# coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# 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.
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# limitations under the License.

from typing import List, Optional

from tokenizers import processors

from ...file_utils import add_start_docstrings, is_sentencepiece_available
from ...tokenization_utils import BatchEncoding
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from ...utils import logging
from ..xlm_roberta.tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    MBartTokenizer = None


logger = logging.get_logger(__name__)

_all_mbart_models = ["facebook/mbart-large-en-ro", "facebook/mbart-large-cc25"]
SPM_URL = "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentence.bpe.model"
tokenizer_URL = "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json"

FAIRSEQ_LANGUAGE_CODES = [
    "ar_AR",
    "cs_CZ",
    "de_DE",
    "en_XX",
    "es_XX",
    "et_EE",
    "fi_FI",
    "fr_XX",
    "gu_IN",
    "hi_IN",
    "it_IT",
    "ja_XX",
    "kk_KZ",
    "ko_KR",
    "lt_LT",
    "lv_LV",
    "my_MM",
    "ne_NP",
    "nl_XX",
    "ro_RO",
    "ru_RU",
    "si_LK",
    "tr_TR",
    "vi_VN",
    "zh_CN",
]


class MBartTokenizerFast(XLMRobertaTokenizerFast):
    """
    Construct a "fast" MBART tokenizer (backed by HuggingFace's `tokenizers` library). Based on `BPE
    <https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models>`__.

    :class:`~transformers.MBartTokenizerFast` is a subclass of :class:`~transformers.XLMRobertaTokenizerFast` and adds
    a new :meth:`~transformers.MBartTokenizerFast.prepare_seq2seq_batch`.

    Refer to superclass :class:`~transformers.XLMRobertaTokenizerFast` for usage examples and documentation concerning
    the initialization parameters and other methods.

    .. warning::
        ``prepare_seq2seq_batch`` should be used to encode inputs. Other tokenizer methods like ``encode`` do not work
        properly.

    The tokenization method is ``<tokens> <eos> <language code>`` for source language documents, and ``<language code>
    <tokens> <eos>``` for target language documents.

    Examples::

        >>> from transformers import MBartTokenizerFast
        >>> tokenizer = MBartTokenizerFast.from_pretrained('facebook/mbart-large-en-ro')
        >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
        >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
        >>> batch: dict = tokenizer.prepare_seq2seq_batch(
        ...     example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt"
        ... )
    """

    vocab_files_names = {"vocab_file": "sentencepiece.bpe.model"}
    max_model_input_sizes = {m: 1024 for m in _all_mbart_models}
    pretrained_vocab_files_map = {"vocab_file": {m: SPM_URL for m in _all_mbart_models}}
    slow_tokenizer_class = MBartTokenizer

    prefix_tokens: List[int] = []
    suffix_tokens: List[int] = []

    def __init__(self, *args, tokenizer_file=None, **kwargs):
        super().__init__(*args, tokenizer_file=tokenizer_file, **kwargs)

        self.cur_lang_code = self.convert_tokens_to_ids("en_XX")
        self.set_src_lang_special_tokens(kwargs.get("src_lang", "en_XX"))

        self.add_special_tokens({"additional_special_tokens": FAIRSEQ_LANGUAGE_CODES})

    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer ``prepare_for_model`` method.

        Args:
            token_ids_0 (:obj:`List[int]`):
                List of ids.
            token_ids_1 (:obj:`List[int]`, `optional`):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
            if token_ids_1 is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formatted with special tokens for the model."
                )
            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
        prefix_ones = [1] * len(self.prefix_tokens)
        suffix_ones = [1] * len(self.suffix_tokens)
        if token_ids_1 is None:
            return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
        return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones

    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens. The special tokens depend on calling set_lang.

        An MBART sequence has the following format, where ``X`` represents the sequence:

        - ``input_ids`` (for encoder) ``X [eos, src_lang_code]``
        - ``decoder_input_ids``: (for decoder) ``X [eos, tgt_lang_code]``

        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
        separator.

        Args:
            token_ids_0 (:obj:`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (:obj:`List[int]`, `optional`):
                Optional second list of IDs for sequence pairs.

        Returns:
            :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
        """
        if token_ids_1 is None:
            return self.prefix_tokens + token_ids_0 + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens

    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
    def prepare_seq2seq_batch(
        self,
        src_texts: List[str],
        src_lang: str = "en_XX",
        tgt_texts: Optional[List[str]] = None,
        tgt_lang: str = "ro_RO",
        max_length: Optional[int] = None,
        max_target_length: Optional[int] = None,
        truncation: bool = True,
        padding: str = "longest",
        return_tensors: str = None,
        **kwargs,
    ) -> BatchEncoding:
        if max_length is None:
            max_length = self.model_max_length
        self.set_src_lang_special_tokens(src_lang)
        model_inputs: BatchEncoding = self(
            src_texts,
            add_special_tokens=True,
            return_tensors=return_tensors,
            max_length=max_length,
            padding=padding,
            truncation=truncation,
            **kwargs,
        )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            max_target_length = max_length
        self.set_tgt_lang_special_tokens(tgt_lang)

        labels = self(
            tgt_texts,
            add_special_tokens=True,
            return_tensors=return_tensors,
            padding=padding,
            max_length=max_target_length,
            truncation=True,
            **kwargs,
        )["input_ids"]
        model_inputs["labels"] = labels
        self.set_src_lang_special_tokens(src_lang)  # sets to src_lang
        return model_inputs

    def set_src_lang_special_tokens(self, src_lang) -> None:
        """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
        self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
            pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
            special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
        )

    def set_tgt_lang_special_tokens(self, lang: str) -> None:
        """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
        self.cur_lang_code = self.convert_tokens_to_ids(lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
            pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
            special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
        )
