#  Copyright (c) 2017-2018 Uber Technologies, Inc.
#
# 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 collections
import decimal
# Must import pyarrow before torch. See: https://github.com/uber/petastorm/blob/master/docs/troubleshoot.rst
import re
import logging
import numpy as np
from six import PY2
from torch.utils.data.dataloader import default_collate
import torch
from packaging import version

from petastorm.reader_impl.shuffling_buffer import RandomShufflingBuffer, NoopShufflingBuffer
from petastorm.reader_impl.pytorch_shuffling_buffer import BatchedRandomShufflingBuffer, \
    BatchedNoopShufflingBuffer
from petastorm.pytorch import _sanitize_pytorch_types,decimal_friendly_collate, LoaderBase
_TORCH_BEFORE_1_1 = version.parse(torch.__version__) < version.parse('1.1.0')  # type: ignore

if PY2:
    _string_classes = basestring  # noqa: F821
else:
    _string_classes = (str, bytes)

logger = logging.getLogger(__name__)




_PARALLEL_ITER_ERROR = "You must finish a full pass of Petastorm DataLoader before making another pass from the \
beginning.If you do need to terminate early and restart from beginning, please re-create the reader and the data \
loader."


class DataLoader(LoaderBase):
    """
    A data loader adaptor for ``torch.utils.data.DataLoader``.

    This class iterates and returns items from the Reader in batches.

    This loader can be used as an iterator and will terminate when the reader used in the construction of the class
    runs out of samples.
    """

    def __init__(self, reader, batch_size=1, collate_fn=decimal_friendly_collate,
                 shuffling_queue_capacity=0, desired_fields= None):
        """
        Initializes a data loader object, with a default collate.

        Number of epochs is defined by the configuration of the reader argument.

        An optional shuffling queue is created if shuffling_queue_capacity is greater than 0. No samples will be
        returned to a user by the ``DataLoader`` until the queue is full. After that, batches of `batch_size`
        will be created by uniformly sampling the shuffling queue. Once no more samples are available from the data
        reader, the shuffling queue is allowed to be consumed till no further samples are available.

        Note that the last returned batch could have less then ``batch_size`` samples.

        NOTE: ``make_batch_reader`` has it's own ``shuffle_row_groups`` argument. It randomizes order in
        which parquet row-groups are loaded and has no effect on the order of rows within each row-group. To achieve
        row-level shuffling you should set shuffling_queue_capacity to a non zero value.

        :param reader: petastorm Reader instance
        :param batch_size: the number of items to return per batch; factored into the len() of this reader
        :param collate_fn: an optional callable to merge a list of samples to form a mini-batch.
        :param shuffling_queue_capacity: Queue capacity is passed to the underlying :class:`tf.RandomShuffleQueue`
          instance. If set to 0, no shuffling will be done.
        """
        super(DataLoader, self).__init__()
        self.reader = reader
        self.batch_size = batch_size
        self.collate_fn = collate_fn

        # _batch_acc accumulates samples for a single batch.
        self._batch_acc = []
        self.shuffling_queue_capacity = shuffling_queue_capacity
        self._in_iter = None

        self.desired_fields = desired_fields

    def _iter_impl(self):
        """
        The Data Loader iterator stops the for-loop when reader runs out of samples.
        """
        # As we iterate over incoming samples, we are going to store them in `self._batch_acc`, until we have a batch of
        # the requested batch_size ready.

        keys = None
        if self.shuffling_queue_capacity > 0:
            # We can not know what is the reasonable number to use for the extra capacity, so we set a huge number
            # and give up on the unbound growth protection mechanism.
            min_after_dequeue = self.shuffling_queue_capacity - 1
            self._shuffling_buffer = RandomShufflingBuffer(self.shuffling_queue_capacity,
                                                           min_after_retrieve=min_after_dequeue,
                                                           extra_capacity=100000000)
        else:
            self._shuffling_buffer = NoopShufflingBuffer()

        subset = lambda d, keys: {key: d[key] for key in keys}
        for row in self.reader:
            # Default collate does not work nicely on namedtuples and treat them as lists
            # Using dict will result in the yielded structures being dicts as well
            row_as_dict = row._asdict()
            # We add the posibility to select given rows
            if self.desired_fields != None:
                row_as_dict= collections.OrderedDict(subset(row_as_dict, self.desired_fields))

            keys = row_as_dict.keys()
            # Promote some types that are incompatible with pytorch to be pytorch friendly.
            _sanitize_pytorch_types(row_as_dict)

            # Add rows to shuffling buffer
            if not self.reader.batched_output:
                self._shuffling_buffer.add_many([row_as_dict])
            else:
                # Transposition:
                #   row_as_dict:        {'a': [1,2,3], 'b':[4,5,6]}
                #   row_group_as_tuple: [(1, 4), (2, 5), (3, 6)]
                # The order within a tuple is defined by key order in 'keys'
                row_group_as_tuple = list(zip(*(row_as_dict[k] for k in keys)))

                # Adding data as 'row-by-row' into a shuffling buffer. This is a pretty
                # slow implementation though. Probably can comeup with a faster way to shuffle,
                # perhaps at the expense of a larger memory consumption...
                self._shuffling_buffer.add_many(row_group_as_tuple)

            # _yield_batches will emit as much batches as are allowed by the shuffling_buffer (RandomShufflingBuffer
            # will avoid underflowing below a certain number of samples to guarantee some samples decorrelation)
            for batch in self._yield_batches(keys):
                yield batch

        # Once reader can not read new rows, we might still have a bunch of rows waiting in the shuffling buffer.
        # Telling shuffling buffer that we are finished allows to deplete the buffer completely, regardless its
        # min_after_dequeue setting.
        self._shuffling_buffer.finish()

        for batch in self._yield_batches(keys):
            yield batch

        # Yield the last and partial batch
        if self._batch_acc:
            yield self.collate_fn(self._batch_acc)

    def _yield_batches(self, keys):
        while self._shuffling_buffer.can_retrieve():
            post_shuffled_row = self._shuffling_buffer.retrieve()
            if not isinstance(post_shuffled_row, dict):
                # This is for the case of batched reads. Here we restore back the
                # dictionary format of records
                post_shuffled_row = dict(zip(keys, post_shuffled_row))

            self._batch_acc.append(post_shuffled_row)

            # Batch is ready? Collate and emmit
            if len(self._batch_acc) == self.batch_size:
                yield self.collate_fn(self._batch_acc)
                self._batch_acc = []

    # Functions needed to treat data loader as a context manager
    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.reader.stop()
        self.reader.join()
    
class BatchedDataLoader(LoaderBase):
    """
    Same as DataLoader except it uses torch-based shuffling buffers which enable batched buffering
    (significantly faster for small data).
    """

    def __init__(self, reader, batch_size=1,
                 transform_fn=None,
                 shuffling_queue_capacity=0, desired_fields= None):
        """
        Initializes a data loader object.
        Number of epochs is defined by the configuration of the reader argument.
        An optional shuffling queue is created if shuffling_queue_capacity is greater than 0. No samples will be
        returned to a user by the ``BatchedDataLoader`` until the queue is full. After that, batches of `batch_size`
        will be created by uniformly sampling the shuffling queue. Once no more samples are available from the data
        reader, the shuffling queue is allowed to be consumed till no further samples are available.
        Note that the last returned batch could have less then ``batch_size`` samples.
        NOTE: if you are using ``make_batch_reader``, this shuffling queue will be randomizing the order of the
        entire batches and not changing the order of elements within a batch. This is likely not what you intend to do.
        This class does not support special types that are not supported in PyTorch (decimal/string).
        :param reader: petastorm Reader instance
        :param batch_size: the number of items to return per batch; factored into the len() of this reader
        :param transform_fn: an optional callable to convert batches from the reader to PyTorch tensors
        :param shuffling_queue_capacity: Queue capacity is passed to the underlying :class:`tf.RandomShuffleQueue`
          instance. If set to 0, no shuffling will be done.
        """
        super(BatchedDataLoader, self).__init__()
        self.reader = reader
        self.batch_size = batch_size
        self.transform_fn = transform_fn or torch.as_tensor
        self.desired_fields = desired_fields
        # _batch_acc accumulates samples for a single batch.
        self._batch_acc = []
        self.shuffling_queue_capacity = shuffling_queue_capacity
        self._in_iter = None

    def _iter_impl(self):
        """
        The Data Loader iterator stops the for-loop when reader runs out of samples.
        """
        # As we iterate over incoming samples, we are going to store them in `self._batch_acc`, until we have a batch of
        # the requested batch_size ready.

        keys = None
        if self.shuffling_queue_capacity > 0:
            # We can not know what is the reasonable number to use for the extra capacity, so we set a huge number
            # and give up on the unbound growth protection mechanism.
            # To keep the same behavior as DataLoader, we need to increase the shuffling_queue_capacity
            min_after_dequeue = self.shuffling_queue_capacity - 1
            shuffling_queue_capacity = min_after_dequeue + self.batch_size
            self._shuffling_buffer = BatchedRandomShufflingBuffer(
                shuffling_queue_capacity,
                min_after_retrieve=min_after_dequeue,
                extra_capacity=100000000,
                batch_size=self.batch_size
            )
        else:
            self._shuffling_buffer = BatchedNoopShufflingBuffer(batch_size=self.batch_size)

        
        subset = lambda d, keys: {key: d[key] for key in keys}
        for row in self.reader:
            # Default collate does not work nicely on namedtuples and treat them as lists
            # Using dict will result in the yielded structures being dicts as well
            row_as_dict = row._asdict()
            # We add the posibility to select given rows
            if self.desired_fields != None:
                row_as_dict= collections.OrderedDict(subset(row_as_dict, self.desired_fields))

            keys = row_as_dict.keys()

            # Promote some types that are incompatible with pytorch to be pytorch friendly.
            _sanitize_pytorch_types(row_as_dict)

            # Add rows to shuffling buffer
            for k, v in row_as_dict.items():
                if not self.reader.batched_output:
                    row_as_dict[k] = self.transform_fn([v])
                else:
                    row_as_dict[k] = self.transform_fn(v)
            self._shuffling_buffer.add_many(row_as_dict.values())

            # _yield_batches will emit as much batches as are allowed by the shuffling_buffer (RandomShufflingBuffer
            # will avoid underflowing below a certain number of samples to guarantee some samples decorrelation)
            for batch in self._yield_batches(keys):
                yield batch

        # Once reader can not read new rows, we might still have a bunch of rows waiting in the shuffling buffer.
        # Telling shuffling buffer that we are finished allows to deplete the buffer completely, regardless its
        # min_after_dequeue setting.
        self._shuffling_buffer.finish()

        for batch in self._yield_batches(keys):
            yield batch

    def _yield_batches(self, keys):
        while self._shuffling_buffer.can_retrieve():
            batch = self._shuffling_buffer.retrieve()
            if not isinstance(batch, dict):
                # This is for the case of batched reads. Here we restore back the
                # dictionary format of records
                batch = dict(zip(keys, batch))
            yield batch

    # Functions needed to treat data loader as a context manager
    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.reader.stop()
        self.reader.join()