
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case      :							Tuple     	=    {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							List[Any]     	=    [
						    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TapasForMaskedLM",
						    "TapasForQuestionAnswering",
						    "TapasForSequenceClassification",
						    "TapasModel",
						    "TapasPreTrainedModel",
						    "load_tf_weights_in_tapas",
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFTapasForMaskedLM",
						    "TFTapasForQuestionAnswering",
						    "TFTapasForSequenceClassification",
						    "TFTapasModel",
						    "TFTapasPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
						from .tokenization_tapas import TapasTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tapas import (
												    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TapasForMaskedLM,
												    TapasForQuestionAnswering,
												    TapasForSequenceClassification,
												    TapasModel,
												    TapasPreTrainedModel,
												    load_tf_weights_in_tapas,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_tapas import (
												    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTapasForMaskedLM,
												    TFTapasForQuestionAnswering,
												    TFTapasForSequenceClassification,
												    TFTapasModel,
												    TFTapasPreTrainedModel,
												)


else:
						import sys

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


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)
						_snake_case      :							Tuple     	=    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')

						_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
						)

						_snake_case      :							Any     	=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

						_snake_case      :							Dict     	=    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

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