






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCAmelCase_ :						str      =      transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]					)		->			Union[str, Any]:






	"""simple docstring"""



	if isinstance(__magic_name__	,			torch.Tensor					):
		return image
	elif isinstance(__magic_name__	,			PIL.Image.Image					):
		UpperCamelCase     :Union[str, Any]          =	[image]

	UpperCamelCase     :Tuple          =	[trans(img.convert("""RGB"""					)					) for img in image]
	UpperCamelCase     :int          =	torch.stack(__magic_name__					)
	return image

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Any ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Any	):
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		UpperCamelCase     :List[Any]          =	DDIMScheduler.from_config(scheduler.config	)

		self.register_modules(unet=__lowerCamelCase ,  scheduler=__lowerCamelCase	)





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str]	):
		if strength < 0 or strength > 1:
			raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					List[Any]	):
		# get the original timestep using init_timestep
		UpperCamelCase     :Optional[Any]          =	min(int(num_inference_steps * strength	) ,  __lowerCamelCase	)

		UpperCamelCase     :Dict          =	max(num_inference_steps - init_timestep ,  0	)
		UpperCamelCase     :str          =	self.scheduler.timesteps[t_start:]

		return timesteps, num_inference_steps - t_start





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Optional[int]=None	):
		if not isinstance(__lowerCamelCase ,  (torch.Tensor, PIL.Image.Image, list)	):
			raise ValueError(
			    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCamelCase	)}"""	)

		UpperCamelCase     :Tuple          =	image.to(device=__lowerCamelCase ,  dtype=__lowerCamelCase	)

		if isinstance(__lowerCamelCase ,  __lowerCamelCase	) and len(__lowerCamelCase	) != batch_size:
			raise ValueError(
			    F"""You have passed a list of generators of length {len(__lowerCamelCase	)}, but requested an effective batch"""
			    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""	)

		UpperCamelCase     :Tuple          =	init_latents.shape
		UpperCamelCase     :List[str]          =	randn_tensor(__lowerCamelCase ,  generator=__lowerCamelCase ,  device=__lowerCamelCase ,  dtype=__lowerCamelCase	)

		# get latents
		print("""add noise to latents at timestep""" ,  __lowerCamelCase	)
		UpperCamelCase     :List[Any]          =	self.scheduler.add_noise(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Tuple          =	init_latents

		return latents





	@torch.no_grad()
	def __call__(		self				:					Tuple ,  __lowerCamelCase				:					Union[torch.FloatTensor, PIL.Image.Image] = None ,  __lowerCamelCase				:					float = 0.8 ,  __lowerCamelCase				:					int = 1 ,  __lowerCamelCase				:					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  __lowerCamelCase				:					float = 0.0 ,  __lowerCamelCase				:					int = 50 ,  __lowerCamelCase				:					Optional[bool] = None ,  __lowerCamelCase				:					Optional[str] = "pil" ,  __lowerCamelCase				:					bool = True ,  ):
		self.check_inputs(__lowerCamelCase	)

		# 2. Preprocess image
		UpperCamelCase     :Any          =	preprocess(__lowerCamelCase	)

		# 3. set timesteps
		self.scheduler.set_timesteps(__lowerCamelCase ,  device=self.device	)
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.get_timesteps(__lowerCamelCase ,  __lowerCamelCase ,  self.device	)
		UpperCamelCase     :List[Any]          =	timesteps[:1].repeat(__lowerCamelCase	)

		# 4. Prepare latent variables
		UpperCamelCase     :str          =	self.prepare_latents(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  self.unet.dtype ,  self.device ,  __lowerCamelCase	)
		UpperCamelCase     :List[str]          =	latents

		# 5. Denoising loop
		for t in self.progress_bar(__lowerCamelCase	):
			# 1. predict noise model_output
			UpperCamelCase     :List[str]          =	self.unet(__lowerCamelCase ,  __lowerCamelCase	).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			UpperCamelCase     :Union[str, Any]          =	self.scheduler.step(
			    __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  eta=__lowerCamelCase ,  use_clipped_model_output=__lowerCamelCase ,  generator=__lowerCamelCase ,  ).prev_sample

		UpperCamelCase     :Dict          =	(image / 2 + 0.5).clamp(0 ,  1	)
		UpperCamelCase     :Optional[Any]          =	image.cpu().permute(0 ,  2 ,  3 ,  1	).numpy()
		if output_type == "pil":
			UpperCamelCase     :List[str]          =	self.numpy_to_pil(__lowerCamelCase	)

		if not return_dict:
			return (image, latent_timestep.item())

		return ImagePipelineOutput(images=__lowerCamelCase	)




'''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							:   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')

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

 lowerCamelCase							:   Any  						=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 lowerCamelCase							:   List[Any]  						=	train_datagen.flow_from_directory(
     'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

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




