# coding=utf-8
# Copyright 2022, UCLA NLP, 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
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# 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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import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)

SPIECE_UNDERLINE = "▁"

VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/sentencepiece.bpe.model",
        "uclanlp/plbart-c-cpp-defect-detection": (
            "https://huggingface.co/uclanlp/plbart-c-cpp-defect-detection/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-cs-java": "https://huggingface.co/uclanlp/plbart-cs-java/resolve/main/sentencepiece.bpe.model",
        "uclanlp/plbart-en_XX-java": (
            "https://huggingface.co/uclanlp/plbart-en_XX-java/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-go-en_XX": (
            "https://huggingface.co/uclanlp/plbart-go-en_XX/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-java-clone-detection": (
            "https://huggingface.co/uclanlp/plbart-java-clone-detection/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-java-cs": "https://huggingface.co/uclanlp/plbart-java-cs/resolve/main/sentencepiece.bpe.model",
        "uclanlp/plbart-java-en_XX": (
            "https://huggingface.co/uclanlp/plbart-java-en_XX/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-javascript-en_XX": (
            "https://huggingface.co/uclanlp/plbart-javascript-en_XX/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-php-en_XX": (
            "https://huggingface.co/uclanlp/plbart-php-en_XX/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-python-en_XX": (
            "https://huggingface.co/uclanlp/plbart-python-en_XX/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-refine-java-medium": (
            "https://huggingface.co/uclanlp/plbart-refine-java-medium/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-refine-java-small": (
            "https://huggingface.co/uclanlp/plbart-refine-java-small/resolve/main/sentencepiece.bpe.model"
        ),
        "uclanlp/plbart-ruby-en_XX": (
            "https://huggingface.co/uclanlp/plbart-ruby-en_XX/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "uclanlp/plbart-base": 1024,
    "uclanlp/plbart-c-cpp-defect-detection": 1024,
    "uclanlp/plbart-cs-java": 1024,
    "uclanlp/plbart-en_XX-java": 1024,
    "uclanlp/plbart-go-en_XX": 1024,
    "uclanlp/plbart-java-clone-detection": 1024,
    "uclanlp/plbart-java-cs": 1024,
    "uclanlp/plbart-java-en_XX": 1024,
    "uclanlp/plbart-javascript-en_XX": 1024,
    "uclanlp/plbart-php-en_XX": 1024,
    "uclanlp/plbart-python-en_XX": 1024,
    "uclanlp/plbart-refine-java-medium": 1024,
    "uclanlp/plbart-refine-java-small": 1024,
    "uclanlp/plbart-ruby-en_XX": 1024,
}

FAIRSEQ_LANGUAGE_CODES = {
    "base": ["java", "python", "en_XX"],
    "multi": ["java", "python", "en_XX", "javascript", "php", "ruby", "go"],
}


class PLBartTokenizer(PreTrainedTokenizer):
    """
    Construct an PLBART tokenizer.

    Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
    [SentencePiece](https://github.com/google/sentencepiece).

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

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        src_lang (`str`, *optional*):
            A string representing the source language.
        tgt_lang (`str`, *optional*):
            A string representing the target language.
        bos_token (`str`, *optional*, defaults to `"<s>"`):
            The start of sequence token.
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.
        sep_token (`str`, *optional*, defaults to `"</s>"`):
            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
            sequence classification or for a text and a question for question answering. It is also used as the last
            token of a sequence built with special tokens.
        cls_token (`str`, *optional*, defaults to `"<s>"`):
            The cls token, which is a special token used as the first token for all tasks.
        unk_token (`str`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        pad_token (`str`, *optional*, defaults to `"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        mask_token(`str`, *optional*, defaults to `"<mask>"`):
            The token used for masking values. This is the token used when training this model with masking tasks. This
            is only used in the `"base"` tokenizer type. For `"multi"` tokenizer, masking is never done for the
            downstream tasks.
        language_codes (`str`, *optional*, defaults to `"base"`):
            What language codes to use. Should be one of `"base"` or `"multi"`.
        sp_model_kwargs (`dict`, *optional*):
            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
            to set:
            - `enable_sampling`: Enable subword regularization.
            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
              - `nbest_size = {0,1}`: No sampling is performed.
              - `nbest_size > 1`: samples from the nbest_size results.
              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                using forward-filtering-and-backward-sampling algorithm.
            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
              BPE-dropout.

    Examples:

    ```python
    >>> from transformers import PLBartTokenizer

    >>> tokenizer = PLBartTokenizer.from_pretrained("uclanlp/plbart-python-en_XX", src_lang="python", tgt_lang="en_XX")
    >>> example_python_phrase = "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])"
    >>> expected_translation_english = "Returns the maximum value of a b c."
    >>> inputs = tokenizer(example_python_phrase, text_target=expected_translation_english, return_tensors="pt")
    ```"""

    vocab_files_names = VOCAB_FILES_NAMES
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    model_input_names = ["input_ids", "attention_mask"]

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

    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        language_codes="base",
        tokenizer_file=None,
        src_lang=None,
        tgt_lang=None,
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        additional_special_tokens=None,
        **kwargs
    ):
        # Mask token behave like a normal word, i.e. include the space before it
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            language_codes=language_codes,
            tokenizer_file=tokenizer_file,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            additional_special_tokens=additional_special_tokens,
            sp_model_kwargs=self.sp_model_kwargs,
            **kwargs,
        )

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(str(vocab_file))
        self.vocab_file = vocab_file
        self.language_codes = language_codes

        fairseq_language_codes = FAIRSEQ_LANGUAGE_CODES[self.language_codes]

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        self.fairseq_offset = 1

        self.sp_model_size = len(self.sp_model)
        self.lang_code_to_id = {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(fairseq_language_codes)
        }
        self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}

        if self.language_codes == "base":
            self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        self._additional_special_tokens = list(self.lang_code_to_id.keys())

        if additional_special_tokens is not None:
            # Only add those special tokens if they are not already there.
            self._additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in self._additional_special_tokens]
            )

        if self.language_codes == "base":
            self._src_lang = src_lang
            self.cur_lang_code_id = (
                self.lang_code_to_id[self._src_lang] if self._src_lang is not None else self._src_lang
            )
        else:
            self._src_lang = src_lang if src_lang is not None else "en_XX"
            self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]

        self.tgt_lang = tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
        return state

    def __setstate__(self, d):
        self.__dict__ = d

        # for backward compatibility
        if not hasattr(self, "sp_model_kwargs"):
            self.sp_model_kwargs = {}

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

    @property
    def vocab_size(self):
        if self.language_codes == "base":
            return (
                len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1
            )  # Plus 1 for the mask token
        else:
            return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

    @property
    def src_lang(self) -> str:
        return self._src_lang

    @src_lang.setter
    def src_lang(self, new_src_lang: str) -> None:
        self._src_lang = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)

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

        Returns:
            `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:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

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

        Returns:
            `List[int]`: List of [input IDs](../glossary#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

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. PLBart does not
        make use of token type ids, therefore a list of zeros is returned.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of zeros.
        """

        sep = [self.sep_token_id]
        cls = [self.cls_token_id]

        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

    def _build_translation_inputs(
        self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
    ):
        """Used by translation pipeline, to prepare inputs for the generate function"""
        if src_lang is None or tgt_lang is None:
            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
        self.src_lang = src_lang
        inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
        inputs["forced_bos_token_id"] = tgt_lang_id
        return inputs

    def get_vocab(self):
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text: str) -> List[str]:
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        spm_id = self.sp_model.PieceToId(token)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset)

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (strings for sub-words) in a single string."""
        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
        return out_string

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    def prepare_seq2seq_batch(
        self,
        src_texts: List[str],
        src_lang: str = "en_XX",
        tgt_texts: Optional[List[str]] = None,
        tgt_lang: str = "python",
        **kwargs,
    ) -> BatchEncoding:
        self.src_lang = src_lang
        self.tgt_lang = tgt_lang
        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)

    def _switch_to_input_mode(self):
        return self.set_src_lang_special_tokens(self.src_lang)

    def _switch_to_target_mode(self):
        return self.set_tgt_lang_special_tokens(self.tgt_lang)

    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.lang_code_to_id[src_lang] if src_lang is not None else None
        self.prefix_tokens = []
        if self.cur_lang_code is not None:
            self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
        else:
            self.suffix_tokens = [self.eos_token_id]

    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.lang_code_to_id[lang] if lang is not None else None
        self.prefix_tokens = []
        if self.cur_lang_code is not None:
            self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
        else:
            self.suffix_tokens = [self.eos_token_id]
