# Copyright 2020 The Flax Authors.
#
# 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.

"""Flax implementation of ResNet V1.
"""


from flax import linen as nn

import jax.numpy as jnp

from functools import partial
from typing import Any, Callable, Sequence

ModuleDef = Any


class ResNetBlock(nn.Module):
  """ResNet block."""
  filters: int
  strides: (int, int) = (1, 1)
  conv: ModuleDef
  norm: ModuleDef
  act: Callable

  @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
  strides: (int, int) = (1, 1)
  conv: ModuleDef
  norm: ModuleDef
  act: Callable

  @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: Sequence[int]
  num_classes: int
  num_filters: int = 64
  dtype: Any = jnp.float32
  act: Callable = nn.relu
  train: bool = True

  @nn.compact
  def __call__(self, x):
    conv = partial(nn.Conv, use_bias=False, dtype=self.dtype)
    norm = partial(nn.BatchNorm,
                   use_running_average=not self.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)
    x = nn.log_softmax(x)
    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)

@@ -836,39 +836,8 @@
 int%0A
-  strides: (int, int) = (1, 1)%0A
   co
@@ -875,32 +875,63 @@
 %0A  act: Callable
+%0A  strides: (int, int) = (1, 1)
 %0A%0A  @nn.compact%0A
@@ -1508,39 +1508,8 @@
 int%0A
-  strides: (int, int) = (1, 1)%0A
   co
@@ -1555,16 +1555,47 @@
 Callable
+%0A  strides: (int, int) = (1, 1)
 %0A%0A  @nn.

Fix linen imagenet.