# Copyright 2024 the LlamaFactory team.
#
# 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.

from dataclasses import dataclass
from typing import Any, Dict, Sequence

import torch
from transformers import DataCollatorForSeq2Seq


@dataclass
class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
    r"""
    Data collator for pairwise data.
    """

    def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
        r"""
        Pads batched data to the longest sequence in the batch.

        We generate 2 * n examples where the first n examples represent chosen examples and
        the last n examples represent rejected examples.
        """
        concatenated_features = []
        for key in ("chosen", "rejected"):
            for feature in features:
                target_feature = {
                    "input_ids": feature["{}_input_ids".format(key)],
                    "attention_mask": feature["{}_attention_mask".format(key)],
                    "labels": feature["{}_labels".format(key)],
                }
                if "pixel_values" in feature:
                    target_feature["pixel_values"] = feature["pixel_values"]

                if "{}_token_type_ids".format(key) in feature:
                    target_feature["token_type_ids"] = feature["{}_token_type_ids".format(key)]

                concatenated_features.append(target_feature)

        return super().__call__(concatenated_features)


@dataclass
class KTODataCollatorWithPadding(DataCollatorForSeq2Seq):
    r"""
    Data collator for KTO data.
    """

    def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
        target_features = []
        kl_features = []
        kto_tags = []
        for feature in features:
            target_feature = {
                "input_ids": feature["input_ids"],
                "attention_mask": feature["attention_mask"],
                "labels": feature["labels"],
            }
            kl_feature = {
                "input_ids": feature["kl_input_ids"],
                "attention_mask": feature["kl_attention_mask"],
                "labels": feature["kl_labels"],
            }
            if "pixel_values" in feature:
                target_feature["pixel_values"] = feature["pixel_values"]

            if "token_type_ids" in feature:
                target_feature["token_type_ids"] = feature["token_type_ids"]
                kl_feature["token_type_ids"] = feature["kl_token_type_ids"]

            target_features.append(target_feature)
            kl_features.append(kl_feature)
            kto_tags.append(feature["kto_tags"])

        batch = super().__call__(target_features)
        kl_batch = super().__call__(kl_features)
        batch["kl_input_ids"] = kl_batch["input_ids"]
        batch["kl_attention_mask"] = kl_batch["attention_mask"]
        batch["kl_labels"] = kl_batch["labels"]
        if "token_type_ids" in batch:
            batch["kl_token_type_ids"] = kl_batch["token_type_ids"]

        batch["kto_tags"] = torch.tensor(kto_tags)
        return batch