# 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.
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""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, _insert_one_token_to_ordered_list
from ...tokenization_utils_base import AddedToken, 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 (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
                Activates and controls padding. Accepts the following values:

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

                If left unset or set to `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 (`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 (`str` or [`~file_utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

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


class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):

    """
    Constructs a Wav2Vec2CTC tokenizer.

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

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

        **kwargs
            Additional keyword arguments passed along to [`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)

        self._create_trie(self.unique_no_split_tokens)

    @property
    def word_delimiter_token(self) -> str:
        """
        `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]:
        """
        `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `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,)

    def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
        """
        Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
        it with indices starting from length of the current vocabulary.

        Args:
            new_tokens (`List[str]`or `List[tokenizers.AddedToken]`):
                Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by
                checking if the tokenizer assign the index of the `unk_token` to them).
            special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the tokens should be added as special tokens.

        Returns:
            `int`: The number of tokens actually added to the vocabulary.

        Examples:

        ```python
        # Let's see how to increase the vocabulary of Bert model and tokenizer
        tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('facebook/wav2vec2-base-960h')
        model = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-base-960h')

        num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
        print('We have added', num_added_toks, 'tokens')
        # Note: resize_token_embeddings expects to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
        model.resize_token_embeddings(len(tokenizer))
        ```"""
        new_tokens = [str(tok) for tok in new_tokens]

        tokens_to_add = []
        for token in new_tokens:
            assert isinstance(token, str)
            if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case:
                token = token.lower()
            if (
                token != self.unk_token
                and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)
                and token not in tokens_to_add
            ):
                tokens_to_add.append(token)
                if self.verbose:
                    logger.info(f"Adding {token} to the vocabulary")

        added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
        added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
        self.added_tokens_encoder.update(added_tok_encoder)
        self.added_tokens_decoder.update(added_tok_decoder)

        # Make sure we don't split on any special tokens (even they were already in the vocab before)
        for token in tokens_to_add:
            if len(token) > 1:
                self._additional_special_tokens.append(AddedToken(token))
                _insert_one_token_to_ordered_list(self.unique_no_split_tokens, token)

        self._create_trie(self.unique_no_split_tokens)

        return len(tokens_to_add)


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

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

    Args:
        vocab_file (`str`):
            File containing the vocabulary.
        bos_token (`str`, *optional*, defaults to `"<s>"`):
            The beginning of sentence token.
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sentence token.
        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.
        word_delimiter_token (`str`, *optional*, defaults to `"|"`):
            The token used for defining the end of a word.
        do_lower_case (`bool`, *optional*, defaults to `False`):
            Whether or not to lowercase the output when decoding.
        do_normalize (`bool`, *optional*, defaults to `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 (`bool`, *optional*, defaults to `False`):
            Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`.

            <Tip>

            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
            `attention_mask`. For such models, `input_values` should simply be padded with 0 and no
            `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), `attention_mask` should be
            passed for batched inference.

            </Tip>

        **kwargs
            Additional keyword arguments passed along to [`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:
        """
        `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]:
        """
        `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `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 (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `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,)
