# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Dataset help for minddata dataset"""
from __future__ import absolute_import

import math

from mindspore import _checkparam as Validator
from mindspore import context
from mindspore.train._utils import _exec_datagraph, _get_types_and_shapes
from mindspore.nn.wrap import GetNextSingleOp
from mindspore.parallel._utils import _get_device_num, _need_to_full, _to_full_shapes
from mindspore.parallel._ps_context import _is_role_sched


def _send_data(dataset, epoch_num):
    """Engine dataset to write data to tdt queue."""
    if not hasattr(dataset, '__has_sent__'):
        exec_dataset = dataset.__transfer_dataset__
        exec_dataset.send(epoch_num)
        dataset.__has_sent__ = True


def _send_data_no_flag(dataset, epoch_num):
    """Engine dataset to write data to tdt queue directly."""
    exec_dataset = dataset.__transfer_dataset__
    exec_dataset.send(epoch_num)


class DatasetHelper:
    """
    Help function to use the MindData dataset.

    According to different contexts, change the iterations of dataset and use the same iteration for loop in different
    contexts.

    Note:
        The iteration of DatasetHelper will provide one epoch data.

    Args:
        dataset (DataSet): The training dataset iterator.
        dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data from host.
            Default: ``True``.
        sink_size (int): Control the number of steps for each sinking.
                             If sink_size=-1, sink the complete dataset for each epoch.
                             If sink_size>0, sink sink_size data for each epoch. Default: -1.
        epoch_num (int): Control the number of epoch data to send. Default: 1.
        iter_first_order (int): Control the number of steps first_order graph to execute. Default: 1.

    Examples:
        >>> dataset_helper = DatasetHelper(dataset)
        >>> for inputs in dataset_helper:
        >>>     outputs = network(*inputs)
    """

    def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1, iter_first_order=1):
        dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
        Validator.check_is_int(sink_size)
        if sink_size < -1 or sink_size == 0:
            raise ValueError("The sink_size must be -1 or positive, but got sink_size {}.".format(sink_size))

        if dataset_sink_mode:
            if context.get_context("device_target") == "Ascend":
                iterclass = _DatasetIterMSLoopSink
                self.iter = iterclass(dataset, sink_size, epoch_num, iter_first_order)
            elif context.get_context("device_target") == "GPU":
                iterclass = _DatasetIterMS
                self.iter = iterclass(dataset, sink_size, epoch_num)
            elif context.get_context("device_target") == "CPU":
                raise RuntimeError("Currently dataset sink mode is not supported when the device target is CPU.")

    def __iter__(self):
        return self.iter.__iter__()

    # A temp solution for loop sink. Delete later
    def types_shapes(self):
        """Get the types and shapes from dataset on the current configuration."""
        return self.iter.types_shapes()

    def sink_size(self):
        """Get sink_size for each iteration."""
        return self.iter.get_sink_size()

    def stop_send(self):
        """Free up resources about data sink."""
        self.iter.stop_send()


class _DatasetIter:
    """Base iter for dataset helper"""
    def __init__(self, dataset, sink_size, epoch_num):
        self.dataset = dataset
        self.sink_size = sink_size
        self.sink_count = 1

        if not hasattr(dataset, '__transfer_dataset__'):
            if hasattr(dataset, '__loop_size__'):
                self.sink_size = dataset.__loop_size__
            dataset.__transfer_dataset__ = _exec_datagraph(dataset, self.sink_size)

            if not hasattr(dataset, '__no_send__'):
                _send_data(dataset, epoch_num)
        else:
            _send_data_no_flag(dataset, epoch_num)

        self.stop_send = dataset.__transfer_dataset__.stop_send
        self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)

    def __iter__(self):
        self.index = 0
        return self

    def __next__(self):
        if self.index >= self.sink_count:
            raise StopIteration()
        self.index += 1
        return self.op()

    def types_shapes(self):
        return self.dataset_types, self.dataset_shapes

    def get_sink_count(self, dataset):
        sink_count = 1
        if hasattr(dataset, '__loop_size__'):
            loop_size = dataset.__loop_size__
            if loop_size <= dataset.get_dataset_size() and dataset.get_dataset_size() % loop_size != 0:
                raise ValueError(f'Dataset size {dataset.get_dataset_size()} and '
                                 f'sink_size {loop_size} are not matched.')
            sink_count = math.ceil(dataset.get_dataset_size() / loop_size)
        return sink_count

    def get_sink_size(self):
        """get sink_size to device"""
        sink_size = 1
        if hasattr(self.dataset, '__loop_size__'):
            sink_size = self.dataset.__loop_size__
        else:
            if context.get_context("enable_ge") or context.get_context("device_target") == "Ascend":
                if self.sink_size > 0:
                    sink_size = self.sink_size
                else:
                    sink_size = self.dataset.get_dataset_size()
        return sink_size


class _DatasetIterMSLoopSink(_DatasetIter):
    """Iter for context when device_target is Ascend"""
    def __init__(self, dataset, sink_size, epoch_num, iter_first_order):
        super().__init__(dataset, sink_size, epoch_num)
        sink_count = 1
        if hasattr(dataset, '__loop_size__'):
            loop_size = dataset.__loop_size__ + iter_first_order
            sink_count = int(sink_size / loop_size) * 2
        self.sink_count = sink_count
        if _is_role_sched():
            self.sink_count = 1
        # for self._parallel_mode equal to semi_auto_parallel or auto_parallel, and not using full_batch,
        # use a complete tensor to compile, and slice tensor to run. The batch dimension of tensors for
        # compile is device_number times the batch dimension of tensors for run. Now only support LoopSink.
        if _need_to_full():
            device_num = _get_device_num()
            self.dataset_shapes = _to_full_shapes(self.dataset_shapes, device_num)

        def op():
            return tuple()

        self.op = op


class _DatasetIterMS(_DatasetIter):
    """Iter for MS when enable_loop_sink is False."""
    def __init__(self, dataset, sink_size, epoch_num):
        super().__init__(dataset, sink_size, epoch_num)
        if sink_size > 0:
            self.sink_count = sink_size
        else:
            self.sink_count = dataset.get_dataset_size()

        queue_name = dataset.__transfer_dataset__.queue_name
        self.op = GetNextSingleOp(self.dataset_types, self.dataset_shapes, queue_name)
