

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_							:					Optional[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=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')

			UpperCAmelCase_							:					Union[str, Any]		 =   tf.keras.preprocessing.image.ImageDataGenerator(
			    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
			)

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

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

			UpperCAmelCase_							:					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

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








import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





