





'''simple docstring'''


import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCAmelCase_   :   List[Any]       =					[
    "EAGER",
    "AOT_EAGER",
    "INDUCTOR",
    "NVFUSER",
    "AOT_NVFUSER",
    "AOT_CUDAGRAPHS",
    "OFI",
    "FX2TRT",
    "ONNXRT",
    "IPEX",
]







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     List[Any]=None     )   ->    Any:

			'''simple docstring'''





			a__     = True
			while ask_again:
						a__     = input(__lowerCamelCase     )
						try:
									if default is not None and len(__lowerCamelCase     ) == 0:
												return default
									return convert_value(__lowerCamelCase     ) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]=[]					,  __lowerCamelCase  :     str=None					,  __lowerCamelCase  :     List[Any]=0     )   ->    List[Any]:

			'''simple docstring'''





			a__     = BulletMenu(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = menu.run(default_choice=__lowerCamelCase     )
			return convert_value(__lowerCamelCase     ) if convert_value is not None else result







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Optional[Any]:

			'''simple docstring'''





			a__     = int(__lowerCamelCase     )
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Optional[int]:

			'''simple docstring'''





			a__     = int(__lowerCamelCase     )
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Optional[Any]:

			'''simple docstring'''





			a__     = int(__lowerCamelCase     )
			return DynamoBackend(DYNAMO_BACKENDS[value]     ).value







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    str:

			'''simple docstring'''





			a__     = int(__lowerCamelCase     )
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    Optional[Any]:

			'''simple docstring'''





			a__     = int(__lowerCamelCase     )
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    List[Any]:

			'''simple docstring'''





			return {"yes": True, "no": False}[value.lower()]






class       UpperCamelCase__  (		argparse.RawDescriptionHelpFormatter     ):







	def 						__a   (				self			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = super()._format_usage(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				a__     = usage.replace("<command> [<args>] " , ""		)
				return usage










'''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)
		lowerCAmelCase_   :   str       =					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')

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

		lowerCAmelCase_   :   List[Any]       =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

		lowerCAmelCase_   :   Any       =					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

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



