#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
"""
import logging
import os
import sys
import warnings
from copy import deepcopy
from dataclasses import dataclass, field
from typing import Optional,Tuple,List

import datasets
import evaluate
import nltk  # Here to have a nice missing dependency error message early on
import numpy as np
from datasets import load_dataset
from filelock import FileLock
import fnmatch
import torch
import deepspeed
import transformers
from transformers import (
    AutoConfig,
    GPT2Config,
    AutoModelForCausalLM,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    BloomForCausalLM,
    BloomTokenizerFast,
    BloomConfig,
    CTRLLMHeadModel,
    CTRLTokenizer,
    CTRLConfig,
    GenerationMixin,
    GPT2LMHeadModel,
    GPT2Tokenizer,
    GPTJForCausalLM,
    GPTJConfig,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OpenAIGPTConfig,
    OPTForCausalLM,
    OPTConfig,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    TransfoXLConfig,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLMConfig,
    XLNetLMHeadModel,
    XLNetTokenizer,
    XLNetConfig,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    set_seed,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, is_offline_mode, send_example_telemetry
from transformers.utils.versions import require_version
from transformers.trainer_pt_utils import LabelSmoother

from peft import (
    PeftType,
    LoraConfig,
    TaskType,
    get_peft_model
)

sys.path.append(os.path.abspath('.'))

from hift import HiFTSeq2SeqTrainer,GetCallBack

logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
logger = logging.getLogger(__name__)
os.environ["WANDB_DISABLED"] = "true"
MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
IGNORE_TOKEN_ID = -100
MODEL_CLASSES = {
    "gpt2": (GPT2LMHeadModel, GPT2Tokenizer,GPT2Config),
    "geo":(AutoModelForCausalLM,AutoTokenizer,AutoConfig),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer,CTRLConfig),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,OpenAIGPTConfig),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer,XLNetConfig),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer,TransfoXLConfig),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer,XLMConfig),
    "gptj": (GPTJForCausalLM, AutoTokenizer,GPTJConfig),
    "bloom": (BloomForCausalLM, BloomTokenizerFast,BloomConfig),
    "llama": (AutoModelForCausalLM, AutoTokenizer,AutoConfig),
    "opt": (OPTForCausalLM, GPT2Tokenizer,OPTConfig),
}


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.27.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

logger = logging.getLogger(__name__)

try:
    nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
    if is_offline_mode():
        raise LookupError(
            "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
        )
    with FileLock(".lock") as lock:
        nltk.download("punkt", quiet=True)

@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
    )
    model_type:str= field(
        default="gpt2",
        metadata={"help": ("Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()) )},
    )
    padding_side: str = field(
        default="right", metadata={"help": "The padding side in tokenizer"}
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )
    resize_position_embeddings: Optional[bool] = field(
        default=None,
        metadata={
            "help": (
                "Whether to automatically resize the position embeddings if `max_source_length` exceeds "
                "the model's position embeddings."
            )
        },
    )

@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    lang: Optional[str] = field(default=None, metadata={"help": "Language id for summarization."})

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    source_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
    )
    target_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
            )
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_source_length: Optional[int] = field(
        default=200,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    max_target_length: Optional[int] = field(
        default=343,
        metadata={
            "help": (
                "The maximum total sequence length for target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    val_max_target_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`. "
                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                "during ``evaluate`` and ``predict``."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to model maximum sentence length. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                "efficient on GPU but very bad for TPU."
            )
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                "value if set."
            )
        },
    )
    num_beams: Optional[int] = field(
        default=1,
        metadata={
            "help": (
                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                "which is used during ``evaluate`` and ``predict``."
            )
        },
    )
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )
    source_prefix: Optional[str] = field(
        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
    )

    forced_bos_token: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The token to force as the first generated token after the decoder_start_token_id. "
                "Useful for multilingual models like mBART where the first generated token"
                "needs to be the target language token (Usually it is the target language token)"
            )
        },
    )

    def __post_init__(self):
        if (
            self.dataset_name is None
            and self.train_file is None
            and self.validation_file is None
            and self.test_file is None
        ):
            raise ValueError("Need either a dataset name or a training, validation, or test file.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
            if self.test_file is not None:
                extension = self.test_file.split(".")[-1]
                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length

@dataclass
class TrainingArguments(Seq2SeqTrainingArguments):
    model_max_length: int = field(
        default=512,
        metadata={
            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )
    pretraining_tp: int = field(
        default=1,
        metadata={
            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )

@dataclass
class HiFTArguments(ModelArguments):
    HiTaskType: str = field(
        default="CAUSAL_LM",
        metadata={"help": ("HiTaskType should be consistent with PEFT TaskType" )},
    )
    group_element: int = field(
        default=1,
        metadata={"help": ("number element for each group parameters" )},
    )
    optimizer_strategy: str = field(
        default="down2up",
        metadata={"help": ("optimizer strategy of ['down2up','down2up','random']" )},
    )
    hier_tuning: bool = field(
        default=False,
        metadata={
            "help": (
                "hierarchical optimization for LLMS"
            )
        },
    )
    lora_tuning:bool = field(
        default=False,
        metadata={
            "help": (
                "whether using lora tuning"
            )
        },
    )
    freeze_layers: List[str] = field(
        default_factory=list,
        metadata={
            "help": (
                "Index of the frozen layer"
            )
        },
    )



datasets_name_mapping = {
    "amazon_reviews_multi": ("review_body", "review_title"),
    "big_patent": ("description", "abstract"),
    "cnn_dailymail": ("article", "highlights"),
    "orange_sum": ("text", "summary"),
    "pn_summary": ("article", "summary"),
    "psc": ("extract_text", "summary_text"),
    "samsum": ("dialogue", "summary"),
    "thaisum": ("body", "summary"),
    "xglue": ("news_body", "news_title"),
    "xsum": ("document", "summary"),
    "wiki_summary": ("article", "highlights"),
    "multi_news": ("document", "summary"),
    "e2e_nlg":("meaning_representation", "human_reference"),
}


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((HiFTArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    if model_args.use_auth_token is not None:
        warnings.warn(
            "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
            FutureWarning,
        )
        if model_args.token is not None:
            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
        model_args.token = model_args.use_auth_token
    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_summarization", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    if data_args.source_prefix is None and model_args.model_name_or_path in [
        "t5-small",
        "t5-base",
        "t5-large",
        "t5-3b",
        "t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
            "`--source_prefix 'summarize: ' `"
        )

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files this script will use the first column for the full texts and the second column for the
    # summaries (unless you specify column names for this with the `source_column` and `target_column` arguments).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    model_class, tokenizer_class,model_config = MODEL_CLASSES[model_args.model_type]
    config = model_config.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    if training_args.pretraining_tp >1:
        config.pretraining_tp = training_args.pretraining_tp
    # if training_args.gradient_checkpointing:
    # config.gradient_checkpointing = training_args.gradient_checkpointing
    tokenizer = tokenizer_class.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        model_max_length=training_args.model_max_length,
        padding_side=model_args.padding_side,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    model = model_class.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    if model_args.lora_tuning:
        peft_config = LoraConfig(
            peft_type = PeftType.LORA,
            lora_alpha=16,
            lora_dropout=0.1,
            r=8,
            bias="none",
            task_type=TaskType.CAUSAL_LM)
        model = get_peft_model(model, peft_config)
    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))
    model.config.decoder_start_token_id = model.config.bos_token_id
    if model.config.decoder_start_token_id is None:
        model.config.decoder_start_token_id = model.config.bos_token_id

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        column_names = raw_datasets["validation"].column_names
    elif training_args.do_predict:
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        column_names = raw_datasets["test"].column_names
    else:
        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
        return

    # Get the column names for input/target.
    dataset_columns = datasets_name_mapping.get(data_args.dataset_name, None)
    if data_args.source_column is None:
        source_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
    else:
        source_column = data_args.source_column
        if source_column not in column_names:
            raise ValueError(
                f"--source_column' value '{data_args.sourcec_column}' needs to be one of: {', '.join(column_names)}"
            )
    if data_args.target_column is None:
        target_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
    else:
        target_column = data_args.target_column
        if target_column not in column_names:
            raise ValueError(
                f"--target_column' value '{data_args.target_column}' needs to be one of: {', '.join(column_names)}"
            )

    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
        logger.warning(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for "
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )
    def preprocess_function_for_generation(examples):
        IGNORE_TOKEN_ID = -100
        if training_args.label_smoothing_factor >0:
            IGNORE_TOKEN_ID = LabelSmoother.ignore_index
        SEPTOKEN=" ### "
        inputs, targets = [], []
        for i in range(len(examples[source_column])):
            if examples[source_column][i] and examples[target_column][i]:
                src = examples[source_column][i].strip().rstrip()+SEPTOKEN
                tgt = examples[target_column][i].strip().rstrip()
                inputs.append(src)
                targets.append(tgt)
        assert len(inputs) == len(targets)
        model_inputs = tokenizer(inputs, 
            max_length=data_args.max_source_length,
            padding=padding,
            truncation=True)
        labels = tokenizer(targets,
                max_length=data_args.max_target_length, 
                padding=padding, 
                truncation=True)
        model_inputs["labels"] = labels["input_ids"]
        return model_inputs
    def preprocess_function(examples):
        # remove pairs where at least one record is None
        IGNORE_TOKEN_ID = -100
        if training_args.label_smoothing_factor >0:
            IGNORE_TOKEN_ID = LabelSmoother.ignore_index
        inputs, targets = [], []
        for i in range(len(examples[source_column])):
            if examples[source_column][i] and examples[target_column][i]:
                inputs.append(examples[source_column][i].strip().rstrip())
                targets.append(examples[target_column][i].strip().rstrip())
        SEPTOKEN = tokenizer.tokenize("###")
        source_tokens,target_tokens=[],[]
        for inp,tar in zip(inputs,targets):
            source_tokens.append(tokenizer.tokenize(inp))
            target_tokens.append(tokenizer.tokenize(tar))
        
        examples = [" ".join(inp+SEPTOKEN+tar) for inp,tar in zip(source_tokens,target_tokens)]
        model_inputs = tokenizer(examples, 
                        max_length=tokenizer.model_max_length,
                        padding=padding,
                        truncation=True)
        labels = deepcopy(model_inputs["input_ids"])
        # IGNORE input
        # for inp,target in zip(source_tokens,labels):
        #     target[:len(inp)+1] = [IGNORE_TOKEN_ID] * (len(inp) + 1)
        # If we are padding here, replace all tokenizer.pad_token_id in the labels by IGNORE_TOKEN_ID when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            labels =  [
                [(l if l != tokenizer.pad_token_id else IGNORE_TOKEN_ID) for l in label] for label in labels
            ]

        model_inputs["labels"] = labels
        return model_inputs

    assert training_args.do_train
    train_dataset = raw_datasets["train"]
    if data_args.max_train_samples is not None:
        max_train_samples = min(len(train_dataset), data_args.max_train_samples)
        train_dataset = train_dataset.select(range(max_train_samples))
    with training_args.main_process_first(desc="train dataset map pre-processing"):
        train_dataset=train_dataset.map(
        lambda x : tokenizer(
            list(map(lambda a,b: a + " ### " + b, x[source_column], x[target_column])), 
            truncation="only_second",
            max_length=tokenizer.model_max_length, 
            padding=padding), 
            batched=True,
            desc="Running tokenizer on train dataset")
        train_dataset = train_dataset.map(lambda x: {'labels':x['input_ids']})

    max_target_length = data_args.val_max_target_length
    eval_dataset = raw_datasets["validation"]
    if data_args.max_eval_samples is not None:
        max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
        eval_dataset = eval_dataset.select(range(max_eval_samples))
    with training_args.main_process_first(desc="validation dataset map pre-processing"):
        eval_dataset=eval_dataset.map(
        lambda x : tokenizer(
            list(map(lambda a,b: a + " ### " + b, x[source_column], x[target_column])), 
            truncation="only_second",
            max_length=tokenizer.model_max_length, 
            padding=padding), 
            batched=True,
            desc="Running tokenizer on validation dataset")
        eval_dataset = train_dataset.map(lambda x: {'labels':x['input_ids']})
    # Data collator
    label_pad_token_id = IGNORE_TOKEN_ID if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    data_collator = DataCollatorForSeq2Seq(
        tokenizer,
        model=model,
        label_pad_token_id=label_pad_token_id,
        pad_to_multiple_of=8 if training_args.fp16 else None,
    )

    # Metric
    metric = evaluate.load("rouge")
    def postprocess_text(preds, labels):
        preds = [pred.strip() for pred in preds]
        labels = [label.strip() for label in labels]
        # rougeLSum expects newline after each sentence
        preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
        labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
        return preds, labels

    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]
        # Replace -100s used for padding as we can't decode them
        preds = np.where(preds != IGNORE_TOKEN_ID, preds, tokenizer.pad_token_id)
        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
        labels = np.where(labels != IGNORE_TOKEN_ID, labels, tokenizer.pad_token_id)
        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

        # Some simple post-processing
        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
        result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
        result = {k: round(v * 100, 4) for k, v in result.items()}
        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
        result["gen_len"] = np.mean(prediction_lens)
        return result
    training_args.generation_max_length = (
        training_args.generation_max_length
        if training_args.generation_max_length is not None
        else data_args.val_max_target_length
    )
    training_args.generation_num_beams = (
        data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
    )
    # Initialize our Trainer
    if model_args.hier_tuning:#hier_tuning
        trainer = HiFTSeq2SeqTrainer(
            hiFThandler = GetCallBack(model_args.model_name_or_path),
            HiTaskType = model_args.HiTaskType,
            group_element = model_args.group_element,
            strategy = model_args.optimizer_strategy,
            hier_tuning= model_args.hier_tuning,
            lora_tuning = model_args.lora_tuning,
            freeze_layers = model_args.freeze_layers,
            model=model,
            train_dataset=train_dataset if training_args.do_train else None,
            eval_dataset=eval_dataset if training_args.do_eval else None,
            compute_metrics=compute_metrics if training_args.predict_with_generate else None,
            tokenizer=tokenizer,
            data_collator=data_collator,
            args=training_args
        )
    else:
        trainer = Seq2SeqTrainer(
            model=model,
            args=training_args,
            train_dataset=train_dataset if training_args.do_train else None,
            eval_dataset=eval_dataset if training_args.do_eval else None,
            tokenizer=tokenizer,
            data_collator=data_collator,
            compute_metrics=compute_metrics if training_args.predict_with_generate else None,
        )

    # Training
    checkpoint = None
    if training_args.resume_from_checkpoint is not None:
        checkpoint = training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
        checkpoint = last_checkpoint
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
    trainer.save_model()  # Saves the tokenizer too for easy upload

    metrics = train_result.metrics
    max_train_samples = (
            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
        )
    metrics["train_samples"] = min(max_train_samples, len(train_dataset))

    trainer.log_metrics("train", metrics)
    trainer.save_metrics("train", metrics)
    trainer.save_state()

if __name__ == "__main__":
    main()
