


"""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)
			SCREAMING_SNAKE_CASE :    int									=				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')

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

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

			SCREAMING_SNAKE_CASE :    List[str]									=				train_datagen.flow_from_directory(
			    """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
			)

			SCREAMING_SNAKE_CASE :    Union[str, 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

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








import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Tuple							)      ->List[Any]:
									return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCamelCase							) for s in shape]							)}.npy'''







			def 				snake_case__(     self :				Any							)      ->List[str]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				int=(4, 4, 6_4, 6_4)							,   _UpperCamelCase :				Optional[int]=False							)      ->Tuple:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return image







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				Optional[int]="CompVis/stable-diffusion-v1-4"							)      ->Optional[Any]:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	'''bf16''' if fpaa else None

									snake_case_,    snake_case_   =	FlaxUNetaDConditionModel.from_pretrained(
									    _UpperCamelCase							,   subfolder='''unet'''							,   dtype=_UpperCamelCase							,   revision=_UpperCamelCase							)
									return model, params







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[Any]=0							,   _UpperCamelCase :				Tuple=(4, 7_7, 7_6_8)							,   _UpperCamelCase :				List[Any]=False							)      ->int:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return hidden_states







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[int]							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->Dict:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   shape=(4, 4, 9_6, 9_6)							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   shape=(4, 7_7, 1_0_2_4)							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)