# Copyright 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 inspect
import os
import random
import textwrap
import warnings
from collections import defaultdict
from contextlib import nullcontext
from typing import Any, Callable, Literal, Optional, Union

import numpy as np
import pandas as pd
import torch
import torch.amp as amp
import torch.nn as nn
import torch.nn.functional as F
import transformers
from accelerate import PartialState
from datasets import Dataset
from packaging import version
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForCausalLM,
    BaseImageProcessor,
    DataCollator,
    FeatureExtractionMixin,
    PreTrainedModel,
    PreTrainedTokenizerBase,
    ProcessorMixin,
    Trainer,
    is_comet_available,
    is_wandb_available,
)
from transformers.trainer_callback import TrainerCallback
from transformers.trainer_utils import EvalLoopOutput
from transformers.utils import is_peft_available, is_torch_fx_proxy

from ..data_utils import maybe_apply_chat_template, maybe_extract_prompt
from .cpo_config import CPOConfig
from .utils import (
    DPODataCollatorWithPadding,
    add_bos_token_if_needed,
    add_eos_token_if_needed,
    disable_dropout_in_model,
    generate_model_card,
    get_comet_experiment_url,
    log_table_to_comet_experiment,
    pad_to_length,
    peft_module_casting_to_bf16,
    selective_log_softmax,
)


if is_peft_available():
    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training


if is_wandb_available():
    import wandb


class CPOTrainer(Trainer):
    r"""
    Initialize CPOTrainer.

    Args:
        model (`transformers.PreTrainedModel`):
            The model to train, preferably an `AutoModelForSequenceClassification`.
        args (`CPOConfig`):
            The CPO config arguments to use for training.
        data_collator (`transformers.DataCollator`):
            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.
        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.
        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.
    """

    _tag_names = ["trl", "cpo"]

    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
        args: Optional[CPOConfig] = 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,
        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,
    ):
        if args.model_init_kwargs is None:
            model_init_kwargs = {}
        elif not isinstance(model, str):
            raise ValueError("You passed model_kwargs to the CPOTrainer. 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 CPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
                    )
                model_init_kwargs["torch_dtype"] = torch_dtype

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

        if self.is_encoder_decoder:
            self.decoder_start_token_id = model.config.decoder_start_token_id
            self.pad_token_id = model.config.pad_token_id

        if processing_class is None:
            raise ValueError("processing_class must be specified to tokenize a CPO dataset.")
        if args.max_length is None:
            warnings.warn(
                "`max_length` is not set in the CPOConfig's init"
                " it will default to `512` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_length = 512
        else:
            max_length = args.max_length
        if args.max_prompt_length is None:
            warnings.warn(
                "`max_prompt_length` is not set in the CPOConfig's init"
                " it will default to `128` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_prompt_length = 128
        else:
            max_prompt_length = args.max_prompt_length

        if args.max_completion_length is None and self.is_encoder_decoder:
            warnings.warn(
                "When using an encoder decoder architecture, you should set `max_completion_length` in the CPOConfig's init"
                " it will default to `128` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_completion_length = 128
        else:
            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 TrainingArguments"
                    " 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
        if args.disable_dropout:
            disable_dropout_in_model(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.processing_class = processing_class

        if args.loss_type in ["hinge", "ipo"] and args.label_smoothing > 0:
            warnings.warn(
                f"You are using the {args.loss_type} loss type that does not support label smoothing. The "
                "`label_smoothing` parameter will be ignored. Set `label_smoothing` to `0.0` to remove this warning.",
                UserWarning,
            )
        if args.loss_type == "kto_pair":
            raise ValueError("Support for kto_pair has been removed in CPOTrainer. Please use KTOTrainer.")

        self.beta = args.beta
        self.label_smoothing = args.label_smoothing
        self.loss_type = args.loss_type
        self.cpo_alpha = args.cpo_alpha
        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,
            )

        if args.loss_type == "simpo":
            self.simpo_gamma = args.simpo_gamma

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

        # 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 CPO, the sampled data does not include the
        # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and
        # "rejected_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

        # Compute that only on the main process for faster data processing.
        # see: https://github.com/huggingface/trl/pull/1255
        with PartialState().local_main_process_first():
            # Extract the prompt if needed, and apply the chat template if needed
            train_dataset = train_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
            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_extract_prompt, num_proc=args.dataset_num_proc)
                eval_dataset = eval_dataset.map(
                    maybe_apply_chat_template,
                    fn_kwargs={"tokenizer": processing_class},
                    num_proc=args.dataset_num_proc,
                )

            # tokenize the dataset
            train_dataset = train_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)
            if eval_dataset is not None:
                eval_dataset = eval_dataset.map(self.tokenize_row, 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,
        )

        # 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`."
            )

    def build_tokenized_answer(self, prompt, answer):
        """
        Llama tokenizer does satisfy `enc(a + b) = enc(a) + enc(b)`.
        It does ensure `enc(a + b) = enc(a) + enc(a + b)[len(enc(a)):]`.
        Reference:
            https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
        """

        full_tokenized = self.processing_class(prompt + answer, add_special_tokens=False)
        prompt_input_ids = self.processing_class(prompt, add_special_tokens=False)["input_ids"]

        answer_input_ids = full_tokenized["input_ids"][len(prompt_input_ids) :]
        answer_attention_mask = full_tokenized["attention_mask"][len(prompt_input_ids) :]

        # Concat tokens to form `enc(a) + enc(a + b)[len(enc(a)):]`
        full_concat_input_ids = np.concatenate([prompt_input_ids, answer_input_ids])

        # Prepare input tokens for token by token comparison
        full_input_ids = np.array(full_tokenized["input_ids"])

        if len(full_input_ids) != len(full_concat_input_ids):
            raise ValueError("Prompt input ids and answer input ids should have the same length.")

        # On some tokenizers, like Llama-2 tokenizer, there are occasions where tokens
        # can be merged together when tokenizing prompt+answer. This could result
        # on the last token from the prompt being different when tokenized on its own
        # vs when done as prompt+answer.
        response_token_ids_start_idx = len(prompt_input_ids)

        # If tokenized prompt is different than both prompt+answer, then it means the
        # last token has changed due to merging.
        if prompt_input_ids != full_tokenized["input_ids"][:response_token_ids_start_idx]:
            response_token_ids_start_idx -= 1

        prompt_input_ids = full_tokenized["input_ids"][:response_token_ids_start_idx]
        prompt_attention_mask = full_tokenized["attention_mask"][:response_token_ids_start_idx]

        if len(prompt_input_ids) != len(prompt_attention_mask):
            raise ValueError("Prompt input ids and attention mask should have the same length.")

        answer_input_ids = full_tokenized["input_ids"][response_token_ids_start_idx:]
        answer_attention_mask = full_tokenized["attention_mask"][response_token_ids_start_idx:]

        return dict(
            prompt_input_ids=prompt_input_ids,
            prompt_attention_mask=prompt_attention_mask,
            input_ids=answer_input_ids,
            attention_mask=answer_attention_mask,
        )

    def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> dict:
        """Tokenize a single row from a CPO specific dataset.

        At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
        in case the prompt + chosen or prompt + rejected responses is/are too long. First
        we truncate the prompt; if we're still too long, we truncate the chosen/rejected.

        We also create the labels for the chosen/rejected responses, which are of length equal to
        the sum of the length of the prompt and the chosen/rejected response, with
        label_pad_token_id  for the prompt tokens.
        """
        batch = {}
        prompt = feature["prompt"]
        chosen = feature["chosen"]
        rejected = feature["rejected"]

        if not self.is_encoder_decoder:
            # Check issues below for more details
            #  1. https://github.com/huggingface/trl/issues/907
            #  2. https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
            #  3. https://github.com/LianjiaTech/BELLE/issues/337

            if not isinstance(prompt, str):
                raise ValueError(f"prompt should be an str but got {type(prompt)}")
            prompt_tokens = self.processing_class(prompt, add_special_tokens=False)
            prompt_tokens = {f"prompt_{k}": v for k, v in prompt_tokens.items()}

            if not isinstance(chosen, str):
                raise ValueError(f"chosen should be an str but got {type(chosen)}")
            chosen_tokens = self.build_tokenized_answer(prompt, chosen)

            if not isinstance(rejected, str):
                raise ValueError(f"rejected should be an str but got {type(rejected)}")
            rejected_tokens = self.build_tokenized_answer(prompt, rejected)

            # Last prompt token might get merged by tokenizer and
            # it should not be included for generation if that happens
            prompt_len_input_ids = len(prompt_tokens["prompt_input_ids"])

            chosen_prompt_len_input_ids = len(chosen_tokens["prompt_input_ids"])
            rejected_prompt_len_input_ids = len(rejected_tokens["prompt_input_ids"])
            prompt_len_input_ids = min(chosen_prompt_len_input_ids, rejected_prompt_len_input_ids)

            for k, v in prompt_tokens.items():
                prompt_tokens[k] = v[:prompt_len_input_ids]

            # Make sure prompts only have one different token at most an
            # and length only differs by 1 at most
            num_diff_tokens = sum(
                [a != b for a, b in zip(chosen_tokens["prompt_input_ids"], rejected_tokens["prompt_input_ids"])]
            )
            num_diff_len = abs(chosen_prompt_len_input_ids - rejected_prompt_len_input_ids)
            if num_diff_tokens > 1 or num_diff_len > 1:
                raise ValueError(
                    "Chosen and rejected prompt_input_ids might only differ on the "
                    "last token due to tokenizer merge ops."
                )

            # add BOS token to head of prompt. Avoid adding if it's already there
            prompt_tokens, chosen_tokens, rejected_tokens = add_bos_token_if_needed(
                self.processing_class.bos_token_id,
                prompt_len_input_ids,
                prompt_tokens,
                chosen_prompt_len_input_ids,
                chosen_tokens,
                rejected_prompt_len_input_ids,
                rejected_tokens,
            )

            # add EOS token to end of answer. Avoid adding if it's already there
            chosen_tokens, rejected_tokens = add_eos_token_if_needed(
                self.processing_class.eos_token_id, chosen_tokens, rejected_tokens
            )

            longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"]))

            # if combined sequence is too long, truncate the prompt
            for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]:
                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
                    if self.truncation_mode == "keep_start":
                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
                            answer_tokens[k] = answer_tokens[k][: self.max_prompt_length]
                    elif self.truncation_mode == "keep_end":
                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
                            answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :]
                    else:
                        raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")

            # if that's still too long, truncate the response
            for answer_tokens in [chosen_tokens, rejected_tokens]:
                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
                    for k in ["input_ids", "attention_mask"]:
                        answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length]

            # Create labels
            chosen_sequence_tokens = {
                k: chosen_tokens[f"prompt_{k}"] + chosen_tokens[k] for k in ["input_ids", "attention_mask"]
            }
            rejected_sequence_tokens = {
                k: rejected_tokens[f"prompt_{k}"] + rejected_tokens[k] for k in ["input_ids", "attention_mask"]
            }
            chosen_sequence_tokens["labels"] = chosen_sequence_tokens["input_ids"][:]
            chosen_sequence_tokens["labels"][: len(chosen_tokens["prompt_input_ids"])] = [
                self.label_pad_token_id
            ] * len(chosen_tokens["prompt_input_ids"])
            rejected_sequence_tokens["labels"] = rejected_sequence_tokens["input_ids"][:]
            rejected_sequence_tokens["labels"][: len(rejected_tokens["prompt_input_ids"])] = [
                self.label_pad_token_id
            ] * len(rejected_tokens["prompt_input_ids"])

            for k, toks in {
                "chosen_": chosen_sequence_tokens,
                "rejected_": rejected_sequence_tokens,
                "": prompt_tokens,
            }.items():
                for type_key, tokens in toks.items():
                    if type_key == "token_type_ids":
                        continue
                    batch[f"{k}{type_key}"] = tokens

        else:
            chosen_tokens = self.processing_class(
                chosen, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
            )
            rejected_tokens = self.processing_class(
                rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
            )
            prompt_tokens = self.processing_class(
                prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True
            )

            batch["chosen_labels"] = chosen_tokens["input_ids"]
            batch["rejected_labels"] = rejected_tokens["input_ids"]
            batch["prompt_input_ids"] = prompt_tokens["input_ids"]
            batch["prompt_attention_mask"] = prompt_tokens["attention_mask"]

            if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"):
                batch["rejected_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
                    labels=torch.tensor(batch["rejected_labels"])
                )
                batch["chosen_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
                    labels=torch.tensor(batch["chosen_labels"])
                )

        return batch

    @staticmethod
    def concatenated_inputs(
        batch: dict[str, Union[list, torch.LongTensor]],
        is_encoder_decoder: bool = False,
        label_pad_token_id: int = -100,
        padding_value: int = 0,
        device: Optional[torch.device] = None,
    ) -> dict[str, torch.LongTensor]:
        """Concatenate the chosen and rejected inputs into a single tensor.

        Args:
            batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).
            is_encoder_decoder: Whether the model is an encoder-decoder model.
            label_pad_token_id: The label pad token id.
            padding_value: The padding value to use for the concatenated inputs_ids.
            device: The device for the concatenated inputs.

        Returns:
            A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.
        """
        concatenated_batch = {}

        if is_encoder_decoder:
            max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1])
        else:
            max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1])

        for k in batch:
            if k.startswith("chosen") and isinstance(batch[k], torch.Tensor):
                if "labels" in k or is_encoder_decoder:
                    pad_value = label_pad_token_id
                elif k.endswith("_input_ids"):
                    pad_value = padding_value
                elif k.endswith("_attention_mask"):
                    pad_value = 0
                concatenated_key = k.replace("chosen", "concatenated")
                concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)
        for k in batch:
            if k.startswith("rejected") and isinstance(batch[k], torch.Tensor):
                if "labels" in k or is_encoder_decoder:
                    pad_value = label_pad_token_id
                elif k.endswith("_input_ids"):
                    pad_value = padding_value
                elif k.endswith("_attention_mask"):
                    pad_value = 0
                concatenated_key = k.replace("rejected", "concatenated")
                concatenated_batch[concatenated_key] = torch.cat(
                    (
                        concatenated_batch[concatenated_key],
                        pad_to_length(batch[k], max_length, pad_value=pad_value),
                    ),
                    dim=0,
                ).to(device=device)

        if is_encoder_decoder:
            concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1).to(device=device)
            concatenated_batch["concatenated_attention_mask"] = (
                batch["prompt_attention_mask"].repeat(2, 1).to(device=device)
            )

        return concatenated_batch

    def cpo_loss(
        self,
        policy_chosen_logps: torch.FloatTensor,
        policy_rejected_logps: torch.FloatTensor,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Compute the CPO 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: (batch_size,)
            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)

        Returns:
            A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).
            The losses tensor contains the CPO loss for each example in the batch.
            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
        """
        logits = (policy_chosen_logps - policy_rejected_logps).to(self.accelerator.device)

        # The beta is a temperature parameter for the CPO loss, typically something in the range of 0.1 to 0.5.
        # We ignore the reference model as beta -> 0. The label_smoothing parameter encodes our uncertainty about the labels and
        # calculates a conservative CPO loss.

        if self.loss_type == "simpo":
            gamma_logratios = self.simpo_gamma / self.beta
            logits = logits - gamma_logratios
            # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0.
            losses = (
                -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
                - F.logsigmoid(-self.beta * logits) * self.label_smoothing
            )
        elif self.loss_type == "sigmoid":
            # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0.
            losses = (
                -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
                - F.logsigmoid(-self.beta * logits) * self.label_smoothing
            )
        elif self.loss_type == "hinge":
            losses = torch.relu(1 - self.beta * logits)
        elif self.loss_type == "ipo":
            # eqn (17) of the paper where beta is the regularization parameter for the IPO loss, denoted by tau in the paper.
            losses = (logits - 1 / (2 * self.beta)) ** 2
        else:
            raise ValueError(
                f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge', 'ipo', 'simpo']"
            )

        chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach()
        rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach()

        return losses, chosen_rewards, rejected_rewards

    @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.
            label_pad_token_id: The label pad token id.
            is_encoder_decoder: Whether the model is an encoder-decoder model.

        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, :]
        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 concatenated_forward(
        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.

        We do this to avoid doing two forward passes, because it's faster for FSDP.
        """
        concatenated_batch = self.concatenated_inputs(
            batch,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
            padding_value=self.padding_value,
            device=self.accelerator.device,
        )
        len_chosen = batch["chosen_labels"].shape[0]

        model_kwargs = (
            {
                "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]),
            }
            if self.is_encoder_decoder
            else {}
        )

        if self.aux_loss_enabled:
            model_kwargs["output_router_logits"] = True

        outputs = model(
            concatenated_batch["concatenated_input_ids"],
            attention_mask=concatenated_batch["concatenated_attention_mask"],
            use_cache=False,
            **model_kwargs,
        )
        all_logits = outputs.logits

        def cross_entropy_loss(logits, labels):
            if not self.is_encoder_decoder:
                # Shift so that tokens < n predict n
                logits = logits[..., :-1, :].contiguous()
                labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()
            logits = logits.view(-1, logits.shape[-1])
            labels = labels.view(-1)
            # Enable model parallelism
            labels = labels.to(logits.device)
            loss = loss_fct(logits, labels)
            return loss

        labels = concatenated_batch["concatenated_labels"].clone()

        if self.cpo_alpha == 0:
            nll_loss = torch.tensor(0.0).to(self.accelerator.device)
        else:
            nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])

        all_logps = self.get_batch_logps(
            all_logits,
            concatenated_batch["concatenated_labels"],
            average_log_prob=self.loss_type in ["ipo", "simpo"],
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
        )

        chosen_logps = all_logps[:len_chosen]
        rejected_logps = all_logps[len_chosen:]

        chosen_logits = all_logits[:len_chosen]
        rejected_logits = all_logits[len_chosen:]

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

        return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss)

    def get_batch_loss_metrics(
        self,
        model,
        batch: dict[str, Union[list, torch.LongTensor]],
        train_eval: Literal["train", "eval"] = "train",
    ):
        """Compute the CPO loss and other metrics for the given batch of inputs for train or test."""
        metrics = {}

        forward_output = self.concatenated_forward(model, batch)
        (
            policy_chosen_logps,
            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
            policy_nll_loss,
        ) = forward_output[:5]
        if self.aux_loss_enabled:
            aux_loss = forward_output[5]

        losses, chosen_rewards, rejected_rewards = self.cpo_loss(
            policy_chosen_logps,
            policy_rejected_logps,
        )

        loss = losses.mean() + self.cpo_alpha * policy_nll_loss
        reward_accuracies = (chosen_rewards > rejected_rewards).float()

        prefix = "eval_" if train_eval == "eval" else ""
        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean().item()
        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean().item()
        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean().item()
        metrics[f"{prefix}rewards/margins"] = (
            self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards).mean().item()
        )
        metrics[f"{prefix}logps/rejected"] = (
            self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean().item()
        )
        metrics[f"{prefix}logps/chosen"] = (
            self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean().item()
        )
        metrics[f"{prefix}logits/rejected"] = (
            self.accelerator.gather_for_metrics(policy_rejected_logits).detach().mean().item()
        )
        metrics[f"{prefix}logits/chosen"] = (
            self.accelerator.gather_for_metrics(policy_chosen_logits).detach().mean().item()
        )
        metrics[f"{prefix}nll_loss"] = self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item()

        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, train_eval="train")

        # force log the metrics
        self.store_metrics(metrics, train_eval="train")

        if return_outputs:
            return (loss, metrics)
        return loss

    def generate_from_model(self, model, batch: dict[str, torch.LongTensor]) -> 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,
            )

        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)

        return policy_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, train_eval="eval")

        # force log the metrics
        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 = {
            "eval_logits/chosen": metrics["eval_logits/chosen"],
            "eval_logits/rejected": metrics["eval_logits/rejected"],
        }
        logits = tuple(v.unsqueeze(dim=0) for k, v in logits_dict.items() if k not in ignore_keys)
        logits = torch.stack(logits).mean(axis=1).to(self.accelerator.device)
        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)

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

    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 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)

            policy_output_decoded = self.generate_from_model(self.model, random_batch)

            table = pd.DataFrame(
                columns=["Prompt", "Policy"],
                data=[
                    [prompt, pol[len(prompt) :]] for prompt, pol in zip(random_batch["prompt"], policy_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"
        # Add averaged stored metrics to logs
        for key, metrics in self._stored_metrics[train_eval].items():
            logs[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 _shift_right(self, input_ids):
        if self.decoder_start_token_id is None:
            raise ValueError(
                "model.config.decoder_start_token_id has to be defined. It is usually set to the pad_token_id."
            )

        # shift inputs to the right
        if is_torch_fx_proxy(input_ids):
            # Item assignment is not supported natively for proxies.
            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), self.decoder_start_token_id)
            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
        else:
            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
            shifted_input_ids[..., 0] = self.decoder_start_token_id

        if self.pad_token_id is None:
            raise ValueError("model.config.pad_token_id has to be defined.")
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id)

        return shifted_input_ids

    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("""\
        @inproceedings{xu2024contrastive,
            title        = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
            author       = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
            year         = 2024,
            booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
            publisher    = {OpenReview.net},
            url          = {https://openreview.net/forum?id=51iwkioZpn}
        }""")

        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="CPO",
            trainer_citation=citation,
            paper_title="Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation",
            paper_id="2401.08417",
        )
        model_card.save(os.path.join(self.args.output_dir, "README.md"))
