






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)
	__UpperCAmelCase							:   Dict							=  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')

	__UpperCAmelCase							:   str							=  tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	__UpperCAmelCase							:   Tuple							=  tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

	__UpperCAmelCase							:   List[Any]							=  train_datagen.flow_from_directory(
	    "dataset/training_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
	)

	__UpperCAmelCase							:   Tuple							=  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

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







import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )