# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, 
# The HuggingFace Inc. team, and The XTREME Benchmark Authors.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
"""Utility functions for NER/POS tagging tasks."""

from __future__ import absolute_import, division, print_function

import logging
import os
from io import open
from transformers import XLMTokenizer

logger = logging.getLogger(__name__)


class InputExample(object):
  """A single training/test example for token classification."""

  def __init__(self, guid, words, labels, langs=None):
    """Constructs a InputExample.

    Args:
      guid: Unique id for the example.
      words: list. The words of the sequence.
      labels: (Optional) list. The labels for each word of the sequence. This should be
      specified for train and dev examples, but not for test examples.
    """
    self.guid = guid
    self.words = words
    self.labels = labels
    self.langs = langs


class InputFeatures(object):
  """A single set of features of data."""

  def __init__(self, input_ids, input_mask, segment_ids, label_ids, langs=None):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.label_ids = label_ids
    self.langs = langs


def read_examples_from_file(file_path, lang, lang2id=None):
  if not os.path.exists(file_path):
    logger.info("[Warming] file {} not exists".format(file_path))
    return []
  guid_index = 1
  examples = []
  subword_len_counter = 0
  if lang2id:
    lang_id = lang2id.get(lang, lang2id['en'])
  else:
    lang_id = 0
  logger.info("lang_id={}, lang={}, lang2id={}".format(lang_id, lang, lang2id))
  with open(file_path, encoding="utf-8") as f:
    words = []
    labels = []
    langs = []
    for line in f:
      if line.startswith("-DOCSTART-") or line == "" or line == "\n":
        if word:
          examples.append(InputExample(guid="{}-{}".format(lang, guid_index),
                         words=words,
                         labels=labels,
                         langs=langs))
          guid_index += 1
          words = []
          labels = []
          langs = []
          subword_len_counter = 0
        else:
          print(f'guid_index', guid_index, words, langs, labels, subword_len_counter)
      else:
        splits = line.split("\t")
        word = splits[0]
      
        words.append(splits[0])
        langs.append(lang_id)
        if len(splits) > 1:
          labels.append(splits[-1].replace("\n", ""))
        else:
          # Examples could have no label for mode = "test"
          labels.append("O")
    if words:
      examples.append(InputExample(guid="%s-%d".format(lang, guid_index),
                     words=words,
                     labels=labels,
                     langs=langs))
  return examples

def convert_examples_to_features(examples,
                 label_list,
                 max_seq_length,
                 tokenizer,
                 cls_token_at_end=False,
                 cls_token="[CLS]",
                 cls_token_segment_id=1,
                 sep_token="[SEP]",
                 sep_token_extra=False,
                 pad_on_left=False,
                 pad_token=0,
                 pad_token_segment_id=0,
                 pad_token_label_id=-1,
                 sequence_a_segment_id=0,
                 mask_padding_with_zero=True,
                 lang='en'):
  """ Loads a data file into a list of `InputBatch`s
    `cls_token_at_end` define the location of the CLS token:
      - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
      - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
    `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
  """

  label_map = {label: i for i, label in enumerate(label_list)}

  features = []
  for (ex_index, example) in enumerate(examples):
    if ex_index % 10000 == 0:
      logger.info("Writing example %d of %d", ex_index, len(examples))

    tokens = []
    label_ids = []
    for word, label in zip(example.words, example.labels):
      if isinstance(tokenizer, XLMTokenizer):
        word_tokens = tokenizer.tokenize(word, lang=lang)
      else:
        word_tokens = tokenizer.tokenize(word)
      if len(word) != 0 and len(word_tokens) == 0:
        word_tokens = [tokenizer.unk_token]
      tokens.extend(word_tokens)
      # Use the real label id for the first token of the word, and padding ids for the remaining tokens
      label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))

    # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
    special_tokens_count = 3 if sep_token_extra else 2
    if len(tokens) > max_seq_length - special_tokens_count:
      print('truncate token', len(tokens), max_seq_length, special_tokens_count)
      tokens = tokens[:(max_seq_length - special_tokens_count)]
      label_ids = label_ids[:(max_seq_length - special_tokens_count)]    

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids:   0   0   0   0  0     0   0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.

    tokens += [sep_token]
    label_ids += [pad_token_label_id]
    if sep_token_extra:
      # roberta uses an extra separator b/w pairs of sentences
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
    segment_ids = [sequence_a_segment_id] * len(tokens)

    if cls_token_at_end:
      tokens += [cls_token]
      label_ids += [pad_token_label_id]
      segment_ids += [cls_token_segment_id]
    else:
      tokens = [cls_token] + tokens
      label_ids = [pad_token_label_id] + label_ids
      segment_ids = [cls_token_segment_id] + segment_ids

    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

    # Zero-pad up to the sequence length.
    padding_length = max_seq_length - len(input_ids)
    if pad_on_left:
      input_ids = ([pad_token] * padding_length) + input_ids
      input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
      segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
      label_ids = ([pad_token_label_id] * padding_length) + label_ids
    else:
      input_ids += ([pad_token] * padding_length)
      input_mask += ([0 if mask_padding_with_zero else 1] * padding_length)
      segment_ids += ([pad_token_segment_id] * padding_length)
      label_ids += ([pad_token_label_id] * padding_length)

    if example.langs and len(example.langs) > 0:
      langs = [example.langs[0]] * max_seq_length
    else:
      print('example.langs', example.langs, example.words, len(example.langs))
      print('ex_index', ex_index, len(examples))
      langs = None

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    assert len(label_ids) == max_seq_length
    assert len(langs) == max_seq_length

    if ex_index < 5:
      logger.info("*** Example ***")
      logger.info("guid: %s", example.guid)
      logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
      logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
      logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
      logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
      logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))
      logger.info("langs: {}".format(langs))

    features.append(
        InputFeatures(input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_ids=label_ids,
                langs=langs))
  return features


def get_labels(path):
  with open(path, "r") as f:
    labels = f.read().splitlines()
  if "O" not in labels:
    labels = ["O"] + labels
  return labels
