import os
from enum import Enum
from typing import List, Optional, Union

import logging
from transformers import (DataProcessor,
                          InputExample,
                          InputFeatures,
                          PreTrainedTokenizer)


logger = logging.getLogger(__name__)


def glue_convert_examples_to_features(
    examples: List[InputExample],
    tokenizer: PreTrainedTokenizer,
    max_length: Optional[int] = None,
    task=None,
    label_list=None,
    output_mode=None,
):
    """
    Loads a data file into a list of ``InputFeatures``

    Args:
        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
        tokenizer: Instance of a tokenizer that will tokenize the examples
        max_length: Maximum example length. Defaults to the tokenizer's max_len
        task: GLUE task
        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
        output_mode: String indicating the output mode, classification

    Returns:
        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset`` containing the
        task-specific features. If the input is a list of ``InputExamples``, will return a list of task-specific
        ``InputFeatures`` which can be fed to the model.

    """
    return _glue_convert_examples_to_features(
        examples, tokenizer, max_length=max_length, task=task, label_list=label_list, output_mode=output_mode
    )


def _glue_convert_examples_to_features(
    examples: List[InputExample],
    tokenizer: PreTrainedTokenizer,
    max_length: Optional[int] = None,
    task=None,
    label_list=None,
    output_mode=None,
):
    if max_length is None:
        max_length = tokenizer.max_len

    if task is not None:
        processor = glue_processors[task]()
        if label_list is None:
            label_list = processor.get_labels()
            logger.info("Using label list %s for task %s" % (label_list, task))
        if output_mode is None:
            output_mode = glue_output_modes[task]
            logger.info("Using output mode %s for task %s" % (output_mode, task))

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

    def label_from_example(example: InputExample) -> Union[int, float, None]:
        if example.label is None:
            return None
        return label_map[example.label]

    labels = [label_from_example(example) for example in examples]

    batch_encoding = tokenizer(
        [[example.text_a, example.text_b] for example in examples],
        max_length=max_length,
        padding="max_length",
        truncation=True,
    )

    features = []
    for i in range(len(examples)):
        inputs = {k: batch_encoding[k][i] for k in batch_encoding}

        feature = InputFeatures(**inputs, label=labels[i])
        features.append(feature)

    for i, example in enumerate(examples[:5]):
        logger.info("*** Example ***")
        logger.info("guid: %s" % example.guid)
        logger.info("features: %s" % features[i])

    return features


class OutputMode(Enum):
    classification = "classification"

class EHRProcessor(DataProcessor):
    """Processor for EHR data."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def get_example_from_tensor_dict(self, tensor_dict):
        """See base class."""
        return InputExample(
            tensor_dict["idx"].numpy(),
            tensor_dict["sentence"].numpy().decode("utf-8"),
            None,
            str(tensor_dict["label"].numpy()),
        )

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training, dev and test sets."""
        examples = []
        text_index = 1 if set_type == "test" else 0
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[text_index]
            label = None if set_type == "test" else line[1]
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples


glue_tasks_num_labels = {"ehr-re": 2}

glue_processors = {"ehr-re": EHRProcessor}

glue_output_modes = {"ehr-re": "classification"}