# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# 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.

import copy
import csv
import json
import logging

from ...file_utils import is_tf_available, is_torch_available


logger = logging.getLogger(__name__)


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

    Args:
        guid: Unique id for the example.
        text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
        text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
        label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """

    def __init__(self, guid, text_a, text_b=None, label=None):
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


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

    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
        label: Label corresponding to the input
    """

    def __init__(self, input_ids, attention_mask=None, token_type_ids=None, label=None):
        self.input_ids = input_ids
        self.attention_mask = attention_mask
        self.token_type_ids = token_type_ids
        self.label = label

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            return list(csv.reader(f, delimiter="\t", quotechar=quotechar))


class SingleSentenceClassificationProcessor(DataProcessor):
    """ Generic processor for a single sentence classification data set."""

    def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
        self.labels = [] if labels is None else labels
        self.examples = [] if examples is None else examples
        self.mode = mode
        self.verbose = verbose

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, idx):
        if isinstance(idx, slice):
            return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx])
        return self.examples[idx]

    @classmethod
    def create_from_csv(
        cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
    ):
        processor = cls(**kwargs)
        processor.add_examples_from_csv(
            file_name,
            split_name=split_name,
            column_label=column_label,
            column_text=column_text,
            column_id=column_id,
            skip_first_row=skip_first_row,
            overwrite_labels=True,
            overwrite_examples=True,
        )
        return processor

    @classmethod
    def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs):
        processor = cls(**kwargs)
        processor.add_examples(texts_or_text_and_labels, labels=labels)
        return processor

    def add_examples_from_csv(
        self,
        file_name,
        split_name="",
        column_label=0,
        column_text=1,
        column_id=None,
        skip_first_row=False,
        overwrite_labels=False,
        overwrite_examples=False,
    ):
        lines = self._read_tsv(file_name)
        if skip_first_row:
            lines = lines[1:]
        texts = []
        labels = []
        ids = []
        for (i, line) in enumerate(lines):
            texts.append(line[column_text])
            labels.append(line[column_label])
            if column_id is not None:
                ids.append(line[column_id])
            else:
                guid = "%s-%s" % (split_name, i) if split_name else "%s" % i
                ids.append(guid)

        return self.add_examples(
            texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples
        )

    def add_examples(
        self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
    ):
        assert labels is None or len(texts_or_text_and_labels) == len(labels)
        assert ids is None or len(texts_or_text_and_labels) == len(ids)
        if ids is None:
            ids = [None] * len(texts_or_text_and_labels)
        if labels is None:
            labels = [None] * len(texts_or_text_and_labels)
        examples = []
        added_labels = set()
        for (text_or_text_and_label, label, guid) in zip(texts_or_text_and_labels, labels, ids):
            if isinstance(text_or_text_and_label, (tuple, list)) and label is None:
                text, label = text_or_text_and_label
            else:
                text = text_or_text_and_label
            added_labels.add(label)
            examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label))

        # Update examples
        if overwrite_examples:
            self.examples = examples
        else:
            self.examples.extend(examples)

        # Update labels
        if overwrite_labels:
            self.labels = list(added_labels)
        else:
            self.labels = list(set(self.labels).union(added_labels))

        return self.examples

    def get_features(
        self,
        tokenizer,
        max_length=None,
        pad_on_left=False,
        pad_token=0,
        mask_padding_with_zero=True,
        return_tensors=None,
    ):
        """
        Convert examples in a list of ``InputFeatures``

        Args:
            tokenizer: Instance of a tokenizer that will tokenize the examples
            max_length: Maximum example length
            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. Either ``regression`` or ``classification``
            pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
            pad_token: Padding token
            mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
                and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
                actual values)

        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.

        """
        if max_length is None:
            max_length = tokenizer.max_len

        label_map = {label: i for i, label in enumerate(self.labels)}

        all_input_ids = []
        for (ex_index, example) in enumerate(self.examples):
            if ex_index % 10000 == 0:
                logger.info("Tokenizing example %d", ex_index)

            input_ids = tokenizer.encode(
                example.text_a, add_special_tokens=True, max_length=min(max_length, tokenizer.max_len),
            )
            all_input_ids.append(input_ids)

        batch_length = max(len(input_ids) for input_ids in all_input_ids)

        features = []
        for (ex_index, (input_ids, example)) in enumerate(zip(all_input_ids, self.examples)):
            if ex_index % 10000 == 0:
                logger.info("Writing example %d", ex_index)
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = batch_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

            assert len(input_ids) == batch_length, "Error with input length {} vs {}".format(
                len(input_ids), batch_length
            )
            assert len(attention_mask) == batch_length, "Error with input length {} vs {}".format(
                len(attention_mask), batch_length
            )

            if self.mode == "classification":
                label = label_map[example.label]
            elif self.mode == "regression":
                label = float(example.label)
            else:
                raise ValueError(self.mode)

            if ex_index < 5 and self.verbose:
                logger.info("*** Example ***")
                logger.info("guid: %s" % (example.guid))
                logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
                logger.info("label: %s (id = %d)" % (example.label, label))

            features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label))

        if return_tensors is None:
            return features
        elif return_tensors == "tf":
            if not is_tf_available():
                raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
            import tensorflow as tf

            def gen():
                for ex in features:
                    yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

            dataset = tf.data.Dataset.from_generator(
                gen,
                ({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
                ({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])),
            )
            return dataset
        elif return_tensors == "pt":
            if not is_torch_available():
                raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
            import torch
            from torch.utils.data import TensorDataset

            all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
            all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
            if self.mode == "classification":
                all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
            elif self.mode == "regression":
                all_labels = torch.tensor([f.label for f in features], dtype=torch.float)

            dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels)
            return dataset
        else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'")
