import tensorflow as tf
from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block
from models.base import AEEncoder, Bottleneck, Decoder


class AutoEncoder(tf.keras.Model):
    
    def __init__(self, size):
        super(AutoEncoder, self).__init__()

        if size == 512:
            bottleneck_size = 64
            latent_dim = 128
            kernel_size = 7
            encoder_depths = [3, 3, 9, 9, 3]
            encoder_dims = [32, 64, 128, 256, bottleneck_size]
            decoder_input = (8, 4, bottleneck_size)
            decoder_depths = [3, 3, 9, 3, 3]
            decoder_dims = [bottleneck_size, 256, 128, 64, 16]
        elif size == 128:
            raise NotImplementedError
            bottleneck_size = 512
            latent_dim = 128
            kernel_size = 5
            encoder_depths =[16, 16, 9, 9]
            encoder_dims = [64, 128, 256, bottleneck_size]
            decoder_input = (4, 2, bottleneck_size)
            decoder_depths = [16, 16, 9, 9]
            decoder_dims = [bottleneck_size, 256, 128, 64]
        elif size == 32:
            raise NotImplementedError
            bottleneck_size = 512
            latent_dim = 128
            kernel_size = 3
            encoder_depths = [27, 27]
            encoder_dims = [256, bottleneck_size]
            decoder_input = (4, 2, bottleneck_size)
            decoder_depths = [27, 27]
            decoder_dims = [bottleneck_size, 256]
        else:
            raise NotImplementedError

        self.encoder : AEEncoder = AEEncoder(latent_dim, encoder_depths, encoder_dims, bottleneck_size, kernel_size)
        self.decoder = Decoder(decoder_input, decoder_depths, decoder_dims, kernel_size=kernel_size)


    def call(self, inputs, training=None, use_mask=True):
        if use_mask:
            x, mask = inputs
        else:
            x = inputs

        x = self.encoder(x, training=training)
        x = self.decoder(x, training=training)

        if use_mask:
            x = x * mask[tf.newaxis,:,:,tf.newaxis]

        return x



# class Bottleneck32(tf.keras.Model):
    
#     def __init__(self, dim, latent_dim):
#         super(Bottleneck32, self).__init__()

#         self.bottleneck = tf.keras.Sequential(
#                 [Block(dim=dim, drop_path=0,
#                        layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
#             )
#         self.flatten = tf.keras.layers.Flatten()
#         self.dense = tf.keras.layers.Dense(latent_dim)

#     def call(self, inputs, training=None):
#         x = inputs

#         x = self.bottleneck(x)
#         x = self.flatten(x)
#         x = self.dense(x)
        
#         return x


# class Decoder32(tf.keras.Model):
    
#     def __init__(self, bottleneck_size, latent_dim):
#         super(Decoder32, self).__init__()

#         self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
#         self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
#         self.decoder = ConvNeXtReverse(depths=list(reversed([27, 27])), dims=list(reversed([bottleneck_size, 256])))

#     def call(self, inputs, training=None):
#         x = inputs

#         x = self.dense(x)
#         x = self.reshape(x)
#         x = self.decoder(x)

#         return x


# class AutoEncoder32(tf.keras.Model):
    
#     def __init__(self):
#         super(AutoEncoder32, self).__init__()


#         bottleneck_size = 512
#         latent_dim = 128

#         self.encoder = ConvNeXt(depths=[27, 27], dims=[256, bottleneck_size], include_top=False)
#         self.bottleneck = Bottleneck32(bottleneck_size, latent_dim)
#         self.decoder = Decoder32(bottleneck_size, latent_dim)


#     def call(self, inputs, training=None):
#         x = inputs

#         x = tf.transpose(x, [0,2,3,1])
#         x = self.encoder(x, training=training)

#         x = self.bottleneck(x, training=training)

#         x = self.decoder(x, training=training)
#         # x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
#         x = tf.transpose(x, [0,3,1,2])

#         return x




# class Bottleneck128(tf.keras.Model):
    
#     def __init__(self, dim, latent_dim):
#         super(Bottleneck128, self).__init__()

#         self.bottleneck = tf.keras.Sequential(
#                 [Block(dim=dim, drop_path=0,
#                        layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
#             )
#         self.flatten = tf.keras.layers.Flatten()
#         self.dense = tf.keras.layers.Dense(latent_dim)

#     def call(self, inputs, training=None):
#         x = inputs

#         x = self.bottleneck(x)
#         x = self.flatten(x)
#         x = self.dense(x)
        
#         return x


# class Decoder128(tf.keras.Model):
    
#     def __init__(self, bottleneck_size, latent_dim):
#         super(Decoder128, self).__init__()

#         self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
#         self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
#         self.decoder = ConvNeXtReverse(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])))

#     def call(self, inputs, training=None):
#         x = inputs

#         x = self.dense(x)
#         x = self.reshape(x)
#         x = self.decoder(x)

#         return x


# class AutoEncoder128(tf.keras.Model):
    
#     def __init__(self):
#         super(AutoEncoder128, self).__init__()


#         bottleneck_size = 512
#         latent_dim = 128

#         self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
#         self.bottleneck = Bottleneck128(bottleneck_size, latent_dim)
#         self.decoder = Decoder128(bottleneck_size, latent_dim)


#     def call(self, inputs, training=None):
#         x = inputs

#         x = tf.transpose(x, [0,2,3,1])
#         x = self.encoder(x, training=training)

#         x = self.bottleneck(x, training=training)

#         x = self.decoder(x, training=training)
#         # x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
#         x = tf.transpose(x, [0,3,1,2])

#         return x