# coding=utf-8
# Copyright {{cookiecutter.authors}} 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 {{cookiecutter.modelname}}."""

{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "{{cookiecutter.checkpoint_identifier}}": 512,
}


PRETRAINED_INIT_CONFIGURATION = {
    "{{cookiecutter.checkpoint_identifier}}": {"do_lower_case": False},
}


class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
    r"""
    Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).

    :class:`~transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs
    end-to-end tokenization: punctuation splitting and wordpiece.

    Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
    parameters.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
    slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer

{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart_fast import BartTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer


logger = logging.get_logger(__name__)

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.json",
    },
    "merges_file": {
        "{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "{{cookiecutter.checkpoint_identifier}}": 1024,
}


class {{cookiecutter.camelcase_modelname}}TokenizerFast(BartTokenizerFast):
    r"""
    Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).

    :class:`~transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast` is identical to :class:`~transformers.BartTokenizerFast` and runs
    end-to-end tokenization: punctuation splitting and wordpiece.

    Refer to superclass :class:`~transformers.BartTokenizerFast` for usage examples and documentation concerning
    parameters.
    """

    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer

{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {}

PRETRAINED_VOCAB_FILES_MAP = {}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "{{cookiecutter.checkpoint_identifier}}": 1024,
}

class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
    """
    Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).

    Args:
        vocab_file (:obj:`str`):
            Path to the vocabulary file.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer

    def __init__(
            self,
            vocab_file,
            merges_file,
            unk_token="<|endoftext|>",
            bos_token="<|endoftext|>",
            eos_token="<|endoftext|>",
            add_prefix_space=False,
            trim_offsets=True,
            **kwargs
    ):
        super().__init__(
            ByteLevelBPETokenizer(
                vocab_file=vocab_file,
                merges_file=merges_file,
                add_prefix_space=add_prefix_space,
                trim_offsets=trim_offsets,
            ),
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            **kwargs,
        )
        self.add_prefix_space = add_prefix_space

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
        if token_ids_1 is None:
            return output

        return output + [self.eos_token_id] + token_ids_1 + [self.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]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task.
        {{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.

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

        Returns:
            :obj:`List[int]`:  List of zeros.
        """
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]

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


{% endif %}
