'''simple docstring'''





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)
    SCREAMING_SNAKE_CASE_:					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(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
    )

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

    # Adding a second convolutional layer
    classifier.add(layers.ConvaD(32, (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_28, 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')

    SCREAMING_SNAKE_CASE_:					Optional[int]  =tf.keras.preprocessing.image.ImageDataGenerator(
        rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
    )

    SCREAMING_SNAKE_CASE_:					List[str]  =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

    SCREAMING_SNAKE_CASE_:					int  =train_datagen.flow_from_directory(
        'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
    )

    SCREAMING_SNAKE_CASE_:					int  =test_datagen.flow_from_directory(
        'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
    )

    classifier.fit_generator(
        training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
    )

    classifier.save('cnn.h5')

    # Part 3 - Making new predictions

    SCREAMING_SNAKE_CASE_:					int  =tf.keras.preprocessing.image.load_img(
        'dataset/single_prediction/image.png', target_size=(64, 64)
    )
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =tf.keras.preprocessing.image.img_to_array(test_image)
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =np.expand_dims(test_image, axis=0)
    SCREAMING_SNAKE_CASE_:					List[str]  =classifier.predict(test_image)
    # training_set.class_indices
    if result[0][0] == 0:
        SCREAMING_SNAKE_CASE_:					Dict  ='Normal'
    if result[0][0] == 1:
        SCREAMING_SNAKE_CASE_:					Dict  ='Abnormality detected'





'''simple docstring'''





import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
    import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class 	__A				(			unittest.TestCase		,     UpperCamelCase__     ):



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       load_tool("text-to-speech"			)
         self.tool.setup()



    def  _lowercase						(self   :   Union[str, Any]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)






    def  _lowercase						(self   :   List[str]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)




