import json
import os
import pickle
import random
import time
from typing import Dict, List, Optional

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

from filelock import FileLock

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__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    This will be superseded by a framework-agnostic approach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,
        cache_dir: Optional[str] = None,
    ):
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"

        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(
            cache_dir if cache_dir is not None else directory,
            "cached_lm_{}_{}_{}".format(
                tokenizer.__class__.__name__,
                str(block_size),
                filename,
            ),
        )

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        lock_path = cached_features_file + ".lock"
        with FileLock(lock_path):

            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(
                    "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):
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"
        # 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())]

        batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)
        self.examples = batch_encoding["input_ids"]
        self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]

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

    def __getitem__(self, i) -> Dict[str, torch.tensor]:
        return self.examples[i]


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    This will be superseded by a framework-agnostic approach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

    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"
        assert os.path.isfile(ref_path), f"Ref file path {file_path} not found"
        # 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)
        logger.info("Use ref segment results at %s", ref_path)
        with open(file_path, encoding="utf-8") as f:
            data = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
        batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size)
        self.examples = batch_encoding["input_ids"]
        self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]

        # Get ref inf from file
        with open(ref_path, encoding="utf-8") as f:
            ref = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
        assert len(data) == len(ref)
        n = len(self.examples)
        for i in range(n):
            self.examples[i]["chinese_ref"] = torch.tensor(ref[i], dtype=torch.long)

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

    def __getitem__(self, i) -> Dict[str, torch.tensor]:
        return self.examples[i]


class LineByLineWithSOPTextDataset(Dataset):
    """
    Dataset for sentence order prediction task, prepare sentence pairs for SOP task
    """

    def __init__(self, tokenizer: PreTrainedTokenizer, file_dir: str, block_size: int):
        assert os.path.isdir(file_dir)
        logger.info(f"Creating features from dataset file folder at {file_dir}")
        self.examples = []
        # TODO: randomness could apply a random seed, ex. rng = random.Random(random_seed)
        # file path looks like ./dataset/wiki_1, ./dataset/wiki_2
        for file_name in os.listdir(file_dir):
            file_path = os.path.join(file_dir, file_name)
            assert os.path.isfile(file_path)
            article_open = False
            with open(file_path, encoding="utf-8") as f:
                original_lines = f.readlines()
                article_lines = []
                for line in original_lines:
                    if "<doc id=" in line:
                        article_open = True
                    elif "</doc>" in line:
                        article_open = False
                        document = [
                            tokenizer.convert_tokens_to_ids(tokenizer.tokenize(line))
                            for line in article_lines[1:]
                            if (len(line) > 0 and not line.isspace())
                        ]

                        examples = self.create_examples_from_document(document, block_size, tokenizer)
                        self.examples.extend(examples)
                        article_lines = []
                    else:
                        if article_open:
                            article_lines.append(line)

        logger.info("Dataset parse finished.")

    def create_examples_from_document(self, document, block_size, tokenizer, short_seq_prob=0.1):
        """Creates examples for a single document."""

        # Account for special tokens
        max_num_tokens = block_size - tokenizer.num_special_tokens_to_add(pair=True)

        # We *usually* want to fill up the entire sequence since we are padding
        # to `block_size` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
        # sequences to minimize the mismatch between pre-training and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `block_size` is a hard limit.
        target_seq_length = max_num_tokens
        if random.random() < short_seq_prob:
            target_seq_length = random.randint(2, max_num_tokens)

        # We DON'T just concatenate all of the tokens from a document into a long
        # sequence and choose an arbitrary split point because this would make the
        # next sentence prediction task too easy. Instead, we split the input into
        # segments "A" and "B" based on the actual "sentences" provided by the user
        # input.
        examples = []
        current_chunk = []  # a buffer stored current working segments
        current_length = 0
        i = 0
        while i < len(document):
            segment = document[i]  # get a segment
            if not segment:
                i += 1
                continue
            current_chunk.append(segment)  # add a segment to current chunk
            current_length += len(segment)  # overall token length
            # if current length goes to the target length or reaches the end of file, start building token a and b
            if i == len(document) - 1 or current_length >= target_seq_length:
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A` (first) sentence.
                    a_end = 1
                    # if current chunk has more than 2 sentences, pick part of it `A` (first) sentence
                    if len(current_chunk) >= 2:
                        a_end = random.randint(1, len(current_chunk) - 1)
                    # token a
                    tokens_a = []
                    for j in range(a_end):
                        tokens_a.extend(current_chunk[j])

                    # token b
                    tokens_b = []
                    for j in range(a_end, len(current_chunk)):
                        tokens_b.extend(current_chunk[j])

                    if len(tokens_a) == 0 or len(tokens_b) == 0:
                        continue

                    # switch tokens_a and tokens_b randomly
                    if random.random() < 0.5:
                        is_next = False
                        tokens_a, tokens_b = tokens_b, tokens_a
                    else:
                        is_next = True

                    def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens):
                        """Truncates a pair of sequences to a maximum sequence length."""
                        while True:
                            total_length = len(tokens_a) + len(tokens_b)
                            if total_length <= max_num_tokens:
                                break
                            trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
                            assert len(trunc_tokens) >= 1
                            # We want to sometimes truncate from the front and sometimes from the
                            # back to add more randomness and avoid biases.
                            if random.random() < 0.5:
                                del trunc_tokens[0]
                            else:
                                trunc_tokens.pop()

                    truncate_seq_pair(tokens_a, tokens_b, max_num_tokens)
                    assert len(tokens_a) >= 1
                    assert len(tokens_b) >= 1

                    # add special tokens
                    input_ids = tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b)
                    # add token type ids, 0 for sentence a, 1 for sentence b
                    token_type_ids = tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b)

                    example = {
                        "input_ids": torch.tensor(input_ids, dtype=torch.long),
                        "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
                        "sentence_order_label": torch.tensor(0 if is_next else 1, dtype=torch.long),
                    }
                    examples.append(example)
                current_chunk = []  # clear current chunk
                current_length = 0  # reset current text length
            i += 1  # go to next line
        return examples

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

    def __getitem__(self, i) -> Dict[str, torch.tensor]:
        return self.examples[i]


class TextDatasetForNextSentencePrediction(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,
        short_seq_probability=0.1,
        nsp_probability=0.5,
    ):
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"

        self.block_size = block_size - tokenizer.num_special_tokens_to_add(pair=True)
        self.short_seq_probability = short_seq_probability
        self.nsp_probability = nsp_probability

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

        self.tokenizer = tokenizer

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        lock_path = cached_features_file + ".lock"

        # Input file format:
        # (1) One sentence per line. These should ideally be actual sentences, not
        # entire paragraphs or arbitrary spans of text. (Because we use the
        # sentence boundaries for the "next sentence prediction" task).
        # (2) Blank lines between documents. Document boundaries are needed so
        # that the "next sentence prediction" task doesn't span between documents.
        #
        # Example:
        # I am very happy.
        # Here is the second sentence.
        #
        # A new document.

        with FileLock(lock_path):
            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.documents = [[]]
                with open(file_path, encoding="utf-8") as f:
                    while True:
                        line = f.readline()
                        if not line:
                            break
                        line = line.strip()

                        # Empty lines are used as document delimiters
                        if not line and len(self.documents[-1]) != 0:
                            self.documents.append([])
                        tokens = tokenizer.tokenize(line)
                        tokens = tokenizer.convert_tokens_to_ids(tokens)
                        if tokens:
                            self.documents[-1].append(tokens)

                logger.info(f"Creating examples from {len(self.documents)} documents.")
                self.examples = []
                for doc_index, document in enumerate(self.documents):
                    self.create_examples_from_document(document, doc_index)

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

    def create_examples_from_document(self, document: List[List[int]], doc_index: int):
        """Creates examples for a single document."""

        max_num_tokens = self.block_size - self.tokenizer.num_special_tokens_to_add(pair=True)

        # We *usually* want to fill up the entire sequence since we are padding
        # to `block_size` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
        # sequences to minimize the mismatch between pre-training and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `block_size` is a hard limit.
        target_seq_length = max_num_tokens
        if random.random() < self.short_seq_probability:
            target_seq_length = random.randint(2, max_num_tokens)

        current_chunk = []  # a buffer stored current working segments
        current_length = 0
        i = 0

        while i < len(document):
            segment = document[i]
            current_chunk.append(segment)
            current_length += len(segment)
            if i == len(document) - 1 or current_length >= target_seq_length:
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A`
                    # (first) sentence.
                    a_end = 1
                    if len(current_chunk) >= 2:
                        a_end = random.randint(1, len(current_chunk) - 1)

                    tokens_a = []
                    for j in range(a_end):
                        tokens_a.extend(current_chunk[j])

                    tokens_b = []

                    if len(current_chunk) == 1 or random.random() < self.nsp_probability:
                        is_random_next = True
                        target_b_length = target_seq_length - len(tokens_a)

                        # This should rarely go for more than one iteration for large
                        # corpora. However, just to be careful, we try to make sure that
                        # the random document is not the same as the document
                        # we're processing.
                        for _ in range(10):
                            random_document_index = random.randint(0, len(self.documents) - 1)
                            if random_document_index != doc_index:
                                break

                        random_document = self.documents[random_document_index]
                        random_start = random.randint(0, len(random_document) - 1)
                        for j in range(random_start, len(random_document)):
                            tokens_b.extend(random_document[j])
                            if len(tokens_b) >= target_b_length:
                                break
                        # We didn't actually use these segments so we "put them back" so
                        # they don't go to waste.
                        num_unused_segments = len(current_chunk) - a_end
                        i -= num_unused_segments
                    # Actual next
                    else:
                        is_random_next = False
                        for j in range(a_end, len(current_chunk)):
                            tokens_b.extend(current_chunk[j])

                    assert len(tokens_a) >= 1
                    assert len(tokens_b) >= 1

                    self.examples.append(
                        {"tokens_a": tokens_a, "tokens_b": tokens_b, "is_random_next": is_random_next}
                    )

                current_chunk = []
                current_length = 0

            i += 1

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

    def __getitem__(self, i):
        return self.examples[i]
