# coding=utf-8
# 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.
"""
 Sequence feature extraction class for common feature extractors to preprocess sequences.
"""
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


logger = logging.get_logger(__name__)


class SequenceFeatureExtractor(FeatureExtractionMixin):
    """
    This is a general feature extraction class for speech recognition.

    Args:
        feature_size (`int`):
            The feature dimension of the extracted features.
        sampling_rate (`int`):
            The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
        padding_value (`float`):
            The value that is used to fill the padding values / vectors.
    """

    def __init__(self, feature_size: int, sampling_rate: int, padding_value: float, **kwargs):
        self.feature_size = feature_size
        self.sampling_rate = sampling_rate
        self.padding_value = padding_value

        self.padding_side = kwargs.pop("padding_side", "right")
        self.return_attention_mask = kwargs.pop("return_attention_mask", True)

        super().__init__(**kwargs)

    def pad(
        self,
        processed_features: Union[
            BatchFeature,
            List[BatchFeature],
            Dict[str, BatchFeature],
            Dict[str, List[BatchFeature]],
            List[Dict[str, BatchFeature]],
        ],
        padding: Union[bool, str, PaddingStrategy] = True,
        max_length: Optional[int] = None,
        truncation: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
    ) -> BatchFeature:
        """
        Pad input values / input vectors or a batch of input values / input vectors up to predefined length or to the
        max sequence length in the batch.

        Padding side (left/right) padding values are defined at the feature extractor level (with `self.padding_side`,
        `self.padding_value`)

        <Tip>

        If the `processed_features` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
        result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of
        PyTorch tensors, you will lose the specific device of your tensors however.

        </Tip>

        Args:
            processed_features ([`BatchFeature`], list of [`BatchFeature`], `Dict[str, List[float]]`, `Dict[str, List[List[float]]` or `List[Dict[str, List[float]]]`):
                Processed inputs. Can represent one input ([`BatchFeature`] or `Dict[str, List[float]]`) or a batch of
                input values / vectors (list of [`BatchFeature`], *Dict[str, List[List[float]]]* or *List[Dict[str,
                List[float]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
                collate function.

                Instead of `List[float]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
                see the note above for the return type.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
                Select a strategy to pad the returned sequences (according to the model's padding side and padding
                index) among:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            max_length (`int`, *optional*):
                Maximum length of the returned list and optionally padding length (see above).
            truncation (`bool`):
                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
            return_attention_mask (`bool`, *optional*):
                Whether to return the attention mask. If left to the default, will return the attention mask according
                to the specific feature_extractor's default.

                [What are attention masks?](../glossary#attention-mask)
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        """
        # If we have a list of dicts, let's convert it in a dict of lists
        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
        if isinstance(processed_features, (list, tuple)) and isinstance(processed_features[0], (dict, BatchFeature)):
            processed_features = {
                key: [example[key] for example in processed_features] for key in processed_features[0].keys()
            }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
            raise ValueError(
                "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
                f" to this method that includes {self.model_input_names[0]}, but you provided"
                f" {list(processed_features.keys())}"
            )

        required_input = processed_features[self.model_input_names[0]]
        return_attention_mask = (
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if not required_input:
            if return_attention_mask:
                processed_features["attention_mask"] = []
            return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        first_element = required_input[0]
        if isinstance(first_element, (list, tuple)):
            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
            index = 0
            while len(required_input[index]) == 0:
                index += 1
            if index < len(required_input):
                first_element = required_input[index][0]

        if return_tensors is None:
            if is_tf_tensor(first_element):
                return_tensors = "tf"
            elif is_torch_tensor(first_element):
                return_tensors = "pt"
            elif isinstance(first_element, (int, float, list, tuple, np.ndarray)):
                return_tensors = "np"
            else:
                raise ValueError(
                    f"type of {first_element} unknown: {type(first_element)}. "
                    "Should be one of a python, numpy, pytorch or tensorflow object."
                )

        for key, value in processed_features.items():
            if isinstance(value[0], (int, float)):
                processed_features[key] = to_numpy(value)
            else:
                processed_features[key] = [to_numpy(v) for v in value]

        # Convert padding_strategy in PaddingStrategy
        padding_strategy = self._get_padding_strategies(padding=padding, max_length=max_length)

        required_input = processed_features[self.model_input_names[0]]

        batch_size = len(required_input)
        if not all(len(v) == batch_size for v in processed_features.values()):
            raise ValueError("Some items in the output dictionary have a different batch size than others.")

        truncated_inputs = []
        for i in range(batch_size):
            inputs = dict((k, v[i]) for k, v in processed_features.items())
            # truncation
            inputs_slice = self._truncate(
                inputs,
                max_length=max_length,
                pad_to_multiple_of=pad_to_multiple_of,
                truncation=truncation,
            )
            truncated_inputs.append(inputs_slice)

        if padding_strategy == PaddingStrategy.LONGEST:
            # make sure that `max_length` cannot be longer than the longest truncated length
            max_length = max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
            padding_strategy = PaddingStrategy.MAX_LENGTH

        batch_outputs = {}
        for i in range(batch_size):
            # padding
            outputs = self._pad(
                truncated_inputs[i],
                max_length=max_length,
                padding_strategy=padding_strategy,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )

            for key, value in outputs.items():
                if key not in batch_outputs:
                    batch_outputs[key] = []
                if value.dtype is np.dtype(np.float64):
                    value = value.astype(np.float32)
                batch_outputs[key].append(value)

        return BatchFeature(batch_outputs, tensor_type=return_tensors)

    def _pad(
        self,
        processed_features: Union[Dict[str, np.ndarray], BatchFeature],
        max_length: Optional[int] = None,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
    ) -> dict:
        """
        Pad inputs (on left/right and up to predefined length or max length in the batch)

        Args:
            processed_features:
                Dictionary of input values (`np.ndarray[float]`) / input vectors (`List[np.ndarray[float]]`) or batch
                of inputs values (`List[np.ndarray[int]]`) / input vectors (`List[np.ndarray[int]]`)
            max_length: maximum length of the returned list and optionally padding length (see below)
            padding_strategy: PaddingStrategy to use for padding.

                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
                - PaddingStrategy.DO_NOT_PAD: Do not pad
                The feature_extractor padding sides are defined in self.padding_side:

                    - 'left': pads on the left of the sequences
                    - 'right': pads on the right of the sequences
            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
            return_attention_mask:
                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
        """
        required_input = processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = len(required_input)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
            processed_features["attention_mask"] = np.ones(len(required_input), dtype=np.int32)

        if needs_to_be_padded:
            difference = max_length - len(required_input)
            if self.padding_side == "right":
                if return_attention_mask:
                    processed_features["attention_mask"] = np.pad(
                        processed_features["attention_mask"], (0, difference)
                    )
                padding_shape = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                processed_features[self.model_input_names[0]] = np.pad(
                    required_input, padding_shape, "constant", constant_values=self.padding_value
                )
            elif self.padding_side == "left":
                if return_attention_mask:
                    processed_features["attention_mask"] = np.pad(
                        processed_features["attention_mask"], (difference, 0)
                    )
                padding_shape = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                processed_features[self.model_input_names[0]] = np.pad(
                    required_input, padding_shape, "constant", constant_values=self.padding_value
                )
            else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side))

        return processed_features

    def _truncate(
        self,
        processed_features: Union[Dict[str, np.ndarray], BatchFeature],
        max_length: Optional[int] = None,
        pad_to_multiple_of: Optional[int] = None,
        truncation: Optional[bool] = None,
    ):
        """
        Truncate inputs to predefined length or max length in the batch

        Args:
            processed_features:
                Dictionary of input values (`np.ndarray[float]`) / input vectors (`List[np.ndarray[float]]`) or batch
                of inputs values (`List[np.ndarray[int]]`) / input vectors (`List[np.ndarray[int]]`)
            max_length: maximum length of the returned list and optionally padding length (see below)
            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
            truncation:
                (optional) Activates truncation to cut input sequences longer than `max_length` to `max_length`.
        """
        if not truncation:
            return processed_features
        elif truncation and max_length is None:
            raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined.")

        required_input = processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        needs_to_be_truncated = len(required_input) > max_length

        if needs_to_be_truncated:
            processed_features[self.model_input_names[0]] = processed_features[self.model_input_names[0]][:max_length]
            if "attention_mask" in processed_features:
                processed_features["attention_mask"] = processed_features["attention_mask"][:max_length]

        return processed_features

    def _get_padding_strategies(self, padding=False, max_length=None):
        """
        Find the correct padding strategy
        """

        # Get padding strategy
        if padding is not False:
            if padding is True:
                padding_strategy = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
            elif not isinstance(padding, PaddingStrategy):
                padding_strategy = PaddingStrategy(padding)
            elif isinstance(padding, PaddingStrategy):
                padding_strategy = padding
        else:
            padding_strategy = PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
            if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"
                )

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
            raise ValueError(
                "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
                " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."
            )

        return padding_strategy
