#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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.
"""
Fine-tuning XLNet for question answering with beam search using 🤗 Accelerate.
"""
# You can also adapt this script on your own question answering task. Pointers for this are left as comments.

import argparse
import json
import logging
import math
import os
import random
from pathlib import Path

import datasets
import evaluate
import numpy as np
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from utils_qa import postprocess_qa_predictions_with_beam_search

import transformers
from transformers import (
    AdamW,
    DataCollatorWithPadding,
    EvalPrediction,
    SchedulerType,
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetTokenizerFast,
    default_data_collator,
    get_scheduler,
)
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.41.0.dev0")

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

logger = get_logger(__name__)


def save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"):
    """
    Save results while prefixing metric names.

    Args:
        results: (:obj:`dict`):
            A dictionary of results.
        output_dir: (:obj:`str`):
            An output directory.
        file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):
            An output file name.
        metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):
            A metric name prefix.
    """
    # Prefix all keys with metric_key_prefix + '_'
    for key in list(results.keys()):
        if not key.startswith(f"{metric_key_prefix}_"):
            results[f"{metric_key_prefix}_{key}"] = results.pop(key)

    with open(os.path.join(output_dir, file_name), "w") as f:
        json.dump(results, f, indent=4)


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
    )
    parser.add_argument(
        "--preprocessing_num_workers", type=int, default=1, help="A csv or a json file containing the training data."
    )
    parser.add_argument("--do_predict", action="store_true", help="Eval the question answering model")
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
    )
    parser.add_argument(
        "--test_file", type=str, default=None, help="A csv or a json file containing the Prediction data."
    )
    parser.add_argument(
        "--max_seq_length",
        type=int,
        default=384,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
            " sequences shorter will be padded if `--pad_to_max_length` is passed."
        ),
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=True,
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--doc_stride",
        type=int,
        default=128,
        help="When splitting up a long document into chunks how much stride to take between chunks.",
    )
    parser.add_argument(
        "--n_best_size",
        type=int,
        default=20,
        help="The total number of n-best predictions to generate when looking for an answer.",
    )
    parser.add_argument(
        "--null_score_diff_threshold",
        type=float,
        default=0.0,
        help=(
            "The threshold used to select the null answer: if the best answer has a score that is less than "
            "the score of the null answer minus this threshold, the null answer is selected for this example. "
            "Only useful when `version_2_with_negative=True`."
        ),
    )
    parser.add_argument(
        "--version_2_with_negative",
        action="store_true",
        help="If true, some of the examples do not have an answer.",
    )
    parser.add_argument(
        "--max_answer_length",
        type=int,
        default=30,
        help=(
            "The maximum length of an answer that can be generated. This is needed because the start "
            "and end predictions are not conditioned on one another."
        ),
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--max_eval_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--max_predict_samples",
        type=int,
        default=None,
        help="For debugging purposes or quicker training, truncate the number of prediction examples to this",
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to load in all available experiment trackers from the environment and use them for logging.",
    )
    args = parser.parse_args()

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

    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."

    return args


def main():
    args = parse_args()

    # 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_qa_beam_search_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Get the datasets: you can either provide your own CSV/JSON/TXT 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 column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
    else:
        data_files = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
            extension = args.train_file.split(".")[-1]
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
            extension = args.validation_file.split(".")[-1]
        if args.test_file is not None:
            data_files["test"] = args.test_file
            extension = args.test_file.split(".")[-1]
        raw_datasets = load_dataset(extension, data_files=data_files, field="data")
    # 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
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = XLNetConfig.from_pretrained(args.model_name_or_path)
    tokenizer = XLNetTokenizerFast.from_pretrained(args.model_name_or_path)
    model = XLNetForQuestionAnswering.from_pretrained(
        args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
    )

    # Preprocessing the datasets.
    # Preprocessing is slightly different for training and evaluation.
    column_names = raw_datasets["train"].column_names

    question_column_name = "question" if "question" in column_names else column_names[0]
    context_column_name = "context" if "context" in column_names else column_names[1]
    answer_column_name = "answers" if "answers" in column_names else column_names[2]

    # Padding side determines if we do (question|context) or (context|question).
    pad_on_right = tokenizer.padding_side == "right"

    if args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the "
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )

    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)

    # Training preprocessing
    def prepare_train_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            return_special_tokens_mask=True,
            return_token_type_ids=True,
            padding="max_length",
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")
        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).
        special_tokens = tokenized_examples.pop("special_tokens_mask")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []
        tokenized_examples["is_impossible"] = []
        tokenized_examples["cls_index"] = []
        tokenized_examples["p_mask"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_examples["input_ids"][i]
            if tokenizer.cls_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.cls_token_id)
            elif tokenizer.bos_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.bos_token_id)
            else:
                cls_index = 0
            tokenized_examples["cls_index"].append(cls_index)

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples["token_type_ids"][i]
            for k, s in enumerate(special_tokens[i]):
                if s:
                    sequence_ids[k] = 3
            context_idx = 1 if pad_on_right else 0

            # Build the p_mask: non special tokens and context gets 0.0, the others get 1.0.
            # The cls token gets 1.0 too (for predictions of empty answers).
            tokenized_examples["p_mask"].append(
                [
                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
                    for k, s in enumerate(sequence_ids)
                ]
            )

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            answers = examples[answer_column_name][sample_index]
            # If no answers are given, set the cls_index as answer.
            if len(answers["answer_start"]) == 0:
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
                tokenized_examples["is_impossible"].append(1.0)
            else:
                # Start/end character index of the answer in the text.
                start_char = answers["answer_start"][0]
                end_char = start_char + len(answers["text"][0])

                # Start token index of the current span in the text.
                token_start_index = 0
                while sequence_ids[token_start_index] != context_idx:
                    token_start_index += 1

                # End token index of the current span in the text.
                token_end_index = len(input_ids) - 1
                while sequence_ids[token_end_index] != context_idx:
                    token_end_index -= 1
                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
                    tokenized_examples["start_positions"].append(cls_index)
                    tokenized_examples["end_positions"].append(cls_index)
                    tokenized_examples["is_impossible"].append(1.0)
                else:
                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
                    # Note: we could go after the last offset if the answer is the last word (edge case).
                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
                        token_start_index += 1
                    tokenized_examples["start_positions"].append(token_start_index - 1)
                    while offsets[token_end_index][1] >= end_char:
                        token_end_index -= 1
                    tokenized_examples["end_positions"].append(token_end_index + 1)
                    tokenized_examples["is_impossible"].append(0.0)

        return tokenized_examples

    if "train" not in raw_datasets:
        raise ValueError("--do_train requires a train dataset")
    train_dataset = raw_datasets["train"]
    if args.max_train_samples is not None:
        # We will select sample from whole data if argument is specified
        train_dataset = train_dataset.select(range(args.max_train_samples))
    # Create train feature from dataset
    with accelerator.main_process_first():
        train_dataset = train_dataset.map(
            prepare_train_features,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not args.overwrite_cache,
            desc="Running tokenizer on train dataset",
        )
    if args.max_train_samples is not None:
        # Number of samples might increase during Feature Creation, We select only specified max samples
        train_dataset = train_dataset.select(range(args.max_train_samples))

    # Validation preprocessing
    def prepare_validation_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            return_special_tokens_mask=True,
            return_token_type_ids=True,
            padding="max_length",
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")

        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).
        special_tokens = tokenized_examples.pop("special_tokens_mask")

        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
        # corresponding example_id and we will store the offset mappings.
        tokenized_examples["example_id"] = []

        # We still provide the index of the CLS token and the p_mask to the model, but not the is_impossible label.
        tokenized_examples["cls_index"] = []
        tokenized_examples["p_mask"] = []

        for i, input_ids in enumerate(tokenized_examples["input_ids"]):
            # Find the CLS token in the input ids.
            if tokenizer.cls_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.cls_token_id)
            elif tokenizer.bos_token_id in input_ids:
                cls_index = input_ids.index(tokenizer.bos_token_id)
            else:
                cls_index = 0
            tokenized_examples["cls_index"].append(cls_index)

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples["token_type_ids"][i]
            for k, s in enumerate(special_tokens[i]):
                if s:
                    sequence_ids[k] = 3
            context_idx = 1 if pad_on_right else 0

            # Build the p_mask: non special tokens and context gets 0.0, the others 1.0.
            tokenized_examples["p_mask"].append(
                [
                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
                    for k, s in enumerate(sequence_ids)
                ]
            )

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            tokenized_examples["example_id"].append(examples["id"][sample_index])

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            tokenized_examples["offset_mapping"][i] = [
                (o if sequence_ids[k] == context_idx else None)
                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
            ]

        return tokenized_examples

    if "validation" not in raw_datasets:
        raise ValueError("--do_eval requires a validation dataset")
    eval_examples = raw_datasets["validation"]
    if args.max_eval_samples is not None:
        # We will select sample from whole data
        eval_examples = eval_examples.select(range(args.max_eval_samples))
    # Validation Feature Creation
    with accelerator.main_process_first():
        eval_dataset = eval_examples.map(
            prepare_validation_features,
            batched=True,
            num_proc=args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not args.overwrite_cache,
            desc="Running tokenizer on validation dataset",
        )

    if args.max_eval_samples is not None:
        # During Feature creation dataset samples might increase, we will select required samples again
        eval_dataset = eval_dataset.select(range(args.max_eval_samples))

    if args.do_predict:
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_examples = raw_datasets["test"]
        if args.max_predict_samples is not None:
            # We will select sample from whole data
            predict_examples = predict_examples.select(range(args.max_predict_samples))
        # Predict Feature Creation
        with accelerator.main_process_first():
            predict_dataset = predict_examples.map(
                prepare_validation_features,
                batched=True,
                num_proc=args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not args.overwrite_cache,
                desc="Running tokenizer on prediction dataset",
            )
            if args.max_predict_samples is not None:
                # During Feature creation dataset samples might increase, we will select required samples again
                predict_dataset = predict_dataset.select(range(args.max_predict_samples))

    # Log a few random samples from the training set:
    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # DataLoaders creation:
    if args.pad_to_max_length:
        # If padding was already done ot max length, we use the default data collator that will just convert everything
        # to tensors.
        data_collator = default_data_collator
    else:
        # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
        # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
        # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))

    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
    )

    eval_dataset_for_model = eval_dataset.remove_columns(["example_id", "offset_mapping"])
    eval_dataloader = DataLoader(
        eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
    )

    if args.do_predict:
        predict_dataset_for_model = predict_dataset.remove_columns(["example_id", "offset_mapping"])
        predict_dataloader = DataLoader(
            predict_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
        )

    # Post-processing:
    def post_processing_function(examples, features, predictions, stage="eval"):
        # Post-processing: we match the start logits and end logits to answers in the original context.
        predictions, scores_diff_json = postprocess_qa_predictions_with_beam_search(
            examples=examples,
            features=features,
            predictions=predictions,
            version_2_with_negative=args.version_2_with_negative,
            n_best_size=args.n_best_size,
            max_answer_length=args.max_answer_length,
            start_n_top=model.config.start_n_top,
            end_n_top=model.config.end_n_top,
            output_dir=args.output_dir,
            prefix=stage,
        )
        # Format the result to the format the metric expects.
        if args.version_2_with_negative:
            formatted_predictions = [
                {"id": k, "prediction_text": v, "no_answer_probability": scores_diff_json[k]}
                for k, v in predictions.items()
            ]
        else:
            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]

        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
        return EvalPrediction(predictions=formatted_predictions, label_ids=references)

    metric = evaluate.load("squad_v2" if args.version_2_with_negative else "squad")

    def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
        """
        Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor

        Args:
            start_or_end_logits(:obj:`tensor`):
                This is the output predictions of the model. We can only enter either start or end logits.
            eval_dataset: Evaluation dataset
            max_len(:obj:`int`):
                The maximum length of the output tensor. ( See the model.eval() part for more details )
        """

        step = 0
        # create a numpy array and fill it with -100.
        logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float32)
        # Now since we have create an array now we will populate it with the outputs gathered using accelerator.gather_for_metrics
        for i, output_logit in enumerate(start_or_end_logits):  # populate columns
            # We have to fill it such that we have to take the whole tensor and replace it on the newly created array
            # And after every iteration we have to change the step

            batch_size = output_logit.shape[0]
            cols = output_logit.shape[1]
            if step + batch_size < len(dataset):
                logits_concat[step : step + batch_size, :cols] = output_logit
            else:
                logits_concat[step:, :cols] = output_logit[: len(dataset) - step]

            step += batch_size

        return logits_concat

    # Optimizer
    # Split weights in two groups, one with weight decay and the other not.
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": args.weight_decay,
        },
        {
            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
            "weight_decay": 0.0,
        },
    ]
    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps
        if overrode_max_train_steps
        else args.max_train_steps * accelerator.num_processes,
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
    )

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # Figure out how many steps we should save the Accelerator states
    checkpointing_steps = args.checkpointing_steps
    if checkpointing_steps is not None and checkpointing_steps.isdigit():
        checkpointing_steps = int(checkpointing_steps)

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        experiment_config = vars(args)
        # TensorBoard cannot log Enums, need the raw value
        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
        accelerator.init_trackers("qa_beam_search_no_trainer", experiment_config)

    # Train!
    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    starting_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            checkpoint_path = args.resume_from_checkpoint
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            checkpoint_path = path
            path = os.path.basename(checkpoint_path)

        accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
        accelerator.load_state(checkpoint_path)
        # Extract `epoch_{i}` or `step_{i}`
        training_difference = os.path.splitext(path)[0]

        if "epoch" in training_difference:
            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
            resume_step = None
            completed_steps = starting_epoch * num_update_steps_per_epoch
        else:
            # need to multiply `gradient_accumulation_steps` to reflect real steps
            resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
            starting_epoch = resume_step // len(train_dataloader)
            completed_steps = resume_step // args.gradient_accumulation_steps
            resume_step -= starting_epoch * len(train_dataloader)

    # update the progress_bar if load from checkpoint
    progress_bar.update(completed_steps)

    for epoch in range(starting_epoch, args.num_train_epochs):
        model.train()
        if args.with_tracking:
            total_loss = 0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We skip the first `n` batches in the dataloader when resuming from a checkpoint
            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
        else:
            active_dataloader = train_dataloader
        for step, batch in enumerate(active_dataloader):
            with accelerator.accumulate(model):
                outputs = model(**batch)
                loss = outputs.loss
                # We keep track of the loss at each epoch
                if args.with_tracking:
                    total_loss += loss.detach().float()

                accelerator.backward(loss)

                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                completed_steps += 1

            if isinstance(checkpointing_steps, int):
                if completed_steps % checkpointing_steps == 0:
                    accelerator.save_state(f"step_{completed_steps}")

            if completed_steps >= args.max_train_steps:
                break

        if args.push_to_hub and epoch < args.num_train_epochs - 1:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(
                args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
            )
            if accelerator.is_main_process:
                tokenizer.save_pretrained(args.output_dir)
                api.upload_folder(
                    commit_message=f"Training in progress epoch {epoch}",
                    folder_path=args.output_dir,
                    repo_id=repo_id,
                    repo_type="model",
                    token=args.hub_token,
                )

    # initialize all lists to collect the batches
    all_start_top_log_probs = []
    all_start_top_index = []
    all_end_top_log_probs = []
    all_end_top_index = []
    all_cls_logits = []

    model.eval()

    for step, batch in enumerate(eval_dataloader):
        with torch.no_grad():
            outputs = model(**batch)
            start_top_log_probs = outputs.start_top_log_probs
            start_top_index = outputs.start_top_index
            end_top_log_probs = outputs.end_top_log_probs
            end_top_index = outputs.end_top_index
            cls_logits = outputs.cls_logits

            if not args.pad_to_max_length:  # necessary to pad predictions and labels for being gathered
                start_top_log_probs = accelerator.pad_across_processes(start_top_log_probs, dim=1, pad_index=-100)
                start_top_index = accelerator.pad_across_processes(start_top_index, dim=1, pad_index=-100)
                end_top_log_probs = accelerator.pad_across_processes(end_top_log_probs, dim=1, pad_index=-100)
                end_top_index = accelerator.pad_across_processes(end_top_index, dim=1, pad_index=-100)
                cls_logits = accelerator.pad_across_processes(cls_logits, dim=1, pad_index=-100)

            all_start_top_log_probs.append(accelerator.gather_for_metrics(start_top_log_probs).cpu().numpy())
            all_start_top_index.append(accelerator.gather_for_metrics(start_top_index).cpu().numpy())
            all_end_top_log_probs.append(accelerator.gather_for_metrics(end_top_log_probs).cpu().numpy())
            all_end_top_index.append(accelerator.gather_for_metrics(end_top_index).cpu().numpy())
            all_cls_logits.append(accelerator.gather_for_metrics(cls_logits).cpu().numpy())

    max_len = max([x.shape[1] for x in all_end_top_log_probs])  # Get the max_length of the tensor

    # concatenate all numpy arrays collected above
    start_top_log_probs_concat = create_and_fill_np_array(all_start_top_log_probs, eval_dataset, max_len)
    start_top_index_concat = create_and_fill_np_array(all_start_top_index, eval_dataset, max_len)
    end_top_log_probs_concat = create_and_fill_np_array(all_end_top_log_probs, eval_dataset, max_len)
    end_top_index_concat = create_and_fill_np_array(all_end_top_index, eval_dataset, max_len)
    cls_logits_concat = np.concatenate(all_cls_logits, axis=0)

    # delete the list of numpy arrays
    del start_top_log_probs
    del start_top_index
    del end_top_log_probs
    del end_top_index
    del cls_logits

    outputs_numpy = (
        start_top_log_probs_concat,
        start_top_index_concat,
        end_top_log_probs_concat,
        end_top_index_concat,
        cls_logits_concat,
    )
    prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
    eval_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
    logger.info(f"Evaluation metrics: {eval_metric}")

    if args.do_predict:
        # initialize all lists to collect the batches

        all_start_top_log_probs = []
        all_start_top_index = []
        all_end_top_log_probs = []
        all_end_top_index = []
        all_cls_logits = []

        model.eval()

        for step, batch in enumerate(predict_dataloader):
            with torch.no_grad():
                outputs = model(**batch)
                start_top_log_probs = outputs.start_top_log_probs
                start_top_index = outputs.start_top_index
                end_top_log_probs = outputs.end_top_log_probs
                end_top_index = outputs.end_top_index
                cls_logits = outputs.cls_logits

                if not args.pad_to_max_length:  # necessary to pad predictions and labels for being gathered
                    start_top_log_probs = accelerator.pad_across_processes(start_top_log_probs, dim=1, pad_index=-100)
                    start_top_index = accelerator.pad_across_processes(start_top_index, dim=1, pad_index=-100)
                    end_top_log_probs = accelerator.pad_across_processes(end_top_log_probs, dim=1, pad_index=-100)
                    end_top_index = accelerator.pad_across_processes(end_top_index, dim=1, pad_index=-100)
                    cls_logits = accelerator.pad_across_processes(cls_logits, dim=1, pad_index=-100)

                all_start_top_log_probs.append(accelerator.gather_for_metrics(start_top_log_probs).cpu().numpy())
                all_start_top_index.append(accelerator.gather_for_metrics(start_top_index).cpu().numpy())
                all_end_top_log_probs.append(accelerator.gather_for_metrics(end_top_log_probs).cpu().numpy())
                all_end_top_index.append(accelerator.gather_for_metrics(end_top_index).cpu().numpy())
                all_cls_logits.append(accelerator.gather_for_metrics(cls_logits).cpu().numpy())

        max_len = max([x.shape[1] for x in all_end_top_log_probs])  # Get the max_length of the tensor

        # concatenate all numpy arrays collected above
        start_top_log_probs_concat = create_and_fill_np_array(all_start_top_log_probs, predict_dataset, max_len)
        start_top_index_concat = create_and_fill_np_array(all_start_top_index, predict_dataset, max_len)
        end_top_log_probs_concat = create_and_fill_np_array(all_end_top_log_probs, predict_dataset, max_len)
        end_top_index_concat = create_and_fill_np_array(all_end_top_index, predict_dataset, max_len)
        cls_logits_concat = np.concatenate(all_cls_logits, axis=0)

        # delete the list of numpy arrays
        del start_top_log_probs
        del start_top_index
        del end_top_log_probs
        del end_top_index
        del cls_logits

        outputs_numpy = (
            start_top_log_probs_concat,
            start_top_index_concat,
            end_top_log_probs_concat,
            end_top_index_concat,
            cls_logits_concat,
        )

        prediction = post_processing_function(predict_examples, predict_dataset, outputs_numpy)
        predict_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
        logger.info(f"Predict metrics: {predict_metric}")

    if args.with_tracking:
        log = {
            "squad_v2" if args.version_2_with_negative else "squad": eval_metric,
            "train_loss": total_loss,
            "epoch": epoch,
            "step": completed_steps,
        }
        if args.do_predict:
            log["squad_v2_predict" if args.version_2_with_negative else "squad_predict"] = predict_metric

        accelerator.log(log)

    if args.checkpointing_steps == "epoch":
        accelerator.save_state(f"epoch_{epoch}")

    if args.output_dir is not None:
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        unwrapped_model.save_pretrained(
            args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
        )
        if accelerator.is_main_process:
            tokenizer.save_pretrained(args.output_dir)
            if args.push_to_hub:
                api.upload_folder(
                    commit_message="End of training",
                    folder_path=args.output_dir,
                    repo_id=repo_id,
                    repo_type="model",
                    token=args.hub_token,
                )

            logger.info(json.dumps(eval_metric, indent=4))
            save_prefixed_metrics(eval_metric, args.output_dir)


if __name__ == "__main__":
    main()
