





'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			str								=						(UnCLIPScheduler,)







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




				'''simple docstring'''
				a__     = {
				    "num_train_timesteps": 1_0_0_0,
				    "variance_type": "fixed_small_log",
				    "clip_sample": True,
				    "clip_sample_range": 1.0,
				    "prediction_type": "epsilon",
				}

				config.update(**lowerCamelCase		)
				return config







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				for variance in ["fixed_small_log", "learned_range"]:
							self.check_over_configs(variance_type=lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowerCamelCase		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				for clip_sample_range in [1, 5, 1_0, 2_0]:
							self.check_over_configs(clip_sample_range=lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				for prediction_type in ["epsilon", "sample"]:
							self.check_over_configs(prediction_type=lowerCamelCase		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				for time_step in [0, 5_0_0, 9_9_9]:
							for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
										if prev_timestep is not None and prev_timestep >= time_step:
													continue

										self.check_over_forward(time_step=lowerCamelCase , prev_timestep=lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config(variance_type="fixed_small_log"		)
				a__     = scheduler_class(**lowerCamelCase		)

				assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000e-10		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0549625		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.9994987		)		) < 1e-5







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config(variance_type="learned_range"		)
				a__     = scheduler_class(**lowerCamelCase		)

				a__     = 0.5

				assert scheduler._get_variance(1 , predicted_variance=lowerCamelCase		) - -10.1712790 < 1e-5
				assert scheduler._get_variance(4_8_7 , predicted_variance=lowerCamelCase		) - -5.7998052 < 1e-5
				assert scheduler._get_variance(9_9_9 , predicted_variance=lowerCamelCase		) - -0.0010011 < 1e-5







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config()
				a__     = scheduler_class(**lowerCamelCase		)

				a__     = scheduler.timesteps

				a__     = self.dummy_model()
				a__     = self.dummy_sample_deter
				a__     = torch.manual_seed(0		)

				for i, t in enumerate(lowerCamelCase		):
							# 1. predict noise residual
							a__     = model(lowerCamelCase , lowerCamelCase		)

							# 2. predict previous mean of sample x_t-1
							a__     = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , generator=lowerCamelCase		).prev_sample

							a__     = pred_prev_sample

				a__     = torch.sum(torch.abs(lowerCamelCase		)		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_sum.item() - 252.2682495		) < 1e-2
				assert abs(result_mean.item() - 0.3284743		) < 1e-3







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config()
				a__     = scheduler_class(**lowerCamelCase		)

				scheduler.set_timesteps(2_5		)

				a__     = scheduler.timesteps

				a__     = self.dummy_model()
				a__     = self.dummy_sample_deter
				a__     = torch.manual_seed(0		)

				for i, t in enumerate(lowerCamelCase		):
							# 1. predict noise residual
							a__     = model(lowerCamelCase , lowerCamelCase		)

							if i + 1 == timesteps.shape[0]:
										a__     = None
							else:
										a__     = timesteps[i + 1]

							# 2. predict previous mean of sample x_t-1
							a__     = scheduler.step(
							    lowerCamelCase , lowerCamelCase , lowerCamelCase , prev_timestep=lowerCamelCase , generator=lowerCamelCase		).prev_sample

							a__     = pred_prev_sample

				a__     = torch.sum(torch.abs(lowerCamelCase		)		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_sum.item() - 258.2044983		) < 1e-2
				assert abs(result_mean.item() - 0.3362038		) < 1e-3







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				pass










'''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"



