#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 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.
""" Finetuning the library models for sequence classification on GLUE."""
import json
# You can also adapt this script on your own text classification task. Pointers for this are left as comments.

import logging
import os
import random
import shutil
import sys
import time
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import frozendict
import torch
import datasets
import numpy as np
import tqdm
from datasets import load_dataset, load_metric

import transformers
from scipy.special import softmax
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    PretrainedConfig,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
import torch.nn.functional as F

from bias_bench.model import models, load_tokenizer
from bias_bench.util import CustomTrainingArguments

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.16.0")

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

glue_task_to_keys = {
    # GLUE
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}

super_glue_task_to_keys = {
    # SuperGLUE
    "boolq": ("passage", "question"),
    "cb": ("premise", "hypothesis"),
    "copa": ("premise", None), # choice1, choice2
    "multirc": ("paragraph", "question"), # answer
    "record": ("passage", "query"), # answers
    "rte": ("premise", "hypothesis"), #
    "wic": ("sentence1", "sentence2"),
    "wsc.fixed": ("text", None),
    "axb": ("sentence1", "sentence2"),
    "axg": ("sentence1", "sentence2"),
}

benchmark_to_tasks = {
    "glue": glue_task_to_keys,
    "super_glue": super_glue_task_to_keys,
}

logger = logging.getLogger(__name__)


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

    Using `HfArgumentParser` we can turn this class
    into argparse arguments to be able to specify them on
    the command line.
    """

    benchmark_name: Optional[str] = field(
        default='glue',
        metadata={
            "help": "The name of the benchmark to use (via the datasets library, e.g, 'glue' or 'super_glue')."
        },
    )

    task_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The name of the task to train on: "
            + ", ".join(glue_task_to_keys.keys()) + ' for GLUE'
            + ", ".join(super_glue_task_to_keys.keys()) + ' for SuperGLUE'
        },
    )
    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)."
        },
    )
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached preprocessed datasets or not."},
    )
    pad_to_max_length: bool = field(
        default=True,
        metadata={
            "help": "Whether to pad all samples to `max_seq_length`. "
            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
        },
    )
    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."
        },
    )
    train_file: Optional[str] = field(
        default=None,
        metadata={"help": "A csv or a json file containing the training data."},
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "A csv or a json file containing the validation data."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "A csv or a json file containing the test data."},
    )
    persistent_dir: str = field(
        default=None,
        metadata={"help": "Directory where all persistent data will be stored."},
    )

    early_stopping: str = field(
        default=True,
        metadata={"help": "Whether to use early stopping or not."},
    )

    def __post_init__(self):
        if self.benchmark_name is None:
            raise ValueError("You should specify a benchmark name, e.g., 'glue' or 'super_glue'.")

        if self.benchmark_name not in benchmark_to_tasks.keys():
            raise ValueError(
                "Unknown benchmark, you should pick one in "
                + ",".join(benchmark_to_tasks.keys())
            )

        print("Using benchmark:", self.benchmark_name)
        task_to_keys = benchmark_to_tasks[self.benchmark_name]

        if self.task_name is not None:
            self.task_name = self.task_name.lower()
            if self.task_name not in task_to_keys.keys():
                raise ValueError(
                    "Unknown task, you should pick one in "
                    + ",".join(task_to_keys.keys())
                )
        elif self.dataset_name is not None:
            pass
        elif self.train_file is None or self.validation_file is None:
            raise ValueError(
                "Need either a GLUE task, a training/validation file or a dataset name."
            )
        else:
            train_extension = self.train_file.split(".")[-1]
            assert train_extension in [
                "csv",
                "json",
            ], "`train_file` should be a csv or a json file."
            validation_extension = self.validation_file.split(".")[-1]
            assert (
                validation_extension == train_extension
            ), "`validation_file` should have the same extension (csv or json) as `train_file`."


@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 do you want to store the pretrained models downloaded from huggingface.co"
        },
    )
    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)."
        },
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
            "with private models)."
        },
    )
    model: str = field(
        default="BertForSequenceClassification",
        metadata={
            "help": "Model to load. These correspond to a HuggingFace class (e.g., BertForSequenceClassification)."
        },
    )
    bias_direction: str = field(
        default=None,
        metadata={
            "help": "Path to the file containing the pre-computed bias direction for SentenceDebias."
        },
    )
    projection_matrix: str = field(
        default=None,
        metadata={
            "help": "Path to the file containing the pre-computed projection matrix for INLP."
        },
    )


from copy import deepcopy

def preprocess_boolq(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    # Passage = context, Question = query
    result = tokenizer(
        examples["passage"],
        examples["question"],
        padding=padding,
        max_length=max_seq_length,
        truncation=True,
    )
    if "label" in examples and label_to_id is not None:
        result["label"] = [label_to_id[l] for l in examples["label"]]
    return result


def preprocess_cb_rte(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    # CB and RTE have premise / hypothesis
    result = tokenizer(
        examples["premise"],
        examples["hypothesis"],
        padding=padding,
        max_length=max_seq_length,
        truncation=True,
    )
    if "label" in examples and label_to_id is not None:
        result["label"] = [label_to_id[l] for l in examples["label"]]
    return result


def preprocess_wic(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    # WIC: sentence1, sentence2, and a target word (can optionally include in text)
    result = tokenizer(
        examples["sentence1"],
        examples["sentence2"],
        padding=padding,
        max_length=max_seq_length,
        truncation=True,
    )
    if "label" in examples and label_to_id is not None:
        result["label"] = [label_to_id[l] for l in examples["label"]]
    return result


def preprocess_copa_old(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    batch_size = len(examples['premise'])

    # Initialize result structure
    result = {
        'input_ids': [],
        'attention_mask': [],
        'token_type_ids': [],
    }

    for i in range(batch_size):
        premise = examples['premise'][i].strip()
        choice1 = examples['choice1'][i].strip()
        choice2 = examples['choice2'][i].strip()
        question = examples['question'][i]

        # Following original COPA methodology:
        # Concatenate context with each answer choice separately using [SEP] token
        if question == "cause":
            # For cause questions: "Why did [premise]?"
            # Format: [choice] [SEP] [premise]
            context1 = f"{choice1} [SEP] {premise}"
            context2 = f"{choice2} [SEP] {premise}"
        else:  # effect
            # For effect questions: "What happened as a result?"
            # Format: [premise] [SEP] [choice]
            context1 = f"{premise} [SEP] {choice1}"
            context2 = f"{premise} [SEP] {choice2}"

        # Tokenize both choices for this example
        choices = [context1, context2]

        tokenized = tokenizer(
            choices,
            padding=padding,
            max_length=max_seq_length,
            truncation=True,
        )

        # Add to results - each example has 2 choices
        result['input_ids'].append(tokenized['input_ids'])
        result['attention_mask'].append(tokenized['attention_mask'])
        result['token_type_ids'].append(tokenized['token_type_ids'])

    # Add labels if present
    if "label" in examples:
        result["labels"] = examples["label"]

    return result

def generic_preprocess_mc(
    examples,
    tokenizer,
    padding,
    max_seq_length,
    context_col,
    choice_cols=None,
    choices_column=None,
    question_col=None,
    replace_placeholder=False,
    copa_style=False,
    label_to_id=None,
):
    """
    Generic multiple-choice / pairwise preprocess:
      - context_col: name of column that holds the passage/context (string)
      - choice_cols: list of column names for choices (e.g. ['choice1','choice2']) OR
      - choices_column: single column name that itself contains a list of choices per example (e.g. 'entities')
      - question_col: optional (e.g. COPA's 'question' field or ReCoRD's 'query')
      - replace_placeholder: if True, replaces '@placeholder' in question_col with each candidate (ReCoRD)
      - copa_style: if True, use COPA ordering: when question == "cause" -> (choice, premise), else (premise, choice)
      - label_to_id: optional mapping from label strings to ints
    Returns a dict with keys: input_ids, attention_mask, (token_type_ids maybe), labels (if present).
    """
    batch_size = len(examples[context_col])
    result = {
        'input_ids': [],
        'attention_mask': [],
    }
    # token_type_ids are optional (some tokenizers don't return them)
    include_token_type = False

    for i in range(batch_size):
        context = examples[context_col][i].strip()

        # gather choices
        if choice_cols is not None:
            choices = [examples[c][i].strip() for c in choice_cols]
        elif choices_column is not None:
            # assume examples[choices_column][i] is an iterable of strings
            choices = [c.strip() for c in examples[choices_column][i]]
        else:
            raise ValueError("Either choice_cols or choices_column must be provided.")

        # Build pair lists according to flags
        pair_a = []
        pair_b = []

        for choice in choices:
            if replace_placeholder:
                # e.g. ReCoRD: context [SEP] query_with_placeholder_replaced_by_choice
                if question_col is None:
                    raise ValueError("question_col must be provided when replace_placeholder=True")
                query = examples[question_col][i].replace('@placeholder', choice).strip()
                pair_a.append(context)
                pair_b.append(query)
            elif copa_style:
                # e.g. COPA: question can be "cause" or "effect"
                if question_col is None:
                    raise ValueError("question_col must be provided when copa_style=True")
                qtype = examples[question_col][i]
                if qtype == "cause":
                    # format: [choice] [SEP] [premise]
                    pair_a.append(choice)
                    pair_b.append(context)
                else:  # "effect" or other -> [premise] [SEP] [choice]
                    pair_a.append(context)
                    pair_b.append(choice)
            else:
                # default: context first, choice second --> [context] [SEP] [choice]
                pair_a.append(context)
                pair_b.append(choice)

        # Tokenize the set of pairs for this example in one call
        tokenized = tokenizer(
            pair_a,
            pair_b,
            padding=padding,
            max_length=max_seq_length,
            truncation=True,
        )

        # Track whether token_type_ids exists
        if 'token_type_ids' in tokenized:
            include_token_type = True

        # Append tokenized lists for this example (shape: num_choices x seq_len)
        result['input_ids'].append(tokenized['input_ids'])
        result['attention_mask'].append(tokenized['attention_mask'])
        if 'token_type_ids' in tokenized:
            # ensure key exists (we'll add after loop if needed)
            result.setdefault('token_type_ids', []).append(tokenized['token_type_ids'])

    # Labels (map if label_to_id provided)
    if "label" in examples:
        if label_to_id is not None:
            result["labels"] = [label_to_id[l] for l in examples["label"]]
        else:
            result["labels"] = examples["label"]

    return result


# --- COPA wrapper ---
def preprocess_copa(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    """
    COPA expects:
      - context_col = 'premise'
      - choices in columns 'choice1' and 'choice2'
      - question in 'question' with values 'cause' or 'effect'
    """
    return generic_preprocess_mc(
        examples=examples,
        tokenizer=tokenizer,
        padding=padding,
        max_seq_length=max_seq_length,
        context_col='premise',
        choice_cols=['choice1', 'choice2'],
        question_col='question',
        copa_style=True,
        label_to_id=label_to_id,
    )

def preprocess_wsc_fixed(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    second_sentences = [
        f"Pronoun: {p}; Candidate: {c}"
        for p, c in zip(examples["span1_text"], examples["span2_text"])
    ]

    result = tokenizer(
        examples["text"],
        second_sentences,
        padding=padding,
        max_length=max_seq_length,
        truncation=True,
    )

    if "label" in examples:
        result["labels"] = examples["label"]  # already 0 or 1
    return result

def preprocess_record(examples, tokenizer, padding, max_seq_length, label_to_id=None, debug=False):
    tokenized = tokenizer(
        examples["passage"],
        examples["query"],
        truncation=True,
        padding="max_length",
        max_length=max_seq_length,
        return_offsets_mapping=True,
    )

    entity_spans = []
    labels = []
    answerss = []
    entitiess = []
    idxs = []

    for i, (passage, query, entities, answers, offset_mapping, idx) in enumerate(zip(
        examples["passage"],
        examples["query"],
        examples["entity_spans"],   # dict with "start", "end", "text"
        examples.get("answers", [[] for _ in examples["passage"]]),
        tokenized["offset_mapping"],
        examples["idx"],
    )):
        sequence_ids = tokenized.sequence_ids(i)  # sequence id list for this example

        # Only consider passage tokens (sequence_id == 0)
        passage_token_indices = [j for j, seq_id in enumerate(sequence_ids) if seq_id == 0]

        spans = []
        labs = []
        ent_texts = []

        for start, end, text in zip(entities["start"], entities["end"], entities["text"]):
            # collect token indices fully inside the char span
            token_spans = [
                j for j in passage_token_indices
                if offset_mapping[j][0] is not None
                   and not (offset_mapping[j][1] <= start or offset_mapping[j][0] >= end)
            ]

            if token_spans:
                span_tuple = (token_spans[0], token_spans[-1] + 1)
                spans.append(span_tuple)

                def normalize(s):
                    return s.lower().strip(" .,'\"-")

                norm_answers = [normalize(a) for a in answers]
                norm_text = normalize(text)
                label = 1 if norm_text in norm_answers else 0

                labs.append(label)
                ent_texts.append(text)

        entity_spans.append(spans)
        labels.append(labs)
        answerss.append(answers)
        entitiess.append(ent_texts)
        idxs.append(idx)

        # Debug: show 1–2 examples of mapping
        if debug and i < 2:
            print("=" * 60)
            print(f"Passage: {passage[:150]}...")
            print(f"Query:   {query}")
            print(f"Entities: {list(zip(entities['text'], entities['start'], entities['end']))}")
            print(f"Tokenized passage (first 40 tokens):")
            print(tokenizer.convert_ids_to_tokens(tokenized['input_ids'][i][:40]))
            print(f"Spans mapped: {spans}")
            print(f"Entities mapped: {ent_texts}")
            print(f"Labels: {labs}")

    # Add aligned data back to tokenized dict
    tokenized["entity_spans"] = entity_spans
    tokenized["labels"] = labels
    tokenized["answers"] = answerss
    tokenized["entities"] = entitiess
    tokenized["idx"] = idxs

    return tokenized


def preprocess_multirc(examples, tokenizer, padding, max_seq_length, label_to_id=None):
    """
    Prepare MultiRC examples for sequence classification.
    Each row corresponds to one candidate answer with a True/False label.
    """
    encodings = tokenizer(
        examples["paragraph"],
        [q + " " + a for q, a in zip(examples["question"], examples["answer"])],
        padding=padding,
        truncation=True,
        max_length=max_seq_length,
    )

    # Convert label strings/booleans to integers
    labels = []
    for lab in examples["label"]:
        if isinstance(lab, str):
            labels.append(label_to_id.get(lab, 0) if label_to_id else int(lab == "True"))
        else:
            labels.append(int(lab))

    encodings["labels"] = labels
    return encodings


from torch.utils.data import DataLoader
import torch

def record_collator(features):
    batch = {}

    # Collect available keys (some models don’t use token_type_ids)
    for key in ["input_ids", "attention_mask", "token_type_ids"]:
        if key in features[0] and features[0][key] is not None:
            batch[key] = torch.tensor([f[key] for f in features], dtype=torch.long)

    # Entity spans stay as a list (ragged structure)
    batch["entity_spans"] = [f["entity_spans"] for f in features]

    # Pad labels to same number of entities
    max_ents = max(len(f["labels"]) for f in features)
    labels = torch.full((len(features), max_ents), -100, dtype=torch.long)
    for i, f in enumerate(features):
        labels[i, : len(f["labels"])] = torch.tensor(f["labels"], dtype=torch.long)
    batch["labels"] = labels

    return batch




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.

    start_time = time.time()

    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, CustomTrainingArguments)
    )
    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()

    # check if results are already computed
    result_file = f'{training_args.output_dir}/eval_results.json'
    if os.path.exists(result_file):
        data = json.load(open(result_file))
        if 'eval_accuracy' in data:
            score = data['eval_accuracy']
        elif 'eval_matthews_correlation' in data:
            score = data['eval_matthews_correlation']
        elif 'eval_pearson' in data:
            score = data['eval_pearson']
        elif 'eval_f1' in data:
            score = data['eval_f1']
        else:
            raise ValueError("No score found in the results file", result_file, data)
        print('Score:', score)
        if score == 0.0:
            print('Result 0.0 for ', result_file)

            training_args.seed = training_args.seed + 10
            training_args.output_dir = training_args.output_dir + f'_{training_args.seed}'
            print('New output dir:', training_args.output_dir)
        else:
            print('Results already computed', result_file)
            #return
    else:
        print(result_file)


    # 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)],
    )

    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: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # 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 specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. 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 data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.benchmark_name, data_args.task_name, cache_dir=model_args.cache_dir
        )
    elif 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,
        )
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        data_files = {
            "train": data_args.train_file,
            "validation": data_args.validation_file,
        }

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                train_extension = data_args.train_file.split(".")[-1]
                test_extension = data_args.test_file.split(".")[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                data_files["test"] = data_args.test_file
            else:
                raise ValueError(
                    "Need either a GLUE task or a test file for `do_predict`."
                )

        for key in data_files.keys():
            logger.info(f"load a local file for {key}: {data_files[key]}")

        if data_args.train_file.endswith(".csv"):
            # Loading a dataset from local csv files
            raw_datasets = load_dataset(
                "csv", data_files=data_files, cache_dir=model_args.cache_dir
            )
        else:
            # Loading a dataset from local json files
            raw_datasets = load_dataset(
                "json", data_files=data_files, cache_dir=model_args.cache_dir
            )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            if data_args.task_name == 'record':
                label_list = [0] # dummy values
                num_labels = 1
            else:
                label_list = raw_datasets["train"].features["label"].names
                num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = raw_datasets["train"].features["label"].dtype in [
            "float32",
            "float64",
        ]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = raw_datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)
    task_to_keys = benchmark_to_tasks[data_args.benchmark_name]

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    print(model_args.model)
    model_name_or_path = model_args.model_name_or_path

    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name
        else model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    tokenizer = load_tokenizer(model_args.model_name_or_path)

    # Load the model.
    kwargs = {}
    if model_args.bias_direction is not None:
        # Load the pre-computed bias direction for SentenceDebias.
        bias_direction = torch.load(model_args.bias_direction)
        kwargs["bias_direction"] = bias_direction

    if model_args.projection_matrix is not None:
        # Load the pre-computed projection matrix for INLP.
        projection_matrix = torch.load(model_args.projection_matrix)
        kwargs["projection_matrix"] = projection_matrix

    print("=" * 40)
    model_type = model_args.model
    if data_args.task_name in ['copa']:
        model_type = model_type.replace('ForSequenceClassification', 'ForMultipleChoice')
    elif data_args.task_name in ['record']:
        model_type = model_type.replace('ForSequenceClassification', 'ForRecord')
        #model_type = model_type.replace('ForSequenceClassification', 'ForReCoRD')

    print(f"Loading: {model_type}")
    model = getattr(models, model_type)(
        model_args.model_name_or_path, config=config, **kwargs
    )
    print("=" * 40)

    if 'gpt2' in model_args.model_name_or_path:
        # Define a padding token
        tokenizer.pad_token = tokenizer.eos_token
        model.config.pad_token_id = tokenizer.pad_token_id

    # Preprocessing the raw_datasets
    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [
            name for name in raw_datasets["train"].column_names if name != "label"
        ]
        if (
            "sentence1" in non_label_column_names
            and "sentence2" in non_label_column_names
        ):
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (
        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
        and data_args.task_name is not None
        and not is_regression
    ):
        # Some have all caps in their config, some don't.
        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {
                i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)
            }
        else:
            logger.warning(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None and not is_regression:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    if label_to_id is not None:
        model.config.label2id = label_to_id
        model.config.id2label = {id: label for label, id in config.label2id.items()}
    elif data_args.task_name is not None and not is_regression:
        model.config.label2id = {l: i for i, l in enumerate(label_list)}
        model.config.id2label = {id: label for label, id in config.label2id.items()}

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({data_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(data_args.max_seq_length, tokenizer.model_max_length)

    def default_preprocess_function(examples):
        # Tokenize the texts
        args = (
            (examples[sentence1_key],)
            if sentence2_key is None
            else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(
            *args, padding=padding, max_length=max_seq_length, truncation=True
        )

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [
                (label_to_id[l] if l != -1 else -1) for l in examples["label"]
            ]
        return result

    superglue_preprocessors = {
        "boolq": preprocess_boolq,
        "cb": preprocess_cb_rte,
        "rte": preprocess_cb_rte,
        "wic": preprocess_wic,
        "wsc.fixed": preprocess_wsc_fixed,  # WSC might need custom coref resolution
        "copa": preprocess_copa,
        "multirc": preprocess_multirc,
        "record": preprocess_record,
    }

    if data_args.benchmark_name == "super_glue" and data_args.task_name in superglue_preprocessors:
        preprocess_function = lambda examples: superglue_preprocessors[data_args.task_name](
            examples, tokenizer, padding, max_seq_length, label_to_id
        )
    else:
        preprocess_function = default_preprocess_function


    def preprocess(ds):
        if data_args.task_name == 'record':
            with training_args.main_process_first(desc="dataset map pre-processing"):
                return ds.map(
                    preprocess_function,
                    batched=True,
                    load_from_cache_file=not data_args.overwrite_cache,
                    desc="Running tokenizer on dataset",
                    remove_columns=ds["train"].column_names,
                )

        else:
            with training_args.main_process_first(desc="dataset map pre-processing"):
                return ds.map(
                    preprocess_function,
                    batched=True,
                    load_from_cache_file=not data_args.overwrite_cache,
                    desc="Running tokenizer on dataset",
                )
    raw_datasets = preprocess(raw_datasets)

    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets["train"]
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(min(len(train_dataset), data_args.max_train_samples)))

    if training_args.do_eval:
        if (
            "validation" not in raw_datasets
            and "validation_matched" not in raw_datasets
        ):
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets[
            "validation_matched" if data_args.task_name == "mnli" else "validation"
        ]
        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))

    if (
        training_args.do_predict
        or data_args.task_name is not None
        or data_args.test_file is not None
    ):
        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        #predict_dataset = raw_datasets[
        #    "test_matched" if data_args.task_name == "mnli" else "test"
        #]
        #if data_args.max_predict_samples is not None:
        #    predict_dataset = predict_dataset.select(
        #        range(data_args.max_predict_samples)
        #    )

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

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric(data_args.benchmark_name, data_args.task_name, trust_remote_code=True)
    else:
        metric = load_metric("accuracy")


    if data_args.task_name == 'multirc':
        def compute_metrics(p):
            preds = np.argmax(p.predictions, axis=1)

            # p.label_ids should be the true labels, but we also need the idx from the dataset
            idx_list = eval_dataset['idx']

            pred_dicts = [
                {"idx": idx, "prediction": int(pred)}
                for idx, pred in zip(idx_list, preds)
            ]
            ref_dicts = [i for i in p.label_ids]
            return metric.compute(predictions=pred_dicts, references=ref_dicts)
    elif data_args.task_name == 'record':
        gold_answers = {
            frozendict.frozendict(example["idx"]): example["answers"]
            for example in eval_dataset
        }
        unique_idxs = sorted(
            gold_answers.keys(),
            key=lambda x: (x["passage"], x["query"])
        )

        def compute_metrics(eval_pred):
            logits, labels = eval_pred
            probs = torch.sigmoid(torch.tensor(logits))

            pred_dict = {}
            for i, (entities, idx) in enumerate(zip(eval_dataset["entities"], eval_dataset["idx"])):
                o_idx = frozendict.frozendict(idx)

                # filter out padding logits
                num_entities = len(entities)
                if num_entities == 0:
                    if o_idx not in pred_dict:
                        pred_dict[o_idx] = ""  # no entities → empty prediction
                    continue

                ent_probs = probs[i, :num_entities]

                # predict all entities with prob > threshold
                pred_dict[o_idx] = entities[torch.argmax(ent_probs).item()]

            # Format predictions
            predictions = [
                {
                    "idx": dict(idx),
                    "prediction_text": pred_dict.get(idx, "")
                }
                for idx in unique_idxs
            ]

            # Format references
            references = [
                {
                    "idx": dict(idx),
                    "answers": gold_answers.get(idx, [])
                }
                for idx in unique_idxs
            ]

            results = metric.compute(predictions=predictions, references=references)
            return {"f1": results["f1"], "exact_match": results["exact_match"]}

    else:
        # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
        # predictions and label_ids field) and has to return a dictionary string to float.
        def compute_metrics(p: EvalPrediction):
            preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
            preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
            if data_args.task_name is not None:
                result = metric.compute(predictions=preds, references=p.label_ids)
                if len(result) > 1:
                    result["combined_score"] = np.mean(list(result.values())).item()
                return result
            elif is_regression:
                return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
            else:
                return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}


    if data_args.task_name == 'record':
        data_collator = record_collator
    elif data_args.pad_to_max_length:
        data_collator = default_data_collator
    elif training_args.fp16:
        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
    else:
        data_collator = None

    training_args.save_strategy = 'no'

    #eval_fraction = 0.5  # Evaluate after 10% of the dataset

    # Calculate eval_steps
    #print(len(eval_dataset), training_args.per_device_eval_batch_size)
    #eval_steps = max(1, int(len(eval_dataset) * eval_fraction // training_args.per_device_eval_batch_size))

    # we evaluate 2 times per epoch
    n_gpus = torch.cuda.device_count()

    effective_batch_size = training_args.per_device_train_batch_size * n_gpus
    steps_per_epoch = len(train_dataset) // effective_batch_size
    eval_steps = steps_per_epoch // 2
    #eval_steps = 10

    print(f"Evaluation steps set to: {eval_steps}")

    if data_args.early_stopping:

        def copy_and_update_training_args(original_args, **overrides):
            """
            Create a new TrainingArguments instance by copying all properties from an existing one
            and overriding specified values, excluding internal properties.
            """
            # Copy all properties from the original TrainingArguments object
            original_args_dict = vars(original_args)

            # Filter out internal properties that cannot be passed to the constructor
            EXCLUDED = {'distributed_state', 'deepspeed_plugin'}
            filtered_args_dict = {k: v for k, v in original_args_dict.items() if not k.startswith("_") and k not in EXCLUDED}

            # Update with any provided overrides
            filtered_args_dict.update(overrides)

            # Return a new TrainingArguments instance with the filtered properties
            return TrainingArguments(**filtered_args_dict)

        print("Using early stopping")

        training_args = copy_and_update_training_args(
            training_args,
            save_strategy="steps",
            evaluation_strategy="steps",
            eval_steps=eval_steps,
            save_steps=eval_steps,
            load_best_model_at_end=True,
            metric_for_best_model="eval_loss",
            greater_is_better=False,
            save_total_limit=1,
        )
        print("Updated training arguments for early stopping:", training_args)

    # Initialize our Trainer

    trainer_cls = Trainer

    trainer = trainer_cls(
        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,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        data_collator=data_collator,
    )

    # Training
    if training_args.do_train:
        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)
        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.save_model()  # Saves the tokenizer too for easy upload
        metrics['time'] = time.time() - start_time
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        #trainer.save_state()

    # Evaluation
    raw_eval_dataset = eval_dataset
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        eval_start_time = time.time()

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        eval_datasets = [eval_dataset]
        main_task = data_args.task_name
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            eval_datasets.append(raw_datasets["validation_mismatched"])
            #todo the current approach results in mnli-mm results to be saved in the same file as mnli results,
            # and due to the order the mnli-mm results overwrite the mnli results, but mnli is typically reported in GLUE
            # this is not important if the bootsrap is used as the raw results are used then (with unique file names)
        elif data_args.task_name == 'cb':
            # we also evaluate the diagnostic ax.g task
            tasks.append('axg')
            axg_dataset = load_dataset('super_glue', 'axg')['test']
            axg_dataset = preprocess(axg_dataset)
            eval_datasets.append(axg_dataset)
            # we also evaluate the diagnostic axb task
            tasks.append('axb')
            axb_dataset = load_dataset('super_glue', 'axb')['test']
            axb_dataset = axb_dataset.rename_column('sentence1', 'premise').rename_column('sentence2', 'hypothesis')
            axb_dataset = preprocess(axb_dataset)
            eval_datasets.append(axb_dataset)


        for eval_dataset, task in zip(eval_datasets, tasks):
            metrics = trainer.evaluate(eval_dataset=eval_dataset)

            max_eval_samples = (
                data_args.max_eval_samples
                if data_args.max_eval_samples is not None
                else len(eval_dataset)
            )
            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
            metrics['time'] = time.time() - eval_start_time
            metrics['total_time'] = time.time() - start_time

            name = 'eval'
            if task != main_task:
                name += f'_{task}'
            trainer.log_metrics(name, metrics)
            trainer.save_metrics(name, metrics)

    if training_args.do_predict:
        logger.info("*** Predict ***")

        if False:
            # Loop to handle MNLI double evaluation (matched, mis-matched)
            tasks = [data_args.task_name]
            predict_datasets = [predict_dataset]
            if data_args.task_name == "mnli":
                tasks.append("mnli-mm")
                predict_datasets.append(raw_datasets["test_mismatched"])

            for predict_dataset, task in zip(predict_datasets, tasks):
                # Removing the `label` columns because it contains -1 and Trainer won't like that.
                #predict_dataset = predict_dataset.remove_columns("label")
                predictions = trainer.predict(
                    predict_dataset, metric_key_prefix="predict"
                ).predictions
                predictions = (
                    np.squeeze(predictions)
                    if is_regression
                    else np.argmax(predictions, axis=1)
                )

                output_predict_file = os.path.join(
                    training_args.output_dir, f"predict_results_{task}.txt"
                )
                if trainer.is_world_process_zero():
                    with open(output_predict_file, "w") as writer:
                        logger.info(f"***** Predict results {task} *****")
                        writer.write("index\tprediction\n")
                        for index, item in enumerate(predictions):
                            if is_regression:
                                writer.write(f"{index}\t{item:3.3f}\n")
                            elif task == 'record':
                                entities = predict_dataset["entities"][index]
                                if len(entities) == 0:
                                    prediction_text = ""
                                else:
                                    ent_probs = torch.sigmoid(torch.tensor(item[:len(entities)]))
                                    chosen = torch.argmax(ent_probs).item()

                                writer.write(f"{index}\t{prediction_text}\n")
                            else:
                                item = label_list[item]
                                writer.write(f"{index}\t{item}\n")

        logger.info("*** Predict Eval ***")
        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        predict_datasets = [raw_eval_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            predict_datasets.append(raw_datasets["validation_mismatched"])
        elif data_args.task_name == 'cb':
            # we also evaluate the diagnostic ax.g task
            tasks.append('axg')
            axg_dataset = load_dataset('super_glue', 'axg')['test']
            axg_dataset = preprocess(axg_dataset)
            predict_datasets.append(axg_dataset)
            # we also evaluate the diagnostic axb task
            tasks.append('axb')
            axb_dataset = load_dataset('super_glue', 'axb')['test']
            axb_dataset = axb_dataset.rename_column('sentence1', 'premise').rename_column('sentence2', 'hypothesis')
            axb_dataset = preprocess(axb_dataset)
            predict_datasets.append(axb_dataset)


        for predict_dataset, task in zip(predict_datasets, tasks):
            #predict_dataset = predict_dataset.remove_columns("label")
            predictions = trainer.predict(
                predict_dataset, metric_key_prefix="predict"
            ).predictions
            predictions = (
                np.squeeze(predictions)
                if is_regression or task == 'record'
                else np.argmax(predictions, axis=1)
            )

            output_predict_file = os.path.join(
                training_args.output_dir, f"eval_results_{task}.txt"
            )
            if trainer.is_world_process_zero():
                with open(output_predict_file, "w") as writer:
                    logger.info(f"***** Eval Predict results {task} *****")
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(tqdm.tqdm(predictions, desc="Writing predictions")):
                        if is_regression:
                            writer.write(f"{index}\t{item:3.3}\n")
                        elif task == 'record':
                            entities = predict_dataset["entities"][index]
                            if len(entities) == 0:
                                prediction_text = ""
                            else:
                                ent_probs = torch.sigmoid(torch.tensor(item[:len(entities)]))
                                chosen = torch.argmax(ent_probs).item()
                                prediction_text = entities[chosen]

                            writer.write(f"{index}\t{prediction_text}\n")
                        else:
                            item = label_list[item]
                            writer.write(f"{index}\t{item}\n")

    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "text-classification",
    }
    if data_args.task_name is not None:
        kwargs["language"] = "en"
        kwargs["dataset_tags"] = "glue"
        kwargs["dataset_args"] = data_args.task_name
        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"

    # delete all checkpoints at the end
    if training_args.do_train:
        for f in os.listdir(training_args.output_dir):
            checkpoint_path = os.path.join(training_args.output_dir, f)

            # Check if the directory starts with 'checkpoint-' and is indeed a directory
            if f.startswith("checkpoint-") and os.path.isdir(checkpoint_path):
                print(f"Deleting checkpoint: {checkpoint_path}")
                shutil.rmtree(checkpoint_path)  # Delete the checkpoint directory


    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)

    if data_args.early_stopping:
        # remove any checkpoint form the output directory
        for f in os.listdir(training_args.output_dir):
            checkpoint_path = os.path.join(training_args.output_dir, f)

            # Check if the directory starts with 'checkpoint-' and is indeed a directory
            if f.startswith("checkpoint-") and os.path.isdir(checkpoint_path):
                print(f"Deleting checkpoint: {checkpoint_path}")
                shutil.rmtree(checkpoint_path)

def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()
