# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 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.

from typing import Optional, Union

from paddlenlp.transformers.tokenizer_utils_base import (
    PaddingStrategy,
    TensorType,
    TruncationStrategy,
)

from ...utils.log import logger
from .. import BertTokenizer
from ..tokenizer_utils_base import BatchEncoding

__all__ = ["MobileBertTokenizer"]

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"mobilebert-uncased": 512}


class MobileBertTokenizer(BertTokenizer):
    r"""
    Construct a MobileBERT tokenizer.
    :class:`~paddlenlp.transformers.MobileBertTokenizer is identical to :class:`~paddlenlp.transformers.BertTokenizer` and runs end-to-end
    tokenization: punctuation splitting and wordpiece.
    Refer to superclass :class:`~~paddlenlp.transformers.BertTokenizer` for usage examples and documentation concerning
    parameters.
    """
    resource_files_names = {"vocab_file": "vocab.txt"}
    pretrained_resource_files_map = {
        "vocab_file": {
            "mobilebert-uncased": "https://bj.bcebos.com/paddlenlp/models/transformers/mobilebert/mobilebert-uncased/vocab.txt"
        }
    }
    pretrained_init_configuration = {"mobilebert-uncased": {"do_lower_case": True}}
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

    def batch_encode(
        self,
        batch_text_or_text_pairs,
        max_length: int = 512,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        stride=0,
        is_split_into_words=False,
        return_position_ids=False,
        return_token_type_ids=True,
        return_attention_mask=False,
        return_length=False,
        return_overflowing_tokens=False,
        return_special_tokens_mask=False,
        return_dict=True,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        verbose: bool = True,
        **kwargs
    ):
        """
        Performs tokenization and uses the tokenized tokens to prepare model
        inputs. It supports batch inputs of sequence or sequence pair.

        Args:
            batch_text_or_text_pairs (list):
                The element of list can be sequence or sequence pair, and the
                sequence is a string or a list of strings depending on whether
                it has been pretokenized. If each sequence is provided as a list
                of strings (pretokenized), you must set `is_split_into_words` as
                `True` to disambiguate with a sequence pair.
            max_length (int, optional):
                If set to a number, will limit the total sequence returned so
                that it has a maximum length. If there are overflowing tokens,
                those overflowing tokens will be added to the returned dictionary
                when `return_overflowing_tokens` is `True`. Defaults to `None`.
            stride (int, optional):
                Only available for batch input of sequence pair and mainly for
                question answering usage. When for QA, `text` represents questions
                and `text_pair` represents contexts. If `stride` is set to a
                positive number, the context will be split into multiple spans
                where `stride` defines the number of (tokenized) tokens to skip
                from the start of one span to get the next span, thus will produce
                a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample'
                and 'offset_mapping' preserving the original example and position
                information will be added to the returned dictionary. Defaults to 0.
            padding (bool, optional):
                If set to `True`, the returned sequences would be padded up to
                `max_length` specified length according to padding side
                (`self.padding_side`) and padding token id. Defaults to `False`.
            truncation_strategy (str, optional):
                String selected in the following options:
                - 'longest_first' (default) Iteratively reduce the inputs sequence
                until the input is under `max_length` starting from the longest
                one at each token (when there is a pair of input sequences).
                - 'only_first': Only truncate the first sequence.
                - 'only_second': Only truncate the second sequence.
                - 'do_not_truncate': Do not truncate (raise an error if the input
                sequence is longer than `max_length`).
                Defaults to 'longest_first'.
            return_position_ids (bool, optional):
                Whether to include tokens position ids in the returned dictionary.
                Defaults to `False`.
            return_token_type_ids (bool, optional):
                Whether to include token type ids in the returned dictionary.
                Defaults to `True`.
            return_attention_mask (bool, optional):
                Whether to include the attention mask in the returned dictionary.
                Defaults to `False`.
            return_length (bool, optional):
                Whether to include the length of each encoded inputs in the
                returned dictionary. Defaults to `False`.
            return_overflowing_tokens (bool, optional):
                Whether to include overflowing token information in the returned
                dictionary. Defaults to `False`.
            return_special_tokens_mask (bool, optional):
                Whether to include special tokens mask information in the returned
                dictionary. Defaults to `False`.

        Returns:
            dict:
                The dict has the following optional items:
                - **input_ids** (list[int]): List of token ids to be fed to a model.
                - **position_ids** (list[int], optional): List of token position ids to be
                  fed to a model. Included when `return_position_ids` is `True`
                - **token_type_ids** (list[int], optional): List of token type ids to be
                  fed to a model. Included when `return_token_type_ids` is `True`.
                - **attention_mask** (list[int], optional): List of integers valued 0 or 1,
                  where 0 specifies paddings and should not be attended to by the
                  model. Included when `return_attention_mask` is `True`.
                - **seq_len** (int, optional): The input_ids length. Included when `return_length`
                  is `True`.
                - **overflowing_tokens** (list[int], optional): List of overflowing tokens.
                  Included when if `max_length` is specified and `return_overflowing_tokens`
                  is True.
                - **num_truncated_tokens** (int, optional): The number of overflowing tokens.
                  Included when if `max_length` is specified and `return_overflowing_tokens`
                  is True.
                - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1,
                  with 0 specifying special added tokens and 1 specifying sequence tokens.
                  Included when `return_special_tokens_mask` is `True`.
                - **offset_mapping** (list[int], optional): list of pair preserving the
                  index of start and end char in original input for each token.
                  For a sqecial token, the index pair is `(0, 0)`. Included when
                  `stride` works.
                - **overflow_to_sample** (int, optional): Index of example from which this
                  feature is generated. Included when `stride` works.
        """
        # Backward compatibility for 'max_seq_len'
        old_max_seq_len = kwargs.get("max_seq_len", None)
        if max_length is None and old_max_seq_len:
            if verbose:
                logger.warnings(
                    "The `max_seq_len` argument is deprecated and will be removed in a future version, "
                    "please use `max_length` instead.",
                    FutureWarning,
                )
            max_length = old_max_seq_len

        padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
            padding=padding, max_length=max_length, verbose=verbose
        )

        def get_input_ids(text):
            if isinstance(text, str):
                tokens = self._tokenize(text)
                return self.convert_tokens_to_ids(tokens)
            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
                return self.convert_tokens_to_ids(text)
            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
                return text
            else:
                raise ValueError(
                    "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
                )

        batch_encode_inputs = []
        for example_id, tokens_or_pair_tokens in enumerate(batch_text_or_text_pairs):
            if not isinstance(tokens_or_pair_tokens, (list, tuple)):
                text, text_pair = tokens_or_pair_tokens, None
            elif is_split_into_words and not isinstance(tokens_or_pair_tokens[0], (list, tuple)):
                text, text_pair = tokens_or_pair_tokens, None
            else:
                text, text_pair = tokens_or_pair_tokens

            first_ids = get_input_ids(text)
            second_ids = get_input_ids(text_pair) if text_pair is not None else None

            if stride > 0 and second_ids is not None:

                max_len_for_pair = (
                    max_length - len(first_ids) - self.num_special_tokens_to_add(pair=True)
                )  # need -4  <sep> A </sep> </sep> B <sep>

                token_offset_mapping = self.get_offset_mapping(text)
                token_pair_offset_mapping = self.get_offset_mapping(text_pair)

                while True:
                    encoded_inputs = {}

                    ids = first_ids
                    mapping = token_offset_mapping
                    if len(second_ids) <= max_len_for_pair:
                        pair_ids = second_ids
                        pair_mapping = token_pair_offset_mapping
                    else:
                        pair_ids = second_ids[:max_len_for_pair]
                        pair_mapping = token_pair_offset_mapping[:max_len_for_pair]

                    offset_mapping = self.build_offset_mapping_with_special_tokens(mapping, pair_mapping)
                    sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
                    token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)

                    # Build output dictionary
                    encoded_inputs["input_ids"] = sequence
                    if return_token_type_ids:
                        encoded_inputs["token_type_ids"] = token_type_ids
                    if return_special_tokens_mask:
                        encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
                    if return_length:
                        encoded_inputs["seq_len"] = len(encoded_inputs["input_ids"])

                    # Check lengths
                    assert max_length is None or len(encoded_inputs["input_ids"]) <= max_length

                    # Padding
                    needs_to_be_padded = padding and max_length and len(encoded_inputs["input_ids"]) < max_length

                    encoded_inputs["offset_mapping"] = offset_mapping

                    if needs_to_be_padded:
                        difference = max_length - len(encoded_inputs["input_ids"])
                        if self.padding_side == "right":
                            if return_attention_mask:
                                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [
                                    0
                                ] * difference
                            if return_token_type_ids:
                                # 0 for padding token mask
                                encoded_inputs["token_type_ids"] = (
                                    encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
                                )
                            if return_special_tokens_mask:
                                encoded_inputs["special_tokens_mask"] = (
                                    encoded_inputs["special_tokens_mask"] + [1] * difference
                                )
                            encoded_inputs["input_ids"] = (
                                encoded_inputs["input_ids"] + [self.pad_token_id] * difference
                            )
                            encoded_inputs["offset_mapping"] = encoded_inputs["offset_mapping"] + [(0, 0)] * difference
                        elif self.padding_side == "left":
                            if return_attention_mask:
                                encoded_inputs["attention_mask"] = [0] * difference + [1] * len(
                                    encoded_inputs["input_ids"]
                                )
                            if return_token_type_ids:
                                # 0 for padding token mask
                                encoded_inputs["token_type_ids"] = [
                                    self.pad_token_type_id
                                ] * difference + encoded_inputs["token_type_ids"]
                            if return_special_tokens_mask:
                                encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs[
                                    "special_tokens_mask"
                                ]
                            encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs[
                                "input_ids"
                            ]
                            encoded_inputs["offset_mapping"] = [(0, 0)] * difference + encoded_inputs["offset_mapping"]
                    else:
                        if return_attention_mask:
                            encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])

                    if return_position_ids:
                        encoded_inputs["position_ids"] = list(range(len(encoded_inputs["input_ids"])))

                    encoded_inputs["overflow_to_sample"] = example_id
                    batch_encode_inputs.append(encoded_inputs)

                    if len(second_ids) <= max_len_for_pair:
                        break
                    else:
                        second_ids = second_ids[max_len_for_pair - stride :]
                        token_pair_offset_mapping = token_pair_offset_mapping[max_len_for_pair - stride :]

            else:
                batch_encode_inputs.append(
                    self.encode(
                        first_ids,
                        second_ids,
                        max_length=max_length,
                        padding=padding,
                        truncation=truncation,
                        return_position_ids=return_position_ids,
                        return_token_type_ids=return_token_type_ids,
                        return_attention_mask=return_attention_mask,
                        return_overflowing_tokens=return_overflowing_tokens,
                        return_special_tokens_mask=return_special_tokens_mask,
                    )
                )

        batch_encode_inputs = {k: [output[k] for output in batch_encode_inputs] for k in batch_encode_inputs[0].keys()}
        batch_encode_inputs = self.pad(
            batch_encode_inputs,
            padding=padding_strategy.value,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )
        if return_dict:
            batch_outputs = BatchEncoding(batch_encode_inputs, tensor_type=return_tensors)
            return batch_outputs
        else:
            batch_outputs_list = []
            for k, v in batch_encode_inputs.items():
                for i in range(len(v)):
                    if i >= len(batch_outputs_list):
                        batch_outputs_list.append({k: v[i]})
                    else:
                        batch_outputs_list[i][k] = v[i]
            return batch_outputs_list
