



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__       =      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')

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

						SCREAMING_SNAKE_CASE__       =      tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

						SCREAMING_SNAKE_CASE__       =      train_datagen.flow_from_directory(
						    '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
						)

						SCREAMING_SNAKE_CASE__       =      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

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









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs



					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				,					reason='XFormers attention is only available with CUDA and `xformers` installed'				,					)
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								super().test_save_load_floataa(expected_max_diff=1E-1					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,					)




