# coding=utf-8
# Copyright 2021 Google LLC
#
# 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
#
#     https://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.

"""ResNet pre-trained networks."""

# pylint: disable=g-importing-member
from functools import partial
from typing import Any, Callable, Sequence, Tuple

from flax import linen as nn
from flax.training import checkpoints
import jax.numpy as jnp

ModuleDef = Any


class ResNetBlock(nn.Module):
  """ResNet block."""

  filters: int
  conv: ModuleDef
  norm: ModuleDef
  act: Callable[[jnp.ndarray], jnp.ndarray]
  strides: Tuple[int, int] = (1, 1)

  @nn.compact
  def __call__(
      self,
      x,
  ):
    residual = x
    y = self.conv(self.filters, (3, 3), self.strides)(x)
    y = self.norm()(y)
    y = self.act(y)
    y = self.conv(self.filters, (3, 3))(y)
    y = self.norm(scale_init=nn.initializers.zeros)(y)

    if residual.shape != y.shape:
      residual = self.conv(
          self.filters, (1, 1), self.strides, name='conv_proj'
      )(residual)
      residual = self.norm(name='norm_proj')(residual)

    return self.act(residual + y)


class BottleneckResNetBlock(nn.Module):
  """Bottleneck ResNet block."""

  filters: int
  conv: ModuleDef
  norm: ModuleDef
  act: Callable[[jnp.ndarray], jnp.ndarray]
  strides: Tuple[int, int] = (1, 1)

  @nn.compact
  def __call__(self, x):
    residual = x
    y = self.conv(self.filters, (1, 1))(x)
    y = self.norm()(y)
    y = self.act(y)
    y = self.conv(self.filters, (3, 3), self.strides)(y)
    y = self.norm()(y)
    y = self.act(y)
    y = self.conv(self.filters * 4, (1, 1))(y)
    y = self.norm(scale_init=nn.initializers.zeros)(y)

    if residual.shape != y.shape:
      residual = self.conv(
          self.filters * 4, (1, 1), self.strides, name='conv_proj'
      )(residual)
      residual = self.norm(name='norm_proj')(residual)

    return self.act(residual + y)


class ResNet(nn.Module):
  """ResNetV1."""

  stage_sizes: Sequence[int]
  block_cls: ModuleDef
  num_classes: int
  num_filters: int = 64
  dtype: Any = jnp.float32
  act: Callable[[jnp.ndarray], jnp.ndarray] = nn.relu
  conv: ModuleDef = nn.Conv

  @nn.compact
  def __call__(self, x, train: bool = True):
    conv = partial(self.conv, use_bias=False, dtype=self.dtype)
    norm = partial(
        nn.BatchNorm,
        use_running_average=not train,
        momentum=0.9,
        epsilon=1e-5,
        dtype=self.dtype,
    )

    x = conv(
        self.num_filters,
        (7, 7),
        (2, 2),
        padding=[(3, 3), (3, 3)],
        name='conv_init',
    )(x)
    x = norm(name='bn_init')(x)
    x = nn.relu(x)
    x = nn.max_pool(x, (3, 3), strides=(2, 2), padding='SAME')
    for i, block_size in enumerate(self.stage_sizes):
      for j in range(block_size):
        strides = (2, 2) if i > 0 and j == 0 else (1, 1)
        x = self.block_cls(
            self.num_filters * 2**i,
            strides=strides,
            conv=conv,
            norm=norm,
            act=self.act,
        )(x)
    x = jnp.mean(x, axis=(1, 2))
    # x = nn.Dense(self.num_classes, dtype=self.dtype)(x)
    x = jnp.asarray(x, self.dtype)
    return x


ResNet18 = partial(ResNet, stage_sizes=[2, 2, 2, 2], block_cls=ResNetBlock)
ResNet34 = partial(ResNet, stage_sizes=[3, 4, 6, 3], block_cls=ResNetBlock)
ResNet50 = partial(
    ResNet, stage_sizes=[3, 4, 6, 3], block_cls=BottleneckResNetBlock
)
ResNet101 = partial(
    ResNet, stage_sizes=[3, 4, 23, 3], block_cls=BottleneckResNetBlock
)
ResNet152 = partial(
    ResNet, stage_sizes=[3, 8, 36, 3], block_cls=BottleneckResNetBlock
)
ResNet200 = partial(
    ResNet, stage_sizes=[3, 24, 36, 3], block_cls=BottleneckResNetBlock
)


ResNet18Local = partial(
    ResNet, stage_sizes=[2, 2, 2, 2], block_cls=ResNetBlock, conv=nn.ConvLocal
)


# Load model checkpoint from cloud.
def load_params(config_name='v100_x8'):
  """Loads the paramteters of a pre-trained ResNet50 or based on the given config_name.

  Args:
    config_name: The trained ResNet to load.

  Returns:
    The parameters to be used with the respective model.
  """
  chkp = checkpoints.restore_checkpoint(pretrained_path, target=None)
  state = {'params': chkp['params'], 'batch_stats': chkp['batch_stats']}
  return state


def embed(params, x, model_cls=ResNet50, num_classes=1000):
  model = model_cls(num_classes=num_classes)
  y = model.apply(params, x, train=False)
  return y
