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



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

#         self.dense = tf.keras.layers.Dense(16 * 8 * bottleneck_size)
#         self.reshape = tf.keras.layers.Reshape((16, 8, bottleneck_size))
#         self.decoder = ConvNeXtReverseLabels(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([bottleneck_size, 128, 64, 16])), nr_of_classes=7)

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

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

#         return x

        
class AutoEncoder(tf.keras.Model):
    
    def __init__(self, size=512):
        super(AutoEncoder, self).__init__()

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


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

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

        # x = x * uv_mask[:,:, tf.newaxis]
        # x = x * uv_mask[:,tf.newaxis,:,:]
        x = x * uv_mask[:,:,:,tf.newaxis]

        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 = ConvNeXtReverseLabels(depths=list(reversed([27, 27])), dims=list(reversed([bottleneck_size, 256])), nr_of_classes=7)

#     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 = Bottleneck(bottleneck_size, latent_dim)
#         self.decoder = Decoder32(bottleneck_size, latent_dim)


#     def call(self, inputs, training=None):
#         x, uv_mask = 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.transpose(x, [0,3,1,2])

#         # x = x * uv_mask[:,:, tf.newaxis]
#         # x = x * uv_mask[:,tf.newaxis,:,:]
#         # x = x * uv_mask[:,:,:,tf.newaxis]

#         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 = ConvNeXtReverseLabels(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])), nr_of_classes=7)

#     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 = Bottleneck(bottleneck_size, latent_dim)
#         self.decoder = Decoder128(bottleneck_size, latent_dim)


#     def call(self, inputs, training=None):
#         x, uv_mask = 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.transpose(x, [0,3,1,2])

#         # x = x * uv_mask[:,:, tf.newaxis]
#         # x = x * uv_mask[:,tf.newaxis,:,:]
#         # x = x * uv_mask[:,:,:,tf.newaxis]

#         return x