# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Tokenization class for Wav2Vec2."""

import json
import os
import sys
import warnings
from itertools import groupby
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging


logger = logging.get_logger(__name__)


VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json",
    },
    "tokenizer_config_file": {
        "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer_config.json",
    },
}

# Wav2Vec2 has no max input length
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-base-960h": sys.maxsize}

WAV2VEC2_KWARGS_DOCSTRING = r"""
            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
                Activates and controls padding. Accepts the following values:

                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                  single sequence if provided).
                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                  maximum acceptable input length for the model if that argument is not provided.
                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                  different lengths).
            max_length (:obj:`int`, `optional`):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to :obj:`None`, this will use the predefined model maximum length if a maximum
                length is required by one of the truncation/padding parameters. If the model has no specific maximum
                input length (like XLNet) truncation/padding to a maximum length will be deactivated.
            pad_to_multiple_of (:obj:`int`, `optional`):
                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
                the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
            return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
            verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to print more information and warnings.
"""


class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):

    """
    Constructs a Wav2Vec2CTC tokenizer.

    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains some of the main methods.
    Users should refer to the superclass for more information regarding such methods.

    Args:
        vocab_file (:obj:`str`):
            File containing the vocabulary.
        bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
            The beginning of sentence token.
        eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
            The end of sentence token.
        unk_token (:obj:`str`, `optional`, defaults to :obj:`"<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 (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        word_delimiter_token (:obj:`str`, `optional`, defaults to :obj:`"|"`):
            The token used for defining the end of a word.
        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to accept lowercase input and lowercase the output when decoding.

        **kwargs
            Additional keyword arguments passed along to :class:`~transformers.PreTrainedTokenizer`
    """

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

    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        word_delimiter_token="|",
        do_lower_case=False,
        **kwargs
    ):
        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            do_lower_case=do_lower_case,
            word_delimiter_token=word_delimiter_token,
            **kwargs,
        )

        self._word_delimiter_token = word_delimiter_token

        self.do_lower_case = do_lower_case

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)
        self.decoder = {v: k for k, v in self.encoder.items()}

        # make sure that tokens made of several
        # characters are not split at tokenization
        for token in self.encoder.keys():
            if len(token) > 1:
                self.unique_no_split_tokens.append(token)

    @property
    def word_delimiter_token(self) -> str:
        """
        :obj:`str`: Word delimiter token. Log an error if used while not having been set.
        """
        if self._word_delimiter_token is None and self.verbose:
            logger.error("Using word_delimiter_token, but it is not set yet.")
            return None
        return str(self._word_delimiter_token)

    @property
    def word_delimiter_token_id(self) -> Optional[int]:
        """
        :obj:`Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns :obj:`None` if the token has
        not been set.
        """
        if self._word_delimiter_token is None:
            return None
        return self.convert_tokens_to_ids(self.word_delimiter_token)

    @word_delimiter_token.setter
    def word_delimiter_token(self, value):
        self._word_delimiter_token = value

    @word_delimiter_token_id.setter
    def word_delimiter_token_id(self, value):
        self._word_delimiter_token = self.convert_tokens_to_ids(value)

    @property
    def vocab_size(self) -> int:
        return len(self.decoder)

    def get_vocab(self) -> Dict:
        return dict(self.encoder, **self.added_tokens_encoder)

    def _tokenize(self, text, **kwargs):
        """
        Converts a string in a sequence of tokens (string), using the tokenizer.
        """
        if self.do_lower_case:
            text = text.upper()

        return list(text.replace(" ", self.word_delimiter_token))

    def _convert_token_to_id(self, token: str) -> int:
        """Converts a token (str) in an index (integer) using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) in a token (str) using the vocab."""
        result = self.decoder.get(index, self.unk_token)
        return result

    def convert_tokens_to_string(
        self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False
    ) -> str:
        """
        Converts a connectionist-temporal-classification (CTC) output tokens into a single string.
        """
        # group same tokens into non-repeating tokens in CTC style decoding
        if group_tokens:
            tokens = [token_group[0] for token_group in groupby(tokens)]

        # filter self.pad_token which is used as CTC-blank token
        filtered_tokens = list(filter(lambda token: token != self.pad_token, tokens))

        if spaces_between_special_tokens:
            join_token = " "
        else:
            join_token = ""

        # replace delimiter token
        string = join_token.join(
            [" " if token == self.word_delimiter_token else token for token in filtered_tokens]
        ).strip()

        if self.do_lower_case:
            string = string.lower()
        return string

    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
        if is_split_into_words:
            text = " " + text
        return (text, kwargs)

    def _decode(
        self,
        token_ids: List[int],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        group_tokens: bool = True,
        spaces_between_special_tokens: bool = False,
    ) -> str:
        """
        special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the
        same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on
        the whole token list and not individually on added tokens
        """
        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)

        result = []
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            result.append(token)

        text = self.convert_tokens_to_string(
            result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens
        )

        if clean_up_tokenization_spaces:
            clean_text = self.clean_up_tokenization(text)
            return clean_text
        else:
            return text

    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
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, ensure_ascii=False))

        return (vocab_file,)


class Wav2Vec2Tokenizer(PreTrainedTokenizer):
    """
    Constructs a Wav2Vec2 tokenizer.

    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains some of the main methods.
    Users should refer to the superclass for more information regarding such methods.

    Args:
        vocab_file (:obj:`str`):
            File containing the vocabulary.
        bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
            The beginning of sentence token.
        eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
            The end of sentence token.
        unk_token (:obj:`str`, `optional`, defaults to :obj:`"<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 (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        word_delimiter_token (:obj:`str`, `optional`, defaults to :obj:`"|"`):
            The token used for defining the end of a word.
        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to lowercase the output when decoding.
        do_normalize (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
            improve the performance for some models, *e.g.*, `wav2vec2-lv60
            <https://huggingface.co/models?search=lv60>`__.
        return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not :meth:`~transformers.Wav2Vec2Tokenizer.__call__` should return :obj:`attention_mask`.

            .. note::

                Wav2Vec2 models that have set ``config.feat_extract_norm == "group"``, such as `wav2vec2-base
                <https://huggingface.co/facebook/wav2vec2-base-960h>`__, have **not** been trained using
                :obj:`attention_mask`. For such models, :obj:`input_values` should simply be padded with 0 and no
                :obj:`attention_mask` should be passed.

                For Wav2Vec2 models that have set ``config.feat_extract_norm == "layer"``, such as `wav2vec2-lv60
                <https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self>`__, :obj:`attention_mask` should be
                passed for batched inference.

        **kwargs
            Additional keyword arguments passed along to :class:`~transformers.PreTrainedTokenizer`
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = {
        "vocab_file": {
            "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json"
        },
        "tokenizer_config_file": {
            "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json",
        },
    }
    model_input_names = ["input_values", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        word_delimiter_token="|",
        do_lower_case=False,
        do_normalize=False,
        return_attention_mask=False,
        **kwargs
    ):
        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            do_lower_case=do_lower_case,
            do_normalize=do_normalize,
            return_attention_mask=return_attention_mask,
            word_delimiter_token=word_delimiter_token,
            **kwargs,
        )

        warnings.warn(
            "The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.",
            FutureWarning,
        )

        self._word_delimiter_token = word_delimiter_token

        self.do_lower_case = do_lower_case
        self.return_attention_mask = return_attention_mask
        self.do_normalize = do_normalize

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)

        self.decoder = {v: k for k, v in self.encoder.items()}

    @property
    def word_delimiter_token(self) -> str:
        """
        :obj:`str`: Padding token. Log an error if used while not having been set.
        """
        if self._word_delimiter_token is None and self.verbose:
            logger.error("Using word_delimiter_token, but it is not set yet.")
            return None
        return str(self._word_delimiter_token)

    @property
    def word_delimiter_token_id(self) -> Optional[int]:
        """
        :obj:`Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns :obj:`None` if the token has
        not been set.
        """
        if self._word_delimiter_token is None:
            return None
        return self.convert_tokens_to_ids(self.word_delimiter_token)

    @word_delimiter_token.setter
    def word_delimiter_token(self, value):
        self._word_delimiter_token = value

    @word_delimiter_token_id.setter
    def word_delimiter_token_id(self, value):
        self._word_delimiter_token = self.convert_tokens_to_ids(value)

    @add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING)
    def __call__(
        self,
        raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
        padding: Union[bool, str, PaddingStrategy] = False,
        max_length: Optional[int] = None,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        verbose: bool = True,
        **kwargs
    ) -> BatchEncoding:
        """
        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
        sequences.

        Args:
            raw_speech (:obj:`np.ndarray`, :obj:`List[float]`, :obj:`List[np.ndarray]`, :obj:`List[List[float]]`):
                The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
                values, a list of numpy arrayr or a list of list of float values.
        """

        is_batched = bool(
            isinstance(raw_speech, (list, tuple))
            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
        )

        # make sure input is in list format
        if is_batched and not isinstance(raw_speech[0], np.ndarray):
            raw_speech = [np.asarray(speech) for speech in raw_speech]
        elif not is_batched and not isinstance(raw_speech, np.ndarray):
            raw_speech = np.asarray(raw_speech)

        # always return batch
        if not is_batched:
            raw_speech = [raw_speech]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
            raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech]

        # convert into correct format for padding
        encoded_inputs = BatchEncoding({"input_values": raw_speech})

        padded_inputs = self.pad(
            encoded_inputs,
            padding=padding,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=self.return_attention_mask,
            return_tensors=return_tensors,
            verbose=verbose,
        )

        return padded_inputs

    @property
    def vocab_size(self) -> int:
        return len(self.decoder)

    def get_vocab(self) -> Dict:
        return dict(self.encoder, **self.added_tokens_encoder)

    def _convert_token_to_id(self, token: str) -> int:
        """Converts a token (str) in an index (integer) using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) in a token (str) using the vocab."""
        result = self.decoder.get(index, self.unk_token)
        return result

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Converts a connectionist-temporal-classification (CTC) output tokens into a single string.
        """
        # group same tokens into non-repeating tokens in CTC style decoding
        grouped_tokens = [token_group[0] for token_group in groupby(tokens)]

        # filter self.pad_token which is used as CTC-blank token
        filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens))

        # replace delimiter token
        string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip()

        if self.do_lower_case:
            string = string.lower()
        return string

    def _decode(
        self,
        token_ids: List[int],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        **kwargs
    ) -> str:
        """
        special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the
        same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on
        the whole token list and not individually on added tokens
        """
        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)

        result = []
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            result.append(token)

        text = self.convert_tokens_to_string(result)

        if clean_up_tokenization_spaces:
            clean_text = self.clean_up_tokenization(text)
            return clean_text
        else:
            return text

    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
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, ensure_ascii=False))

        return (vocab_file,)
