# coding=utf-8
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#
# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Megatron tokenizers. just using huggingface implementation."""
from functools import wraps

from transformers import AutoTokenizer
from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding
from megatron.core.datasets.megatron_tokenizer import MegatronTokenizer


def build_tokenizer_wrapper(build_tokenizer):
    """Initialize tokenizer."""
    @wraps(build_tokenizer)
    def wrapper(args, **kargs):
        if args.tokenizer_type == "PretrainedFromHF":
            if args.rank == 0:
                print(' > building PretrainFromHF tokenizer. Vocab file is un-used, '
                    'loading tokenizer from pre-trained model', flush=True)

            if args.tokenizer_name_or_path is None:
                raise ValueError("Missing tokenizer_name_or_path while building PretrainFromHF tokenizer.")

            hf_tokenizer_kwargs = dict()
            if hasattr(args, "tokenizer_kwargs") and args.tokenizer_kwargs:
                if len(args.tokenizer_kwargs) % 2 != 0:
                    raise ValueError("The token name and token value must be entered in pairs.")

                for i in range(0, len(args.tokenizer_kwargs), 2):
                    hf_tokenizer_kwargs[args.tokenizer_kwargs[i]] = \
                        args.tokenizer_kwargs[i + 1]

            tokenizer = _AutoTokenizer(
                args.tokenizer_name_or_path,
                vocab_extra_ids=args.vocab_extra_ids,
                model_max_length=args.seq_length,
                use_fast=args.tokenizer_not_use_fast,
                **hf_tokenizer_kwargs
            )

            # Add vocab size (if not already set from a checkpoint).
            if getattr(args, "padded_vocab_size", None) is None:
                args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size,
                                                                args)
        else:
            tokenizer = build_tokenizer(args, **kargs)
        return tokenizer
    return wrapper


class _AutoTokenizer(MegatronTokenizer):
    """AutoTokenizer for Hf Pretrained model loading."""

    def __init__(self, tokenizer_name_or_path, vocab_extra_ids, model_max_length, use_fast, **kwargs):
        name = tokenizer_name_or_path
        super().__init__(name)
        hf_tokenizer_kwargs = kwargs
        if vocab_extra_ids > 0:
            hf_tokenizer_kwargs["additional_special_tokens"] = [f"<extra_id_{_id}>" for _id in range(vocab_extra_ids)]

        hf_tokenizer_kwargs["model_max_length"] = model_max_length
        hf_tokenizer_kwargs["use_fast"] = use_fast
        hf_tokenizer_kwargs["trust_remote_code"] = False
        hf_tokenizer_kwargs["local_files_only"] = True
        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, **hf_tokenizer_kwargs)
        if self.tokenizer.pad_token_id is None:
            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
        self.encoder = self.tokenizer.get_vocab()
        self.decoder = {v: k for k, v in self.encoder.items()}

    @property
    def vocab_size(self):
        return len(self.tokenizer)  # vocab_size doesn't contain additional tokens

    @property
    def vocab(self):
        return {
            **{special_token: self.tokenizer.convert_tokens_to_ids(special_token)
               for special_token in self.tokenizer.additional_special_tokens},
            **self.tokenizer.vocab,
        }

    @property
    def inv_vocab(self):
        return {v: k for k, v in self.vocab.items()}

    def tokenize(self, text):
        return self.tokenizer.encode(text)

    def detokenize(self, token_ids):
        return self.tokenizer.decode(token_ids)

    @property
    def eod(self):
        return self.eos

    @property
    def eos_token_id(self):
        return self.tokenizer.eos_token_id

    @property
    def cls(self):
        candidate = self.tokenizer.cls_token_id
        return self._check_token_candidate(candidate)

    @property
    def sep(self):
        candidate = self.tokenizer.sep_token_id
        return self._check_token_candidate(candidate)

    @property
    def pad(self):
        candidate = self.tokenizer.pad_token_id

        # just use eos_token_id if pad_token_id is not available, it is reasonable
        # maybe add a new token, and resize embedding layer is better
        if candidate is None:
            candidate = self.tokenizer.eos_token_id
        return self._check_token_candidate(candidate)

    @property
    def mask(self):
        candidate = self.tokenizer.mask_token_id
        return self._check_token_candidate(candidate)

    @property
    def bos(self):
        raise NotImplementedError("Missing <bos>")

    @property
    def eos(self):
        candidate = self.tokenizer.eos_token_id
        return self._check_token_candidate(candidate)

    @property
    def additional_special_tokens_ids(self):
        """ All the additional special tokens you may want to use (list of strings)."""
        return self.tokenizer.additional_special_tokens_ids

    @staticmethod
    def _check_token_candidate(candidate):
        if candidate is None:
            raise AttributeError("Token doesn't exist")
        return candidate