




import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
  # Initialising the CNN
  # (Sequential- Building the model layer by layer)
  _lowerCAmelCase						:   Union[str, Any]		=   models.Sequential()

  # Step 1 - Convolution
  # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
  # (3,3) is the kernel size (filter matrix)
  classifier.add(
      layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation="relu")
  )

  # Step 2 - Pooling
  classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

  # Adding a second convolutional layer
  classifier.add(layers.ConvaD(3_2, (3, 3), activation="relu"))
  classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

  # Step 3 - Flattening
  classifier.add(layers.Flatten())

  # Step 4 - Full connection
  classifier.add(layers.Dense(units=1_2_8, activation="relu"))
  classifier.add(layers.Dense(units=1, activation="sigmoid"))

  # Compiling the CNN
  classifier.compile(
      optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
  )

  # Part 2 - Fitting the CNN to the images

  # Load Trained model weights

  # from keras.models import load_model
  # regressor=load_model('cnn.h5')

  _lowerCAmelCase						:   Optional[int]		=   tf.keras.preprocessing.image.ImageDataGenerator(
      rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
  )

  _lowerCAmelCase						:   List[str]		=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

  _lowerCAmelCase						:   Tuple		=   train_datagen.flow_from_directory(
      "dataset/training_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
  )

  _lowerCAmelCase						:   int		=   test_datagen.flow_from_directory(
      "dataset/test_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
  )

  classifier.fit_generator(
      training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
  )

  classifier.save("cnn.h5")

  # Part 3 - Making new predictions

  _lowerCAmelCase						:   Any		=   tf.keras.preprocessing.image.load_img(
      "dataset/single_prediction/image.png", target_size=(6_4, 6_4)
  )
  _lowerCAmelCase						:   List[Any]		=   tf.keras.preprocessing.image.img_to_array(test_image)
  _lowerCAmelCase						:   Union[str, Any]		=   np.expand_dims(test_image, axis=0)
  _lowerCAmelCase						:   List[str]		=   classifier.predict(test_image)
  # training_set.class_indices
  if result[0][0] == 0:
    _lowerCAmelCase						:   List[Any]		=   "Normal"
  if result[0][0] == 1:
    _lowerCAmelCase						:   Optional[Any]		=   "Abnormality detected"











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





