"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
logger = logging.get_logger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SquadDataTrainingArguments:
    model_type: str = field(default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)})
    data_dir: str = field(default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."})
    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.'})
    doc_stride: int = field(default=128, metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."})
    max_query_length: int = field(default=64, metadata={'help': 'The maximum number of tokens for the question. Questions longer than this will be truncated to this length.'})
    max_answer_length: int = field(default=30, metadata={'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.'})
    overwrite_cache: bool = field(default=False, metadata={"help": "Overwrite the cached training and evaluation sets"})
    version_2_with_negative: bool = field(default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."})
    null_score_diff_threshold: float = field(default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
    n_best_size: int = field(default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
    lang_id: int = field(default=0, metadata={'help': 'language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'})
    threads: int = field(default=1, metadata={"help": "multiple threads for converting example to features"})
class Split(Enum):
    train = "train"
    dev = "dev"
class SquadDataset(Dataset):
    args: SquadDataTrainingArguments
    features: List[SquadFeatures]
    mode: Split
    is_language_sensitive: bool
    def __init__(self, args: SquadDataTrainingArguments, tokenizer: PreTrainedTokenizer, limit_length: Optional[int] = None, mode: Union[str, Split] = Split.train, is_language_sensitive: Optional[bool] = False, cache_dir: Optional[str] = None, dataset_format: Optional[str] = "pt"):
        self.args = args
        self.is_language_sensitive = is_language_sensitive
        self.processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
        if isinstance(mode, str):
            try: mode = Split[mode]
            except KeyError: raise KeyError("mode is not a valid split name")
        self.mode = mode
        version_tag = "v2" if args.version_2_with_negative else "v1"
        cached_features_file = os.path.join(cache_dir if cache_dir is not None else args.data_dir, f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}")
        lock_path = cached_features_file + ".lock"
        with FileLock(lock_path):
            if os.path.exists(cached_features_file) and not args.overwrite_cache:
                start = time.time()
                self.old_features = torch.load(cached_features_file)
                self.features = self.old_features["features"]
                self.dataset = self.old_features.get("dataset", None)
                self.examples = self.old_features.get("examples", None)
                logger.info(f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start)
                if self.dataset is None or self.examples is None: logger.warning(f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in future run")
            else:
                if mode == Split.dev: self.examples = self.processor.get_dev_examples(args.data_dir)
                else: self.examples = self.processor.get_train_examples(args.data_dir)
                self.features, self.dataset = squad_convert_examples_to_features(examples=self.examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=mode == Split.train, threads=args.threads, return_dataset=dataset_format)
                start = time.time()
                torch.save({"features": self.features, "dataset": self.dataset, "examples": self.examples}, cached_features_file)
                logger.info(f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]")
    def __len__(self): return len(self.features)
    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
        feature = self.features[i]
        input_ids = torch.tensor(feature.input_ids, dtype=torch.long)
        attention_mask = torch.tensor(feature.attention_mask, dtype=torch.long)
        token_type_ids = torch.tensor(feature.token_type_ids, dtype=torch.long)
        cls_index = torch.tensor(feature.cls_index, dtype=torch.long)
        p_mask = torch.tensor(feature.p_mask, dtype=torch.float)
        is_impossible = torch.tensor(feature.is_impossible, dtype=torch.float)
        inputs = {"input_ids": input_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids}
        if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]: del inputs["token_type_ids"]
        if self.args.model_type in ["xlnet", "xlm"]:
            inputs.update({"cls_index": cls_index, "p_mask": p_mask})
            if self.args.version_2_with_negative: inputs.update({"is_impossible": is_impossible})
            if self.is_language_sensitive: inputs.update({"langs": (torch.ones(input_ids.shape, dtype=torch.int64) * self.args.lang_id)})
        if self.mode == Split.train:
            start_positions = torch.tensor(feature.start_position, dtype=torch.long)
            end_positions = torch.tensor(feature.end_position, dtype=torch.long)
            inputs.update({"start_positions": start_positions, "end_positions": end_positions})
        return inputs
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
