# Copyright 2018 The Open AI Team Authors and 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.
"""Tokenization classes for OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import logging
import os
import sys
from io import open

import json
import regex as re

from .file_utils import cached_path

try:
    from functools import lru_cache
except ImportError:
    # Just a dummy decorator to get the checks to run on python2
    # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
    def lru_cache():
        return lambda func: func


logger = logging.getLogger(__name__)

PRETRAINED_VOCAB_ARCHIVE_MAP = {
    'gpt2': '.pytorch_pretrained_bert/gpt2-vocab.json',
    'roberta': '.pytorch_pretrained_bert/roberta-vocab.json'
}
PRETRAINED_MERGES_ARCHIVE_MAP = {
    'gpt2': '.pytorch_pretrained_bert/gpt2-merges.txt',
    'roberta': '.pytorch_pretrained_bert/roberta-merges.txt'
}
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
    'gpt2': 1024,
}
VOCAB_NAME = 'vocab.json'
MERGES_NAME = 'merges.txt'
SPECIAL_TOKENS_NAME = 'special_tokens.txt'


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    _chr = unichr if sys.version_info[0] == 2 else chr
    bs = list(range(ord('!'),
                    ord('~') + 1)) + list(range(
                        ord('¡'),
                        ord('¬') + 1)) + list(range(ord('®'),
                                                    ord('ÿ') + 1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [_chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.

    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


class GPT2Tokenizer(object):
    """
    GPT-2 BPE tokenizer. Peculiarities:
        - Byte-level BPE
    """

    @classmethod
    def from_pretrained(cls,
                        pretrained_model_name_or_path,
                        cache_dir=None,
                        *inputs,
                        **kwargs):
        """
        Instantiate a PreTrainedBertModel from a pre-trained model file.
        Download and cache the pre-trained model file if needed.
        """
        if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[
                pretrained_model_name_or_path]
            merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[
                pretrained_model_name_or_path]
            special_tokens_file = None
        else:
            vocab_file = os.path.join(pretrained_model_name_or_path,
                                      VOCAB_NAME)
            merges_file = os.path.join(pretrained_model_name_or_path,
                                       MERGES_NAME)
            special_tokens_file = os.path.join(pretrained_model_name_or_path,
                                               SPECIAL_TOKENS_NAME)
            if not os.path.exists(special_tokens_file):
                special_tokens_file = None
            else:
                logger.info('loading special tokens file {}'.format(
                    special_tokens_file))
        # redirect to the cache, if necessary
        # try:
        #     resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
        #     resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
        # except EnvironmentError:
        #     logger.error(
        #         "Model name '{}' was not found in model name list ({}). "
        #         "We assumed '{}' was a path or url but couldn't find files {} and {} "
        #         "at this path or url.".format(
        #             pretrained_model_name_or_path,
        #             ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
        #             pretrained_model_name_or_path,
        #             vocab_file, merges_file))
        #     return None
        # if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
        #     logger.info("loading vocabulary file {}".format(vocab_file))
        #     logger.info("loading merges file {}".format(merges_file))
        # else:
        #     logger.info("loading vocabulary file {} from cache at {}".format(
        #         vocab_file, resolved_vocab_file))
        #     logger.info("loading merges file {} from cache at {}".format(
        #         merges_file, resolved_merges_file))
        resolved_vocab_file = vocab_file
        resolved_merges_file = merges_file
        logger.info('loading vocabulary file {}'.format(vocab_file))
        logger.info('loading merges file {}'.format(merges_file))
        if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
            # than the number of positional embeddings
            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[
                pretrained_model_name_or_path]
            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
        # Instantiate tokenizer.
        if special_tokens_file and 'special_tokens' not in kwargs:
            special_tokens = open(
                special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
        else:
            special_tokens = kwargs.pop('special_tokens', [])
        tokenizer = cls(
            resolved_vocab_file,
            resolved_merges_file,
            special_tokens=special_tokens,
            *inputs,
            **kwargs)
        return tokenizer

    def __init__(self,
                 vocab_file,
                 merges_file,
                 errors='replace',
                 special_tokens=None,
                 max_len=None):
        self.max_len = max_len if max_len is not None else int(1e12)
        self.encoder = json.load(open(vocab_file), encoding='utf-8')
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.errors = errors  # how to handle errors in decoding
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
        bpe_merges = [tuple(merge.split()) for merge in bpe_data]
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        self.cache = {}

        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        self.pat = re.compile(
            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
        )

        self.special_tokens = {}
        self.special_tokens_decoder = {}
        self.set_special_tokens(special_tokens)

    def __len__(self):
        return len(self.encoder) + len(self.special_tokens)

    def set_special_tokens(self, special_tokens):
        """ Add a list of additional tokens to the encoder.
            The additional tokens are indexed starting from the last index of the
            current vocabulary in the order of the `special_tokens` list.
        """
        if not special_tokens:
            self.special_tokens = {}
            self.special_tokens_decoder = {}
            return
        self.special_tokens = dict((tok, len(self.encoder) + i)
                                   for i, tok in enumerate(special_tokens))
        self.special_tokens_decoder = {
            v: k
            for k, v in self.special_tokens.items()
        }
        logger.info('Special tokens {}'.format(self.special_tokens))

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token)
        pairs = get_pairs(word)

        if not pairs:
            return token

        while True:
            bigram = min(
                pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except:  # noqa
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word) - 1 and word[
                        i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = ' '.join(word)
        self.cache[token] = word
        return word

    def tokenize(self, text):
        """ Tokenize a string. """
        bpe_tokens = []
        for token in re.findall(self.pat, text):
            if sys.version_info[0] == 2:
                token = ''.join(self.byte_encoder[ord(b)] for b in token)
            else:
                token = ''.join(self.byte_encoder[b]
                                for b in token.encode('utf-8'))
            bpe_tokens.extend(
                bpe_token for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def convert_tokens_to_ids(self, tokens):
        """ Converts a sequence of tokens into ids using the vocab. """
        ids = []
        if isinstance(tokens, str) or (sys.version_info[0] == 2
                                       and isinstance(tokens, unicode)):
            if tokens in self.special_tokens:
                return self.special_tokens[tokens]
            else:
                return self.encoder.get(tokens, 0)
        for token in tokens:
            if token in self.special_tokens:
                ids.append(self.special_tokens[token])
            else:
                ids.append(self.encoder.get(token, 0))
        if len(ids) > self.max_len:
            logger.warning(
                'Token indices sequence length is longer than the specified maximum '
                ' sequence length for this OpenAI GPT model ({} > {}). Running this'
                ' sequence through the model will result in indexing errors'.
                format(len(ids), self.max_len))
        return ids

    def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
        """Converts a sequence of ids in BPE tokens using the vocab."""
        tokens = []
        for i in ids:
            if i in self.special_tokens_decoder:
                if not skip_special_tokens:
                    tokens.append(self.special_tokens_decoder[i])
            else:
                tokens.append(self.decoder[i])
        return tokens

    def encode(self, text):
        return self.convert_tokens_to_ids(self.tokenize(text))

    def decode(self, tokens):
        text = ''.join([self.decoder[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode(
            'utf-8', errors=self.errors)
        return text

    def save_vocabulary(self, vocab_path):
        """Save the tokenizer vocabulary and merge files to a directory."""
        if not os.path.isdir(vocab_path):
            logger.error('Vocabulary path ({}) should be a directory'.format(
                vocab_path))
            return
        vocab_file = os.path.join(vocab_path, VOCAB_NAME)
        merge_file = os.path.join(vocab_path, MERGES_NAME)
        special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)

        with open(vocab_file, 'w', encoding='utf-8') as f:
            f.write(json.dumps(self.encoder, ensure_ascii=False))

        index = 0
        with open(merge_file, 'w', encoding='utf-8') as writer:
            writer.write(u'#version: 0.2\n')
            for bpe_tokens, token_index in sorted(
                    self.bpe_ranks.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        'Saving vocabulary to {}: BPE merge indices are not consecutive.'
                        ' Please check that the tokenizer is not corrupted!'.
                        format(merge_file))
                    index = token_index
                writer.write(' '.join(bpe_tokens) + u'\n')
                index += 1

        index = len(self.encoder)
        with open(special_tokens_file, 'w', encoding='utf-8') as writer:
            for token, token_index in sorted(
                    self.special_tokens.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        'Saving special tokens vocabulary to {}: BPE indices are not consecutive.'
                        ' Please check that the tokenizer is not corrupted!'.
                        format(special_tokens_file))
                    index = token_index
                writer.write(token + u'\n')
                index += 1

        return vocab_file, merge_file, special_tokens_file
