




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)
				a    				=      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=128, 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')

				a    				=      tf.keras.preprocessing.image.ImageDataGenerator(
				    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
				)

				a    				=      tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

				a    				=      train_datagen.flow_from_directory(
				    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
				)

				a    				=      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

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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_							:					Optional[Any]		 =   {
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Optional[Any]		 =   [
			    '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Wav2Vec2ForAudioFrameClassification''',
			    '''Wav2Vec2ForCTC''',
			    '''Wav2Vec2ForMaskedLM''',
			    '''Wav2Vec2ForPreTraining''',
			    '''Wav2Vec2ForSequenceClassification''',
			    '''Wav2Vec2ForXVector''',
			    '''Wav2Vec2Model''',
			    '''Wav2Vec2PreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[Any]		 =   [
			    '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFWav2Vec2ForCTC''',
			    '''TFWav2Vec2Model''',
			    '''TFWav2Vec2PreTrainedModel''',
			    '''TFWav2Vec2ForSequenceClassification''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Dict		 =   [
			    '''FlaxWav2Vec2ForCTC''',
			    '''FlaxWav2Vec2ForPreTraining''',
			    '''FlaxWav2Vec2Model''',
			    '''FlaxWav2Vec2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase_							:					Optional[Any]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





