fix(root): Fixes usage of Optional[] type hint.

nalp/core/corpus.py

@@ -2,7 +2,7 @@
 """
 
 from collections import Counter
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import nalp.utils.constants as c
 import nalp.utils.preprocess as p
@@ -21,7 +21,7 @@ class Corpus:
 
     """
 
-    def __init__(self, min_frequency: Optional[int] = 1) -> None:
+    def __init__(self, min_frequency: int = 1) -> None:
         """Initialization method."""
 
         self.min_frequency = min_frequency

nalp/core/dataset.py

@@ -1,8 +1,6 @@
 """Dataset-related class.
 """
 
-from typing import Optional
-
 import tensorflow as tf
 
 import nalp.utils.constants as c
@@ -14,7 +12,7 @@ class Dataset:
 
     """
 
-    def __init__(self, shuffle: Optional[bool] = True) -> None:
+    def __init__(self, shuffle: bool = True) -> None:
         """Initialization method.
 
         Args:

nalp/core/model.py

@@ -1,7 +1,7 @@
 """Model-related classes.
 """
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import tensorflow as tf
 from tensorflow.keras import Model
@@ -20,7 +20,7 @@ class Discriminator(Model):
 
     """
 
-    def __init__(self, name: Optional[str] = "") -> None:
+    def __init__(self, name: str = "") -> None:
         """Initialization method.
 
         Note that basic variables shared by all childs should be declared here, e.g., layers.
@@ -32,7 +32,7 @@ def __init__(self, name: Optional[str] = "") -> None:
 
         super(Discriminator, self).__init__(name=name)
 
-    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> None:
+    def call(self, x: tf.Tensor, training: bool = True) -> None:
         """Method that holds vital information whenever this class is called.
 
         Note that you will need to implement this method directly on its child. Essentially,
@@ -56,7 +56,7 @@ class Generator(Model):
 
     """
 
-    def __init__(self, name: Optional[str] = "") -> None:
+    def __init__(self, name: str = "") -> None:
         """Initialization method.
 
         Note that basic variables shared by all childs should be declared here, e.g., layers.
@@ -68,7 +68,7 @@ def __init__(self, name: Optional[str] = "") -> None:
 
         super(Generator, self).__init__(name=name)
 
-    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> None:
+    def call(self, x: tf.Tensor, training: bool = True) -> None:
         """Method that holds vital information whenever this class is called.
 
         Note that you will need to implement this method directly on its child. Essentially,
@@ -86,7 +86,7 @@ def call(self, x: tf.Tensor, training: Optional[bool] = True) -> None:
         raise NotImplementedError
 
     def generate_greedy_search(
-        self, start: str, max_length: Optional[int] = 100
+        self, start: str, max_length: int = 100
     ) -> List[str]:
         """Generates text by using greedy search, where the sampled
         token is always sampled according to the maximum probability.
@@ -125,8 +125,8 @@ def generate_greedy_search(
     def generate_temperature_sampling(
         self,
         start: str,
-        max_length: Optional[int] = 100,
-        temperature: Optional[float] = 1.0,
+        max_length: int = 100,
+        temperature: float = 1.0,
     ) -> List[str]:
         """Generates text by using temperature sampling, where the sampled
         token is sampled according to a multinomial/categorical distribution.
@@ -168,9 +168,9 @@ def generate_temperature_sampling(
     def generate_top_sampling(
         self,
         start: str,
-        max_length: Optional[int] = 100,
-        k: Optional[int] = 0,
-        p: Optional[float] = 0.0,
+        max_length: int = 100,
+        k: int = 0,
+        p: float = 0.0,
     ) -> List[str]:
         """Generates text by using top-k and top-p sampling, where the sampled
         token is sampled according to the `k` most likely words distribution, as well
@@ -240,7 +240,7 @@ def __init__(
         self,
         discriminator: Discriminator,
         generator: Generator,
-        name: Optional[str] = "",
+        name: str = "",
     ) -> None:
         """Initialization method.
 
@@ -371,7 +371,7 @@ def step(self, x: tf.Tensor) -> None:
         self.G_loss.update_state(G_loss)
         self.D_loss.update_state(D_loss)
 
-    def fit(self, batches: Dataset, epochs: Optional[int] = 100) -> None:
+    def fit(self, batches: Dataset, epochs: int = 100) -> None:
         """Trains the model.
 
         Args:

nalp/corpus/audio.py

@@ -1,8 +1,6 @@
 """Audio-related corpus.
 """
 
-from typing import Optional
-
 from nalp.core import Corpus
 from nalp.utils import loader, logging
 
@@ -17,7 +15,7 @@ class AudioCorpus(Corpus):
 
     """
 
-    def __init__(self, from_file: str, min_frequency: Optional[int] = 1) -> None:
+    def __init__(self, from_file: str, min_frequency: int = 1) -> None:
         """Initialization method.
 
         Args:

nalp/corpus/sentence.py

@@ -24,10 +24,10 @@ def __init__(
         self,
         tokens: Optional[List[str]] = None,
         from_file: Optional[str] = None,
-        corpus_type: Optional[str] = "char",
-        min_frequency: Optional[int] = 1,
-        max_pad_length: Optional[int] = None,
-        sos_eos_tokens: Optional[bool] = True,
+        corpus_type: str = "char",
+        min_frequency: int = 1,
+        max_pad_length: int = None,
+        sos_eos_tokens: bool = True,
     ) -> None:
         """Initialization method.
 

nalp/corpus/text.py

@@ -21,8 +21,8 @@ def __init__(
         self,
         tokens: Optional[List[str]] = None,
         from_file: Optional[str] = None,
-        corpus_type: Optional[str] = "char",
-        min_frequency: Optional[int] = 1,
+        corpus_type: str = "char",
+        min_frequency: int = 1,
     ) -> None:
         """Initialization method.
 

nalp/datasets/image.py

@@ -21,10 +21,10 @@ class ImageDataset(Dataset):
     def __init__(
         self,
         images: np.array,
-        batch_size: Optional[int] = 256,
+        batch_size: int = 256,
         shape: Optional[Tuple[int, int]] = None,
-        normalize: Optional[bool] = True,
-        shuffle: Optional[bool] = True,
+        normalize: bool = True,
+        shuffle: bool = True,
     ) -> None:
         """Initialization method.
 

nalp/datasets/language_modeling.py

@@ -1,7 +1,7 @@
 """Language modeling dataset class.
 """
 
-from typing import Optional, Tuple
+from typing import Tuple
 
 import numpy as np
 import tensorflow as tf
@@ -21,9 +21,9 @@ class LanguageModelingDataset(Dataset):
     def __init__(
         self,
         encoded_tokens: np.array,
-        max_contiguous_pad_length: Optional[int] = 1,
-        batch_size: Optional[int] = 64,
-        shuffle: Optional[bool] = True,
+        max_contiguous_pad_length: int = 1,
+        batch_size: int = 64,
+        shuffle: bool = True,
     ) -> None:
         """Initialization method.
 

nalp/encoders/word2vec.py

@@ -2,7 +2,7 @@
 """
 
 import multiprocessing
-from typing import List, Optional
+from typing import List
 
 import numpy as np
 from gensim.models.word2vec import Word2Vec as W2V
@@ -31,12 +31,12 @@ def __init__(self) -> None:
     def learn(
         self,
         tokens: List[str],
-        max_features: Optional[int] = 128,
-        window_size: Optional[int] = 5,
-        min_count: Optional[int] = 1,
-        algorithm: Optional[bool] = 0,
-        learning_rate: Optional[float] = 0.01,
-        iterations: Optional[int] = 1000,
+        max_features: int = 128,
+        window_size: int = 5,
+        min_count: int = 1,
+        algorithm: int = 0,
+        learning_rate: float = 0.01,
+        iterations: int = 1000,
     ):
         """Learns a Word2Vec representation based on the its methodology.
 

nalp/models/dcgan.py

@@ -1,7 +1,7 @@
 """Deep Convolutional Generative Adversarial Network.
 """
 
-from typing import Optional, Tuple
+from typing import Tuple
 
 from nalp.core import Adversarial
 from nalp.models.discriminators import ConvDiscriminator
@@ -23,11 +23,11 @@ class DCGAN(Adversarial):
 
     def __init__(
         self,
-        input_shape: Optional[Tuple[int, int, int]] = (28, 28, 1),
-        noise_dim: Optional[int] = 100,
-        n_samplings: Optional[int] = 3,
-        alpha: Optional[float] = 0.3,
-        dropout_rate: Optional[float] = 0.3,
+        input_shape: Tuple[int, int, int] = (28, 28, 1),
+        noise_dim: int = 100,
+        n_samplings: int = 3,
+        alpha: float = 0.3,
+        dropout_rate: float = 0.3,
     ) -> None:
         """Initialization method.
 

nalp/models/discriminators/conv.py

@@ -1,8 +1,6 @@
 """Convolutional discriminator.
 """
 
-from typing import Optional
-
 import tensorflow as tf
 from tensorflow.keras.layers import Conv2D, Dense, Dropout
 
@@ -20,9 +18,9 @@ class ConvDiscriminator(Discriminator):
 
     def __init__(
         self,
-        n_samplings: Optional[int] = 3,
-        alpha: Optional[float] = 0.3,
-        dropout_rate: Optional[float] = 0.3,
+        n_samplings: int = 3,
+        alpha: float = 0.3,
+        dropout_rate: float = 0.3,
     ) -> None:
         """Initialization method.
 
@@ -64,7 +62,7 @@ def alpha(self) -> float:
     def alpha(self, alpha: float) -> None:
         self._alpha = alpha
 
-    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
+    def call(self, x: tf.Tensor, training: bool = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:

nalp/models/discriminators/embedded_text.py

@@ -1,7 +1,7 @@
 """Embedded-text discriminator.
 """
 
-from typing import Optional, Tuple
+from typing import Tuple
 
 import tensorflow as tf
 from tensorflow.keras.layers import Conv2D, Dense, Dropout, Embedding, MaxPool1D
@@ -20,12 +20,12 @@ class EmbeddedTextDiscriminator(Discriminator):
 
     def __init__(
         self,
-        vocab_size: Optional[int] = 1,
-        max_length: Optional[int] = 1,
-        embedding_size: Optional[int] = 32,
-        n_filters: Optional[Tuple[int, ...]] = (64),
-        filters_size: Optional[Tuple[int, ...]] = (1),
-        dropout_rate: Optional[float] = 0.25,
+        vocab_size: int = 1,
+        max_length: int = 1,
+        embedding_size: int = 32,
+        n_filters: Tuple[int, ...] = (64),
+        filters_size: Tuple[int, ...] = (1),
+        dropout_rate: float = 0.25,
     ) -> None:
         """Initialization method.
 
@@ -68,7 +68,7 @@ def __init__(
 
         logger.info("Class overrided.")
 
-    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
+    def call(self, x: tf.Tensor, training: bool = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:

nalp/models/discriminators/linear.py

@@ -1,8 +1,6 @@
 """Linear discriminator.
 """
 
-from typing import Optional
-
 import tensorflow as tf
 from tensorflow.keras.layers import Dense
 
@@ -19,7 +17,7 @@ class LinearDiscriminator(Discriminator):
     """
 
     def __init__(
-        self, n_samplings: Optional[int] = 3, alpha: Optional[float] = 0.01
+        self, n_samplings: int = 3, alpha: float = 0.01
     ) -> None:
         """Initialization method.
 
@@ -53,7 +51,7 @@ def alpha(self) -> float:
     def alpha(self, alpha: float) -> None:
         self._alpha = alpha
 
-    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
+    def call(self, x: tf.Tensor, training: bool = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:

nalp/models/discriminators/lstm.py

@@ -1,8 +1,6 @@
 """Long Short-Term Memory discriminator.
 """
 
-from typing import Optional
-
 import tensorflow as tf
 from tensorflow.keras.layers import RNN, Dense, LSTMCell
 
@@ -22,7 +20,7 @@ class LSTMDiscriminator(Discriminator):
     """
 
     def __init__(
-        self, embedding_size: Optional[int] = 32, hidden_size: Optional[int] = 64
+        self, embedding_size: int = 32, hidden_size: int = 64
     ) -> None:
         """Initialization method.