# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from tokenizers import Tokenizer, decoders, pre_tokenizers
from tokenizers.models import BPE

from ...tokenization_utils_base import _get_prepend_scheme
from ...tokenization_utils_tokenizers import TokenizersBackend
from ...utils import logging


logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

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 LlamaTokenizer(TokenizersBackend):
    """
    Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.

    This uses notably ByteFallback and no normalization.

    ```python
    >>> from transformers import LlamaTokenizer

    >>> tokenizer = LlamaTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
    >>> tokenizer.encode("Hello this is a test")
    [1, 15043, 445, 338, 263, 1243]
    ```

    If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
    call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
    values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
    [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.


    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
    refer to this superclass for more information regarding those methods.

    Args:
        vocab (`str`, `dict` or `list`, *optional*):
            Path to the vocabulary file, a dictionary or a list of tokens.
        merges (`str` or `list`, *optional*):
            Path to the merges file or a list of merges.
        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
            extra spaces.
        unk_token (`str` or `tokenizers.AddedToken`, *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.
        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
            The end of sequence token.
        add_bos_token (`bool`, *optional*, defaults to `True`):
            Whether or not to add an `bos_token` at the start of sequences.
        add_eos_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add an `eos_token` at the end of sequences.
        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
            Whether or not the default system prompt for Llama should be used
        add_prefix_space (`bool`, *optional*):
            Whether or not the tokenizer should automatically add a prefix space
    """

    vocab_files_names = VOCAB_FILES_NAMES
    padding_side = "left"
    model_input_names = ["input_ids", "attention_mask"]
    model = BPE

    def __init__(
        self,
        vocab: str | dict | list | None = None,
        merges: str | list | None = None,
        clean_up_tokenization_spaces=False,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        use_default_system_prompt=False,
        legacy=False,
        add_prefix_space=None,
        **kwargs,
    ):
        self.add_prefix_space = add_prefix_space if add_prefix_space is not None else True
        self.legacy = legacy
        self._vocab = vocab
        if vocab is None:
            self._vocab = {
                str(unk_token): 0,
                str(bos_token): 1,
                str(eos_token): 2,
            }

        self._merges = merges or []
        self._tokenizer = Tokenizer(
            BPE(vocab=self._vocab, merges=self._merges, fuse_unk=True, byte_fallback=True, dropout=None)
        )
        self._tokenizer.normalizer = None
        self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(
            replacement="▁", prepend_scheme=_get_prepend_scheme(self.add_prefix_space, self), split=False
        )

        sequence = [
            decoders.Replace("▁", " "),
            decoders.ByteFallback(),
            decoders.Fuse(),
        ]

        if self.add_prefix_space:
            sequence += [decoders.Strip(content=" ", left=1)]

        self._tokenizer.decoder = decoders.Sequence(sequence)
        self.use_default_system_prompt = use_default_system_prompt
        super().__init__(
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            use_default_system_prompt=use_default_system_prompt,
            add_prefix_space=add_prefix_space,
            **kwargs,
        )


__all__ = ["LlamaTokenizer", "LlamaTokenizerFast"]

# Backward alias
LlamaTokenizerFast = LlamaTokenizer