# coding=utf-8
# Copyright 2021 The Open AI Team Authors and The HuggingFace Inc. team.
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# 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 classes for OpenAI GPT."""


import json
from typing import Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
    },
    "merges_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "openai/clip-vit-base-patch32": 77,
}


class CLIPTokenizerFast(PreTrainedTokenizerFast):
    """
    Construct a "fast" CLIP tokenizer (backed by HuggingFace's `tokenizers` library). Based on byte-level
    Byte-Pair-Encoding.

    This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
    be encoded differently whether it is at the beginning of the sentence (without space) or not:

    ::

        >>> from transformers import CLIPTokenizerFast
        >>> tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32")
        >>> tokenizer("Hello world")['input_ids']
        [15496, 995]
        >>> tokenizer(" Hello world")['input_ids']
        [18435, 995]

    You can get around that behavior by passing ``add_prefix_space=True`` when instantiating this tokenizer or when you
    call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

    .. note::

        When used with ``is_split_into_words=True``, this tokenizer needs to be instantiated with
        ``add_prefix_space=True``.

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

    Args:
        vocab_file (:obj:`str`):
            Path to the vocabulary file.
        merges_file (:obj:`str`):
            Path to the merges file.
        errors (:obj:`str`, `optional`, defaults to :obj:`"replace"`):
            Paradigm to follow when decoding bytes to UTF-8. See `bytes.decode
            <https://docs.python.org/3/library/stdtypes.html#bytes.decode>`__ for more information.
        unk_token (:obj:`str`, `optional`, defaults to :obj:`<|endoftext|>`):
            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 (:obj:`str`, `optional`, defaults to :obj:`<|endoftext|>`):
            The beginning of sequence token.
        eos_token (:obj:`str`, `optional`, defaults to :obj:`<|endoftext|>`):
            The end of sequence token.
        add_prefix_space (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
            other word. (CLIP tokenizer detect beginning of words by the preceding space).
        trim_offsets (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Whether or not the post-processing step should trim offsets to avoid including whitespaces.
    """

    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"]
    slow_tokenizer_class = CLIPTokenizer

    def __init__(
        self,
        vocab_file=None,
        merges_file=None,
        tokenizer_file=None,
        unk_token="<|endoftext|>",
        bos_token="<|startoftext|>",
        eos_token="<|endoftext|>",
        pad_token="<|endoftext|>",  # hack to enable padding
        add_prefix_space=False,
        **kwargs
    ):
        super().__init__(
            vocab_file,
            merges_file,
            tokenizer_file=tokenizer_file,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            add_prefix_space=add_prefix_space,
            **kwargs,
        )

        pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
            pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
            pre_tok_state["add_prefix_space"] = add_prefix_space
            self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)

        self.add_prefix_space = add_prefix_space

    # Very ugly hack to enable padding
    @property
    def pad_token_id(self) -> Optional[int]:
        """
        :obj:`Optional[int]`: Id of the padding token in the vocabulary. Returns :obj:`None` if the token has not been
        set.
        """
        return 0

    def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
        is_split_into_words = kwargs.get("is_split_into_words", False)
        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*args, **kwargs)

    def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
        is_split_into_words = kwargs.get("is_split_into_words", False)

        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*args, **kwargs)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
        return tuple(files)
