# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# coding=utf-8
# Copyright 2023 MetaAI 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 classes for Code LLaMA."""
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...convert_slow_tokenizer import import_protobuf
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging, requires_backends


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "hf-internal-testing/llama-code-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-code-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "hf-internal-testing/llama-code-tokenizer": 2048,
}
SPIECE_UNDERLINE = "▁"

B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
 that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
correct. If you don't know the answer to a question, please don't share false information."""
# fmt: on


class CodeLlamaTokenizer(PreTrainedTokenizer):
    """
    Construct a CodeLlama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as
    there is no padding token in the original model.

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
            The token used is the `sep_token`.

            </Tip>

        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.
        prefix_token (`str`, *optional*, defaults to `"▁<PRE>"`):
            Prefix token used for infilling.
        suffix_token (`str`, *optional*, defaults to `"▁<SUF>"`):
            Suffix token used for infilling.
        middle_token (`str`, *optional*, defaults to `"▁<MID>"`):
            Middle token used for infilling.
        eot_token (`str`, *optional*, defaults to `"▁<EOT>"`):
            End of text token used for infilling.
        fill_token (`str`, *optional*, defaults to `"<FILL_ME>"`):
            The token used to split the input between the prefix and suffix.
        suffix_first (`bool`, *optional*, default to `False`):
            Whether the input prompt and suffix should be formatted with the suffix first.
        additional_special_tokens (`List[str]`, *optional*):
            Additional special tokens used by the tokenizer.
        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.

    """

    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,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        pad_token=None,
        prefix_token="▁<PRE>",
        middle_token="▁<MID>",
        suffix_token="▁<SUF>",
        eot_token="▁<EOT>",
        fill_token="<FILL_ME>",
        suffix_first=False,
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        add_bos_token=True,
        add_eos_token=False,
        clean_up_tokenization_spaces=False,
        **kwargs,
    ):
        requires_backends(self, "protobuf")
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token

        # mark tokens special to skip them
        additional_special_tokens = kwargs.pop("additional_special_tokens", [])
        additional_special_tokens += [prefix_token, middle_token, suffix_token, eot_token]
        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            prefix_token=prefix_token,
            middle_token=middle_token,
            suffix_token=suffix_token,
            eot_token=eot_token,
            fill_token=fill_token,
            sp_model_kwargs=self.sp_model_kwargs,
            suffix_first=suffix_first,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )
        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self._prefix_token = prefix_token
        self._middle_token = middle_token
        self._suffix_token = suffix_token
        self._eot_token = eot_token
        self.fill_token = fill_token
        self.suffix_first = suffix_first
        self.sp_model = self.get_spm_processor()

    @property
    def unk_token_length(self):
        return len(self.sp_model.encode(str(self.unk_token)))

    def get_spm_processor(self):
        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        with open(self.vocab_file, "rb") as f:
            sp_model = f.read()
            model_pb2 = import_protobuf()
            model = model_pb2.ModelProto.FromString(sp_model)
            normalizer_spec = model_pb2.NormalizerSpec()
            normalizer_spec.add_dummy_prefix = False
            model.normalizer_spec.MergeFrom(normalizer_spec)
            sp_model = model.SerializeToString()
            tokenizer.LoadFromSerializedProto(sp_model)
        return tokenizer

    @property
    def prefix_token(self):
        return self._prefix_token

    @property
    def prefix_id(self):
        if self._prefix_token is None:
            return None
        return self.convert_tokens_to_ids(self.prefix_token)

    @property
    def middle_token(self):
        return self._middle_token

    @property
    def middle_id(self):
        if self._middle_token is None:
            return None
        return self.convert_tokens_to_ids(self.middle_token)

    @property
    def suffix_token(self):
        return self._suffix_token

    @property
    def suffix_id(self):
        if self._suffix_token is None:
            return None
        return self.convert_tokens_to_ids(self.suffix_token)

    @property
    def eot_token(self):
        return self._eot_token

    @property
    def eot_id(self):
        if self._eot_token is None:
            return None
        return self.convert_tokens_to_ids(self.eot_token)

    @property
    def vocab_size(self):
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    def get_vocab(self):
        """Returns vocab as a dict"""
        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, prefix, suffix=None, suffix_first=False, **kwargs) -> List[int]:
        # add a prefix space to `prefix`
        if self.fill_token in prefix and suffix is None:
            prefix, suffix = prefix.split(self.fill_token)

        if len(prefix) > 0:
            prefix = SPIECE_UNDERLINE + prefix.replace(SPIECE_UNDERLINE, " ")

        if suffix is None or len(suffix) < 1:
            tokens = super().tokenize(prefix, **kwargs)
            if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
                tokens = tokens[1:]
            return tokens

        prefix_tokens = self._tokenize(prefix)  # prefix has an extra `SPIECE_UNDERLINE`

        if None in (self.prefix_id, self.middle_id, self.suffix_id):
            raise ValueError(
                "Then input includes a `prefix` and a `suffix` used for the infilling task,"
                " the `prefix_id, middle_id, suffix_id` must all be initialized. Current"
                f" values : {self.prefix_id, self.middle_id, self.suffix_id}"
            )
        suffix_tokens = self._tokenize(suffix)  # make sure CodeLlama sp model does not mess up

        suffix_first = suffix_first if suffix_first is not None else self.suffix_first
        if suffix_first:
            # format as " <PRE> <SUF>{suf} <MID> {pre}"
            return [self.prefix_token, self.suffix_token] + suffix_tokens + [self.middle_token] + prefix_tokens
        else:
            # format as " <PRE> {pre} <SUF>{suf} <MID>"
            return [self.prefix_token] + prefix_tokens + [self.suffix_token] + suffix_tokens + [self.middle_token]

    def _tokenize(self, text, **kwargs):
        """
        Returns a tokenized string.

        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
        """
        tokens = self.sp_model.encode(text, out_type=str)
        # 1. Encode string + prefix ex: "<unk> Hey"
        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.IdToPiece(index)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        # since we manually add the prefix space, we have to remove it when decoding
        if tokens[0].startswith(SPIECE_UNDERLINE):
            tokens[0] = tokens[0][1:]

        current_sub_tokens = []
        out_string = ""
        for _, token in enumerate(tokens):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(current_sub_tokens) + token
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary and special tokens file to a directory.

        Args:
            save_directory (`str`):
                The directory in which to save the vocabulary.

        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

    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
            )

        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []

        if token_ids_1 is None:
            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(token_ids_0))
            + eos_token_id
            + bos_token_id
            + ([0] * len(token_ids_1))
            + eos_token_id
        )

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
        sequence pair mask has the following format:

        ```
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |
        ```

        if token_ids_1 is None, only returns the first portion of the mask (0s).

        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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)

        if token_ids_1 is not None:
            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)

        return output

    def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
        r"""Builds the input ids for a conversation.
        This is the format used in the provided examples. System prompts should be manually added at the beginning of
        the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used.
        ```
        <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>
        <bos>[INST] Prompt [/INST] Answer <eos>
        <bos>[INST] Prompt [/INST]
        ```

        If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following:
        ```python
        >>> from transformers import Conversation

        >>> Conversation(
        ...     "<<SYS>>\n Only answer with emojis, and charades\n<</SYS>>\n\nHow can I build a house in 10 septs?"
        ... )  # doctest: +IGNORE_RESULT
        ```
        Args:
            conversation (`Conversation`):
                Conversation to build input ids for.
        Returns:
            `List[int]`:
                Input ids for the conversation.
        """
        if len(conversation.past_user_inputs) > 0:
            if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:
                conversation.past_user_inputs[0] = (
                    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                )
        elif conversation.new_user_input:
            if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input:
                conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input
        else:
            raise ValueError("Last message must be from user")

        dialogue = list(conversation.iter_texts())
        if not all([is_user for is_user, msg in dialogue[::2]]) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]
        ):
            raise ValueError(
                "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"
            )

        dialog_tokens: List[int] = []
        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ", add_special_tokens=False
                )
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2], dialogue[1::2])
            ],
            [],
        )
        dialog_tokens += [self.bos_token_id] + self.encode(
            f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}", add_special_tokens=False
        )
        return dialog_tokens

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

# coding=utf-8
# Copyright 2023 MetaAI 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 classes for Code LLaMA."""
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...convert_slow_tokenizer import import_protobuf
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging, requires_backends


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "hf-internal-testing/llama-code-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-code-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "hf-internal-testing/llama-code-tokenizer": 2048,
}
SPIECE_UNDERLINE = "▁"

B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
 that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
correct. If you don't know the answer to a question, please don't share false information."""
# fmt: on


class CodeLlamaTokenizer(PreTrainedTokenizer):
    """
    Construct a CodeLlama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as
    there is no padding token in the original model.

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
            The token used is the `sep_token`.

            </Tip>

        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.
        prefix_token (`str`, *optional*, defaults to `"▁<PRE>"`):
            Prefix token used for infilling.
        suffix_token (`str`, *optional*, defaults to `"▁<SUF>"`):
            Suffix token used for infilling.
        middle_token (`str`, *optional*, defaults to `"▁<MID>"`):
            Middle token used for infilling.
        eot_token (`str`, *optional*, defaults to `"▁<EOT>"`):
            End of text token used for infilling.
        fill_token (`str`, *optional*, defaults to `"<FILL_ME>"`):
            The token used to split the input between the prefix and suffix.
        suffix_first (`bool`, *optional*, default to `False`):
            Whether the input prompt and suffix should be formatted with the suffix first.
        additional_special_tokens (`List[str]`, *optional*):
            Additional special tokens used by the tokenizer.
        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.

    """

    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,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        pad_token=None,
        prefix_token="▁<PRE>",
        middle_token="▁<MID>",
        suffix_token="▁<SUF>",
        eot_token="▁<EOT>",
        fill_token="<FILL_ME>",
        suffix_first=False,
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        add_bos_token=True,
        add_eos_token=False,
        clean_up_tokenization_spaces=False,
        **kwargs,
    ):
        requires_backends(self, "protobuf")
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token

        # mark tokens special to skip them
        additional_special_tokens = kwargs.pop("additional_special_tokens", [])
        additional_special_tokens += [prefix_token, middle_token, suffix_token, eot_token]
        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            prefix_token=prefix_token,
            middle_token=middle_token,
            suffix_token=suffix_token,
            eot_token=eot_token,
            fill_token=fill_token,
            sp_model_kwargs=self.sp_model_kwargs,
            suffix_first=suffix_first,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )
        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self._prefix_token = prefix_token
        self._middle_token = middle_token
        self._suffix_token = suffix_token
        self._eot_token = eot_token
        self.fill_token = fill_token
        self.suffix_first = suffix_first
        self.sp_model = self.get_spm_processor()

    @property
    def unk_token_length(self):
        return len(self.sp_model.encode(str(self.unk_token)))

    def get_spm_processor(self):
        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        with open(self.vocab_file, "rb") as f:
            sp_model = f.read()
            model_pb2 = import_protobuf()
            model = model_pb2.ModelProto.FromString(sp_model)
            normalizer_spec = model_pb2.NormalizerSpec()
            normalizer_spec.add_dummy_prefix = False
            model.normalizer_spec.MergeFrom(normalizer_spec)
            sp_model = model.SerializeToString()
            tokenizer.LoadFromSerializedProto(sp_model)
        return tokenizer

    @property
    def prefix_token(self):
        return self._prefix_token

    @property
    def prefix_id(self):
        if self._prefix_token is None:
            return None
        return self.convert_tokens_to_ids(self.prefix_token)

    @property
    def middle_token(self):
        return self._middle_token

    @property
    def middle_id(self):
        if self._middle_token is None:
            return None
        return self.convert_tokens_to_ids(self.middle_token)

    @property
    def suffix_token(self):
        return self._suffix_token

    @property
    def suffix_id(self):
        if self._suffix_token is None:
            return None
        return self.convert_tokens_to_ids(self.suffix_token)

    @property
    def eot_token(self):
        return self._eot_token

    @property
    def eot_id(self):
        if self._eot_token is None:
            return None
        return self.convert_tokens_to_ids(self.eot_token)

    @property
    def vocab_size(self):
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    def get_vocab(self):
        """Returns vocab as a dict"""
        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, prefix, suffix=None, suffix_first=False, **kwargs) -> List[int]:
        # add a prefix space to `prefix`
        if self.fill_token in prefix and suffix is None:
            prefix, suffix = prefix.split(self.fill_token)

        if len(prefix) > 0:
            prefix = SPIECE_UNDERLINE + prefix.replace(SPIECE_UNDERLINE, " ")

        if suffix is None or len(suffix) < 1:
            tokens = super().tokenize(prefix, **kwargs)
            if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
                tokens = tokens[1:]
            return tokens

        prefix_tokens = self._tokenize(prefix)  # prefix has an extra `SPIECE_UNDERLINE`

        if None in (self.prefix_id, self.middle_id, self.suffix_id):
            raise ValueError(
                "Then input includes a `prefix` and a `suffix` used for the infilling task,"
                " the `prefix_id, middle_id, suffix_id` must all be initialized. Current"
                f" values : {self.prefix_id, self.middle_id, self.suffix_id}"
            )
        suffix_tokens = self._tokenize(suffix)  # make sure CodeLlama sp model does not mess up

        suffix_first = suffix_first if suffix_first is not None else self.suffix_first
        if suffix_first:
            # format as " <PRE> <SUF>{suf} <MID> {pre}"
            return [self.prefix_token, self.suffix_token] + suffix_tokens + [self.middle_token] + prefix_tokens
        else:
            # format as " <PRE> {pre} <SUF>{suf} <MID>"
            return [self.prefix_token] + prefix_tokens + [self.suffix_token] + suffix_tokens + [self.middle_token]

    def _tokenize(self, text, **kwargs):
        """
        Returns a tokenized string.

        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
        """
        tokens = self.sp_model.encode(text, out_type=str)
        # 1. Encode string + prefix ex: "<unk> Hey"
        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.IdToPiece(index)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        # since we manually add the prefix space, we have to remove it when decoding
        if tokens[0].startswith(SPIECE_UNDERLINE):
            tokens[0] = tokens[0][1:]

        current_sub_tokens = []
        out_string = ""
        for _, token in enumerate(tokens):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(current_sub_tokens) + token
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary and special tokens file to a directory.

        Args:
            save_directory (`str`):
                The directory in which to save the vocabulary.

        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

    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
            )

        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []

        if token_ids_1 is None:
            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(token_ids_0))
            + eos_token_id
            + bos_token_id
            + ([0] * len(token_ids_1))
            + eos_token_id
        )

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
        sequence pair mask has the following format:

        ```
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |
        ```

        if token_ids_1 is None, only returns the first portion of the mask (0s).

        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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)

        if token_ids_1 is not None:
            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)

        return output

    def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
        r"""Builds the input ids for a conversation.
        This is the format used in the provided examples. System prompts should be manually added at the beginning of
        the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used.
        ```
        <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>
        <bos>[INST] Prompt [/INST] Answer <eos>
        <bos>[INST] Prompt [/INST]
        ```

        If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following:
        ```python
        >>> from transformers import Conversation

        >>> Conversation(
        ...     "<<SYS>>\n Only answer with emojis, and charades\n<</SYS>>\n\nHow can I build a house in 10 septs?"
        ... )  # doctest: +IGNORE_RESULT
        ```
        Args:
            conversation (`Conversation`):
                Conversation to build input ids for.
        Returns:
            `List[int]`:
                Input ids for the conversation.
        """
        if len(conversation.past_user_inputs) > 0:
            if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:
                conversation.past_user_inputs[0] = (
                    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                )
        elif conversation.new_user_input:
            if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input:
                conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input
        else:
            raise ValueError("Last message must be from user")

        dialogue = list(conversation.iter_texts())
        if not all([is_user for is_user, msg in dialogue[::2]]) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]
        ):
            raise ValueError(
                "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"
            )

        dialog_tokens: List[int] = []
        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ", add_special_tokens=False
                )
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2], dialogue[1::2])
            ],
            [],
        )
        dialog_tokens += [self.bos_token_id] + self.encode(
            f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}", add_special_tokens=False
        )
        return dialog_tokens

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