_batch_size >= unsloth_grpo_mini_batch:
                self.unsloth_grpo_mini_batch = unsloth_grpo_mini_batch
            else:
                raise ValueError(
                    f"Unsloth GRPO mini batch size needs to be less than or equal to the effective generation batch size, "
                    f"which is self.per_device_train_batch_size * gradient_accumulation_steps."
                )
        self.unsloth_logit_chunk_multiplier = unsloth_logit_chunk_multiplier
        self.max_seq_length = max_seq_length

pass

class _UnslothRewardTrainer(BaseTrainer):
    """"""

    _tag_names = ["trl", "reward-trainer"]
    _name = "Reward"
    _template_file = "rm_model_card.md"

    def __init__(
        self,
        model: Union[str, PreTrainedModel],
        args: Optional[RewardConfig] = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[PreTrainedTokenizerBase] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None),
        optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None,
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional["PeftConfig"] = None,
    ):
        # Args
        if args is None:
            model_name = model if isinstance(model, str) else model.config._name_or_path
            model_name = model_name.split("/")[-1]
            args = RewardConfig(f"{model_name}-Reward")

        # Model
        model_init_kwargs = args.model_init_kwargs or {}
        if isinstance(model, str):
            model_id = model
            dtype = model_init_kwargs.get("dtype")
            if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None:
                pass  # dtype is already a torch.dtype or "auto" or None
            elif isinstance(dtype, str) and dtype in ["bfloat16", "float16", "float32"]:
                model_init_kwargs["dtype"] = getattr(torch, dtype)
            else:
                raise ValueError(
                    "Invalid `dtype` passed to `RewardConfig`. Expected either 'auto' or a string representing "
                    f"a valid `torch.dtype` (e.g., 'float32'), but got {dtype}."
                )
            with suppress_from_pretrained_warning(transformers.modeling_utils.logger):
                model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1, **model_init_kwargs)
        else:
            model_id = model.config._name_or_path
            if args.model_init_kwargs is not None:
                logger.warning(
                    "You passed `model_init_kwargs` to the `RewardConfig`, but your model is already instantiated. "
                    "The `model_init_kwargs` will be ignored."
                )

        # Processing class
        if processing_class is None:
            processing_class = AutoTokenizer.from_pretrained(model_id)

        # Handle pad token for processors or tokenizers
        if args.eos_token is not None:
            eos_token = args.eos_token
            eos_token_id = processing_class.convert_tokens_to_ids(eos_token)
            if eos_token_id is None:
                raise ValueError(
                    f"The specified `eos_token` ('{eos_token}') is not found in the vocabulary of the given "
                    f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `eos_token` exists "
                    "in the vocabulary before using it as an EOS token."
                )
            processing_class.eos_token_id = eos_token_id

        if args.chat_template_path is not None:
            if os.path.isfile(args.chat_template_path) and args.chat_template_path.endswith((".jinja", ".j2")):
                with open(args.chat_template_path, encoding="utf-8") as chat_template_file:
                    processing_class.chat_template = chat_template_file.read()
                added_tokens = []
            else:
                model, processing_class, added_tokens = clone_chat_template(
                    model, processing_class, args.chat_template_path
                )
        else:
            added_tokens = []

        # PEFT configuration and model wrapping
        if False:
            if added_tokens:
                # Ensure that the added tokens are trainable
                if peft_config.trainable_token_indices is None:
                    peft_config.trainable_token_indices = {"embed_tokens": added_tokens}
                elif "embed_tokens" not in peft_config.trainable_token_indices:
                    peft_config.trainable_token_indices["embed_tokens"] = added_tokens
                else:
                    peft_config.trainable_token_indices["embed_tokens"].extend(added_tokens)

                # Ensure that the lm_head is trainable
                if peft_config.modules_to_save is None or "lm_head" not in peft_config.modules_to_save:
                    logger.warning(
                        "Cloning chat template added new tokens to the tokenizer, but 'lm_head' is not in PEFT's "
                        "`modules_to_save`. As a result, the model may not learn to generate outputs with these new "
                        "tokens, leading to degraded generation quality. To fix this, add "
                        "`modules_to_save=['lm_head']` to your PEFT configuration."
                    )

                    if peft_config.modules_to_save is None:
                        peft_config.modules_to_save = ["lm_head"]
                    else:
                        peft_config.modules_to_save.append("lm_head")

        if False:
            pass

        # Disable dropout in the model
        if args.disable_dropout:
            disable_dropout_in_model(model)

        # Pad token [needed for SequenceClassification models]
        # If not provided, use the one from the processing class or the eos token if the processing class does not have
        # a pad token.
        pad_token = args.pad_token or processing_class.pad_token or processing_class.eos_token
        pad_token_id = processing_class.convert_tokens_to_ids(pad_token)
        if pad_token_id is None:
            raise ValueError(
                f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given "
                f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists "
                "in the vocabulary before using it as a padding token."
            )
        model.config.pad_token_id = pad_token_id
        processing_class.pad_token_id = pad_token_id

        # Data collator
        if data_collator is None:
            data_collator = DataCollatorForPreference(
                pad_token_id=pad_token_id,
                pad_to_multiple_of=args.pad_to_multiple_of,
            )

        # Dataset
        train_dataset = self._prepare_dataset(train_dataset, processing_class, args, "train")
        if eval_dataset is not None:
            if isinstance(eval_dataset, dict):
                eval_dataset = {
                    key: self._prepare_dataset(dataset, processing_class, args, key)
                    for key, dataset in eval_dataset.items()
                }
            else:
                eval_dataset = self._prepare_dataset(eval_dataset, processing_class, args, "eval")

        # Initialize the metrics
        self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
        self._total_train_tokens = 0

        # Initialize the Trainer. Parent class will handle:
        # - DeepSpeed configuration [through create_accelerator_and_postprocess]
        # - FSDP setup
        # - Distributed training setup
        # - Optimizer and scheduler creation

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

        # During evaluation, Trainer calls compute_loss[] only if can_return_loss is True and label_names is empty.
        self.can_return_loss = True
        self.label_names = []

        # Initialize activation offloading context
        if self.args.activation_offloading:
            self.maybe_activation_offload_context = get_act_offloading_ctx_manager(model=self.model)
        else:
            self.maybe_activation_offload_context = contextlib.nullcontext()

        # 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)

        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)

    def _prepare_dataset(
        self,
        dataset: Union[Dataset, IterableDataset],
        processing_class: PreTrainedTokenizerBase,
        args: RewardConfig,
        dataset_name: str,
    ) -> Union[Dataset, IterableDataset]:
        # Tabular backends like Arrow/Parquet insert `None` for mismatched keys in nested structures. Clean them from
        # sampled data.
        if isinstance(dataset, Dataset):  # IterableDataset does not support `with_transform`
            dataset = dataset.with_transform(remove_none_values)

        # If the dataset is already preprocessed (tokenized), skip the processing steps.
        column_names = list(next(iter(dataset)).keys())
        is_processed = "chosen_input_ids" in column_names and "rejected_input_ids" in column_names

        # Build the kwargs for the `map` function
        map_kwargs = {}
        if isinstance(dataset, Dataset):  # IterableDataset does not support num_proc
            map_kwargs["num_proc"] = args.dataset_num_proc

        with PartialState().main_process_first():
            if not is_processed:
                # Add EOS token to the end of the sequences if needed
                first_example = next(iter(dataset))
                if not is_conversational(first_example):
                    if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`
                        map_kwargs["desc"] = f"Adding EOS to {dataset_name} dataset"

                    def add_eos(example, eos_token):
                        if not example["chosen"].endswith(eos_token):
                            example["chosen"] = example["chosen"] + eos_token
                        if "rejected" in example and not example["rejected"].endswith(eos_token):
                            example["rejected"] = example["rejected"] + eos_token
                        return example

                    dataset = dataset.map(
                        add_eos,
                        fn_kwargs={"eos_token": processing_class.eos_token},
                        **map_kwargs,
                    )

                # Tokenize the dataset
                if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`
                    map_kwargs["desc"] = f"Tokenizing {dataset_name} dataset"

                def tokenize_fn(example, processing_class):
                    if "prompt" in example:  # explicit prompt case
                        example["chosen"] = example["prompt"] + example["chosen"]
                        example["rejected"] = example["prompt"] + example["rejected"]

                    if is_conversational(example):
                        chosen_input_ids = processing_class.apply_chat_template(
                            example["chosen"],
                            tools=example.get("tools"),
                            **example.get("chat_template_kwargs", {}),
                        )
                        rejected_input_ids = processing_class.apply_chat_template(
                            example["rejected"],
                            tools=example.get("tools"),
                            **example.get("chat_template_kwargs", {}),
                        )
                        output = {"chosen_input_ids": chosen_input_ids, "rejected_input_ids": rejected_input_ids}
                    else:
                        output = {
                            "chosen_input_ids": processing_class(text=example["chosen"])["input_ids"],
                            "rejected_input_ids": processing_class(text=example["rejected"])["input_ids"],
                        }
                    return output

                dataset = dataset.map(tokenize_fn, fn_kwargs={"processing_class": processing_class}, **map_kwargs)

            # Filter samples that are longer than `max_length`
            if args.max_length is not None:
                if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`
                    map_kwargs["desc"] = f"Filtering {dataset_name} >{args.max_length} tokens"
                dataset = dataset.filter(
                    lambda example: len(example["chosen_input_ids"]) <= args.max_length
                    and len(example["rejected_input_ids"]) <= args.max_length,
                    **map_kwargs,
                )

        return dataset

    def _set_signature_columns_if_needed(self):
        # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
        # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids"
        # and "attention_mask").
        if self._signature_columns is None:
            self._signature_columns = ["chosen_input_ids", "rejected_input_ids", "margin"]

    def compute_loss(
        self,
        model: nn.Module,
        inputs: dict[str, Union[torch.Tensor, Any]],
        return_outputs: bool = False,
        num_items_in_batch: Optional[torch.Tensor] = None,
    ):
        """
        Compute training loss and additionally compute token accuracies
        """
        mode = "train" if self.model.training else "eval"

        # If not set, defaults from model config and may warn since cache isn't compatible with gradient checkpointing
        inputs["use_cache"] = False
        outputs = model(**inputs)

        # Split the rewards into chosen and rejected
        rewards_chosen, rewards_rejected = torch.chunk(outputs.logits.squeeze(-1), chunks=2)

        # Calculate loss, optionally modulate with margin
        if "margin" in inputs:
            loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected - inputs["margin"]).mean()
        else:
            loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean()

        if self.args.center_rewards_coefficient is not None:
            loss += self.args.center_rewards_coefficient * torch.mean((rewards_chosen + rewards_rejected) ** 2)

        if mode == "train":
            num_tokens_in_batch = self.accelerator.gather_for_metrics(inputs["attention_mask"].sum()).sum().item()
            self._total_train_tokens += num_tokens_in_batch
        self._metrics[mode]["num_tokens"] = [self._total_train_tokens]

        # Compute min, mean, max, accuracy and margin
        with torch.no_grad():
            all_rewards = self.accelerator.gather(outputs.logits)
            self._metrics[mode]["min_reward"].append(all_rewards.min().item())
            self._metrics[mode]["mean_reward"].append(all_rewards.mean().item())
            self._metrics[mode]["max_reward"].append(all_rewards.max().item())

            mean_accuracy = (rewards_chosen > rewards_rejected).float().mean()
            mean_accuracy = self.accelerator.gather_for_metrics(mean_accuracy).mean().item()
            self._metrics[mode]["accuracy"].append(mean_accuracy)

            mean_margin = (rewards_chosen - rewards_rejected).mean()
            mean_margin = self.accelerator.gather_for_metrics(mean_margin).mean()
            self._metrics[mode]["margin"].append(mean_margin.item())

        return (loss, outputs) if return_outputs else loss

    # Override training step to add activation offloading context.
    def training_step(self, *args, **kwargs):
        with self.maybe_activation_offload_context:
            return super().training_step(*args, **kwargs)

    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
        mode = "train" if self.model.training else "eval"
        metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()}  # average the metrics

        # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs`
        # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format.
        if mode == "eval":
            metrics = {f"eval_{key}": val for key, val in metrics.items()}

        logs.update(metrics)
        super().log(logs, start_time)
        self._metrics[mode].clear()

    # Ensure the model card is saved along with the checkpoint
    def _save_checkpoint(self, model, trial):
        if self.args.hub_model_id is None:
            model_name = Path(self.args.output_dir).name
        else:
            model_name = self.args.hub_model_id.split("/")[-1]
        self.create_model_card(model_name=model_name)
        super()._save_checkpoint(model, trial)
class UnslothRewardTrainer(_UnslothRewardTrainer):
    """
    
    Trainer for Outcome-supervised Reward Models (ORM).

    This class is a wrapper around the [`~transformers.Trainer`] class and inherits all of its attributes and methods.

    Example:

    ```python
    from trl import RewardTrainer
    from datasets import load_dataset

    dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")

    trainer = RewardTrainer(model="Qwen/Qwen2.5-0.5B-Instruct", train_dataset=dataset)
    trainer.train()
    ```

    Args:
        model (`Union[str, PreTrainedModel]`):
            Model to be trained. Can be either:

            - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a
              path to a *directory* containing model weights saved using
              [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
              using `AutoModelForSequenceClassification.from_pretrained` with the keyword arguments in
              `args.model_init_kwargs`.
            - A sequence classification [`~transformers.PreTrainedModel`] object.
        args ([`RewardConfig`], *optional*):
            Configuration for this trainer. If `None`, a default configuration is used.
        data_collator ([`~transformers.DataCollator`], *optional*):
            Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`.
            Will default to [`~trainer.reward_trainer.DataCollatorForPreference`].
        train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
            Dataset to use for training. This trainer supports [preference](#preference) type (both implicit and
            explicit prompt). The format of the samples can be either:

            - [Standard](dataset_formats#standard): Each sample contains plain text.
            - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role
              and content).

            The trainer also supports processed datasets (tokenized) as long as they contain an `chosen_input_ids` and
            `rejected_input_ids` fields.
        eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`):
            Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], *optional*):
            Tokenizer used to process the data. If `None`, the tokenizer is loaded from the model's name with
            [`~transformers.AutoTokenizer.from_pretrained`]. A padding token, `processing_class.pad_token`, must be
            set. If the processing class has not set a padding token, `processing_class.eos_token` will be used as the
            default.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function that will be used to compute metrics at evaluation. Must take a
            [`~transformers.EvalPrediction`] and return a dictionary string to metric values. When passing
            [`RewardConfig`] with `batch_eval_metrics` set to `True`, your `compute_metrics` function must take a
            boolean `compute_result` argument. This will be triggered after the last eval batch to signal that the
            function needs to calculate and return the global summary statistics rather than accumulating the
            batch-level statistics.
        callbacks (list of [`~transformers.TrainerCallback`], *optional*):
            List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed
            in [here](https://huggingface.co/docs/transformers/main_classes/callback).

            If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`]
            method.
        optimizers (`tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]]`, *optional*, defaults to `(None, None)`):
            A tuple containing the optimizer and the scheduler to use. Will default to an instance of `AdamW` on your
            model and a scheduler given by [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`.
        optimizer_cls_and_kwargs (`tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*):
            A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in
            `args`. Incompatible with the `optimizers` argument.

            Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before
            initializing the Trainer.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            A function that preprocess the logits right before caching them at each evaluation step. Must take two
            tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
            by this function will be reflected in the predictions received by `compute_metrics`.

            Note that the labels (second parameter) will be `None` if the dataset does not have them.
        peft_config ([`~peft.PeftConfig`], *optional*):
            PEFT configuration used to wrap the model. If `None`, the model is not wrapped. Note that if the loaded
            model is a causal LM, it's highly recommended to set `modules_to_save=["score"]` in the PEFT configuration
            to ensure that the reward head is properly trained.
    
    """
    def __init__(
        self,
        model,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        compute_metrics = None,
        callbacks = None,
        optimizer_cls_and_kwargs = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        **kwargs
    ):
        if args is None: args = UnslothRewardConfig()
        use_bf16 = getattr(args, 'bf16', False)
        if type(use_bf16) is not bool: use_bf16 = False
        use_fp16 = getattr(args, 'fp16', False)
        if type(use_fp16) is not bool: use_fp16 = False
        force_float32 = False
        full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'
        if not full_finetuning and (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, 'dtype', None) or getattr(model.config, 'torch_dtype', None)
        if dtype is None: dtype = model.get_input_embeddings().weight.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:
            # Forced float32 training
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
            # Mixed precision training
            args.fp16 = float16
            args.bf16 = not float16
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'fp16' if float16 else 'bf16'
            # args.mixed_precision is a new argument which needs to be set now
        elif mixed_precision_dtype == 'bfloat16':
            # Both False since bfloat16 full finetuning doesn't do any autocasting.
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        
        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)
        if type(fp16_full_eval) is not bool: fp16_full_eval = False
        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
        if type(bf16_full_eval) is not bool: 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
            elif args_max_seq_length is not None and model_max_seq_length is not None:
                if args_max_seq_length > model_max_seq_length:
                    print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but '
                           'the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')
                    args.max_seq_length = model_max_seq_length
        if model is not None and hasattr(model, 'for_training'):
            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        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,
                    pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                )
            elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
                data_collator = DataCollatorForSeq2Seq(
                    __tokenizer,
                    pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                )
        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,
                        pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                    )
                else:
                    data_collator = TransformersDataCollatorForLanguageModeling(
                        __tokenizer.tokenizer,
                        mlm = False,
                        mlm_probability = 0.0,
                        pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                    )
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('reward_trainer', other_metrics)
        
        # [TODO] Fix up DataParallel multiplying batch sizes
        # [TODO] DDP works, but DP seems to not work? [TODO]
        if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
            if getattr(args, "_n_gpu", 1) != 1:
                args._n_gpu = 1
        if "model" in locals() and hasattr(model, "for_training"):
            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        super().__init__(
            model = model,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            compute_metrics = compute_metrics,
            callbacks = callbacks,
            optimizer_cls_and_kwargs = optimizer_cls_and_kwargs,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            peft_config = peft_config,**kwargs)
        if "model" in locals() and hasattr(model, "for_inference"):
            model.for_inference()
        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
        if hasattr(self, 'accelerator'):
            scaler = self.accelerator.scaler
            current_model = model
            while hasattr(current_model, 'model'):
                current_model.accelerator_scaler = scaler
                current_model = current_model.model
            current_model.accelerator_scaler = scaler
        pass
        if hasattr(self, 'train'):
            self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)
        pass
        if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):
            _vllm_tok = self.llm.get_tokenizer()
            _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)
            if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:
                _vllm_tok.chat_template = _pc.chat_template
        pass
        
pass


if hasattr(logger, "addFilter"):
    import logging
    class HideLoggingMessage(logging.Filter):
        def __init__(self, text): self.text = text
        def filter(self, x): return not (self.text in x.getMessage())
    pass
    logger.addFilter(HideLoggingMessage("`use_cache=True`"))

