import logging
import os
import pickle
import time

import torch
from torch.utils.data.dataset import Dataset

from ...tokenization_utils import PreTrainedTokenizer
from ...trainer import torch_distributed_zero_first


logger = logging.getLogger(__name__)


class TextDataset(Dataset):
    """
    This will be superseded by a framework-agnostic approach
    soon.
    """

    def __init__(
        self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, local_rank=-1,
    ):
        assert os.path.isfile(file_path)

        block_size = block_size - tokenizer.num_special_tokens_to_add(pair=False)

        directory, filename = os.path.split(file_path)
        cached_features_file = os.path.join(
            directory, "cached_lm_{}_{}_{}".format(tokenizer.__class__.__name__, str(block_size), filename,),
        )

        with torch_distributed_zero_first(local_rank):
            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.

            if os.path.exists(cached_features_file) and not overwrite_cache:
                start = time.time()
                with open(cached_features_file, "rb") as handle:
                    self.examples = pickle.load(handle)
                logger.info(
                    f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
                )

            else:
                logger.info(f"Creating features from dataset file at {directory}")

                self.examples = []
                with open(file_path, encoding="utf-8") as f:
                    text = f.read()

                tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))

                for i in range(0, len(tokenized_text) - block_size + 1, block_size):  # Truncate in block of block_size
                    self.examples.append(
                        tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])
                    )
                # Note that we are losing the last truncated example here for the sake of simplicity (no padding)
                # If your dataset is small, first you should loook for a bigger one :-) and second you
                # can change this behavior by adding (model specific) padding.

                start = time.time()
                with open(cached_features_file, "wb") as handle:
                    pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
                logger.info(
                    f"Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
                )

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i) -> torch.Tensor:
        return torch.tensor(self.examples[i], dtype=torch.long)


class LineByLineTextDataset(Dataset):
    """
    This will be superseded by a framework-agnostic approach
    soon.
    """

    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, local_rank=-1):
        assert os.path.isfile(file_path)
        # Here, we do not cache the features, operating under the assumption
        # that we will soon use fast multithreaded tokenizers from the
        # `tokenizers` repo everywhere =)
        logger.info("Creating features from dataset file at %s", file_path)

        with open(file_path, encoding="utf-8") as f:
            lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]

        lines = lines[:50_000]
        batch_encoding = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)
        self.examples = batch_encoding["input_ids"]

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i) -> torch.Tensor:
        return torch.tensor(self.examples[i], dtype=torch.long)
