# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
from collections import defaultdict
from dataclasses import FrozenInstanceError, replace
from pathlib import Path
from typing import Any, Callable, Optional, Union

import pandas as pd
import torch
import torch.nn as nn
from accelerate import PartialState, logging
from accelerate.utils import gather_object
from datasets import Dataset
from transformers import (
    BaseImageProcessor,
    DataCollator,
    FeatureExtractionMixin,
    PreTrainedModel,
    PreTrainedTokenizerBase,
    ProcessorMixin,
    Trainer,
    is_wandb_available,
)
from transformers.trainer_callback import TrainerCallback
from transformers.trainer_pt_utils import nested_detach
from transformers.trainer_utils import EvalPrediction
from transformers.utils import is_peft_available, is_rich_available

from ..data_utils import maybe_apply_chat_template
from ..models import prepare_peft_model
from .reward_config import RewardConfig
from .utils import (
    RewardDataCollatorWithPadding,
    compute_accuracy,
    decode_and_strip_padding,
    disable_dropout_in_model,
    generate_model_card,
    get_comet_experiment_url,
    log_table_to_comet_experiment,
    print_rich_table,
)


if is_peft_available():
    from peft import PeftModel

if is_wandb_available():
    import wandb


logger = logging.get_logger(__name__)


def _tokenize(batch: dict[str, list[Any]], tokenizer: "PreTrainedTokenizerBase") -> dict[str, list[Any]]:
    """Tokenize a batch from a reward modelling dataset."""
    new_examples = {
        "input_ids_chosen": [],
        "attention_mask_chosen": [],
        "input_ids_rejected": [],
        "attention_mask_rejected": [],
    }
    for chosen, rejected in zip(batch["chosen"], batch["rejected"]):
        tokenized_chosen = tokenizer(chosen)
        tokenized_rejected = tokenizer(rejected)
        new_examples["input_ids_chosen"].append(tokenized_chosen["input_ids"])
        new_examples["attention_mask_chosen"].append(tokenized_chosen["attention_mask"])
        new_examples["input_ids_rejected"].append(tokenized_rejected["input_ids"])
        new_examples["attention_mask_rejected"].append(tokenized_rejected["attention_mask"])

    return new_examples


class RewardTrainer(Trainer):
    """
    Trainer for custom reward.

    Args:
        model ([`~transformers.PreTrainedModel`] or `torch.nn.Module`, *optional*):
            Model to be trained, preferably an [`~transformers.AutoModelForSequenceClassification`].
        args ([`RewardConfig`], *optional*):
            Training arguments.
        data_collator ([`~transformers.DataCollator`], *optional*):
            The data collator to use for training. If None is specified, the default data collator
            [`~trainer.utils.RewardDataCollatorWithPadding`] will be used which will pad the sequences to the maximum
            length of the sequences in the batch, given a dataset of paired sequences.
        train_dataset ([`~datasets.Dataset`], *optional*):
            The dataset to use for training.
        eval_dataset ([`~datasets.Dataset`], *optional*):
            The dataset to use for evaluation.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.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.
        model_init (`Callable[[], transformers.PreTrainedModel]`, *optional*):
            The model initializer to use for training. If None is specified, the default model initializer will be
            used.
        compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional*, defaults to [`~trainer.utils.compute_accuracy`]):
            Function to compute metrics at evaluation. Must take in an [`~transformers.EvalPrediction`] and return a
            dictionary string to float.
        callbacks (`list` of [`~transformers.TrainerCallback`], *optional*):
            Callbacks to use during training.
        optimizers (`tuple` of `torch.optim.Optimizer` and `torch.optim.lr_scheduler.LambdaLR`, *optional*, defaults to `(None, None)`):
            Tuple containing the optimizer and the learning rate scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            Function to preprocess the logits before computing the metrics. Must take in the `logits` and `labels` and
            return the logits to be used for metrics computation.
        peft_config (`dict`, *optional*):
            PEFT configuration to use PEFT for training. If `None`, PEFT is not used. If provided, the `model` will be
            wrapped with the specified PEFT adapter.
    """

    _tag_names = ["trl", "reward-trainer"]

    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module]] = None,
        args: Optional[RewardConfig] = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = 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,
    ):
        if peft_config is not None or (is_peft_available() and isinstance(model, PeftModel)):
            model = prepare_peft_model(model, peft_config, args)

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

        if compute_metrics is None:
            compute_metrics = compute_accuracy

        if data_collator is None:
            if processing_class is None:
                raise ValueError(
                    "A processing_class must be specified when using the default RewardDataCollatorWithPadding"
                )

            max_length = args.max_length

            data_collator = RewardDataCollatorWithPadding(processing_class)

            if args.remove_unused_columns:
                try:  # for bc before https://github.com/huggingface/transformers/pull/25435
                    args.remove_unused_columns = False
                except FrozenInstanceError:
                    args = replace(args, remove_unused_columns=False)
                # warn users
                logger.warning(
                    "When using RewardDataCollatorWithPadding, you should set `remove_unused_columns=False` in your RewardConfig"
                    " we have set it for you, but you should do it yourself in the future.",
                )

            self.use_reward_data_collator = True
        else:
            self.use_reward_data_collator = False

        # 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 Reward, the sampled data does not include the
        # "input_ids" key. Instead, the available keys are "input_ids_chosen" and "input_ids_rejected". 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

        if "input_ids_chosen" not in train_dataset.column_names:
            with PartialState().main_process_first():
                fn_kwargs = {"tokenizer": processing_class}
                train_dataset = train_dataset.map(maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class})
                train_dataset = train_dataset.map(
                    _tokenize,
                    batched=True,
                    fn_kwargs=fn_kwargs,
                    num_proc=args.dataset_num_proc,
                )
                # This filter is important because otherwise you get samples that exceed the model's context length and
                # get truncated => noisy signal the chosen/rejected label gets lost. The downside is that the
                # user might get surprised if N samples are missing from training.
                train_dataset = train_dataset.filter(
                    lambda x: len(x["input_ids_chosen"]) <= max_length and len(x["input_ids_rejected"]) <= max_length,
                    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}
                    )
                    eval_dataset = eval_dataset.map(
                        _tokenize,
                        fn_kwargs=fn_kwargs,
                        batched=True,
                        num_proc=args.dataset_num_proc,
                    )
                    # This filter is important because otherwise you get samples that exceed the model's context length and
                    # get truncated => noisy signal the chosen/rejected label gets lost. The downside is that the
                    # user might get surprised if N samples are missing from training.
                    eval_dataset = eval_dataset.filter(
                        lambda x: len(x["input_ids_chosen"]) <= max_length
                        and len(x["input_ids_rejected"]) <= max_length,
                        num_proc=args.dataset_num_proc,
                    )

        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,
        )

        # 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)

    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]]]:
        rewards_chosen = model(
            input_ids=inputs["input_ids_chosen"],
            attention_mask=inputs["attention_mask_chosen"],
            return_dict=True,
        )["logits"]
        rewards_rejected = model(
            input_ids=inputs["input_ids_rejected"],
            attention_mask=inputs["attention_mask_rejected"],
            return_dict=True,
        )["logits"]
        # 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 return_outputs:
            return loss, {
                "rewards_chosen": rewards_chosen,
                "rewards_rejected": rewards_rejected,
            }
        return loss

    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,
    ) -> tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
        inputs = self._prepare_inputs(inputs)
        if ignore_keys is None:
            if hasattr(self.model, "config"):
                ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
            else:
                ignore_keys = []

        with torch.no_grad():
            loss, logits_dict = self.compute_loss(model, inputs, return_outputs=True)

        if prediction_loss_only:
            return (loss, None, None)

        loss = loss.detach()
        logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)
        logits = nested_detach(logits)
        # Stack accepted against rejected, mean over logits
        # and softmax to get preferences between accepted and rejected to sum to 1
        logits = torch.stack(logits).mean(dim=2).softmax(dim=0).T

        labels = torch.zeros(logits.shape[0])
        labels = self._prepare_inputs(labels)

        return loss, logits, labels

    def evaluate(self, *args, **kwargs):
        num_print_samples = kwargs.pop("num_print_samples", 4)
        self.visualize_samples(num_print_samples)
        return super().evaluate(*args, **kwargs)

    def visualize_samples(self, num_print_samples: int):
        """
        Visualize the reward model logits prediction

        Args:
            num_print_samples (`int`, defaults to `4`):
                The number of samples to print. Set to `-1` to print all samples.
        """
        eval_dataloader = self.get_eval_dataloader()
        table = defaultdict(list)
        for _, inputs in enumerate(eval_dataloader):
            _, logits, _ = self.prediction_step(self.model, inputs, prediction_loss_only=False)
            chosen_text = decode_and_strip_padding(inputs["input_ids_chosen"], self.processing_class)
            rejected_text = decode_and_strip_padding(inputs["input_ids_rejected"], self.processing_class)
            table["chosen_text"].extend(gather_object(chosen_text))
            table["rejected_text"].extend(gather_object(rejected_text))
            table["logits"].extend(
                gather_object([[round(inner_item, 4) for inner_item in item] for item in logits.tolist()])
            )
            if num_print_samples >= 0 and len(table["chosen_text"]) >= num_print_samples:
                break
        df = pd.DataFrame(table)
        if self.accelerator.process_index == 0:
            if is_rich_available():
                print_rich_table(df[:num_print_samples])
            if "wandb" in self.args.report_to:
                import wandb

                if wandb.run is not None:
                    wandb.log({"completions": wandb.Table(dataframe=df)})

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

    # 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)

    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

        # normalize `tags` to a mutable set
        if tags is None:
            tags = set()
        elif isinstance(tags, str):
            tags = {tags}
        else:
            tags = set(tags)

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

        if "JOB_ID" in os.environ:
            tags.add("hf_jobs")

        tags.update(self._tag_names)

        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.url if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="Reward",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))