






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class  _lowercase   (			unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =1
       snake_case__  :    int							       =3
       snake_case__  :    Dict							       =(3_2, 3_2)

       snake_case__  :    Tuple							       =floats_tensor((batch_size, num_channels) + sizes				,			rng=random.Random(0   )   ).to(a   )
       return image
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Dict							       =UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)				,			layers_per_block=2				,			sample_size=3_2				,			in_channels=4				,			out_channels=4				,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,			cross_attention_dim=3_2				,			)
       return model
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    List[str]							       =AutoencoderKL(
           block_out_channels=[3_2, 6_4]				,			in_channels=3				,			out_channels=3				,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,			latent_channels=4				,			)
       return model
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    List[Any]							       =RobertaSeriesConfig(
           hidden_size=3_2				,			project_dim=3_2				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=5_0_0_6				,			)
       return RobertaSeriesModelWithTransformation(a   )
      @property
      def 						lowercase__					(     self   ):
       def extract(*a				,			**a   ):







        class  _lowercase   :
              def __init__(     self   ):
               snake_case__  :    Tuple							       =torch.ones([0]   )



              def 						lowercase__					(     self				,			a   ):
               self.pixel_values.to(a   )
               return self




        return Out()

       return extract
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
       snake_case__  :    Any							       =self.dummy_cond_unet
       snake_case__  :    Tuple							       =PNDMScheduler(skip_prk_steps=a   )
       snake_case__  :    Optional[int]							       =self.dummy_vae
       snake_case__  :    Dict							       =self.dummy_text_encoder
       snake_case__  :    int							       =XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
       snake_case__  :    Any							       =7_7

       snake_case__  :    List[str]							       =self.dummy_image.to(a   )
       snake_case__  :    List[str]							       =init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       snake_case__  :    str							       =AltDiffusionImgaImgPipeline(
           unet=a				,			scheduler=a				,			vae=a				,			text_encoder=a				,			tokenizer=a				,			safety_checker=a				,			feature_extractor=self.dummy_extractor				,			)
       snake_case__  :    Optional[Any]							       =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,			do_normalize=a   )
       snake_case__  :    List[str]							       =alt_pipe.to(a   )
       alt_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[Any]							       ="""A painting of a squirrel eating a burger"""
       snake_case__  :    Tuple							       =torch.Generator(device=a   ).manual_seed(0   )
       snake_case__  :    str							       =alt_pipe(
           [prompt]				,			generator=a				,			guidance_scale=6.0				,			num_inference_steps=2				,			output_type="""np"""				,			image=a				,			)

       snake_case__  :    Tuple							       =output.images

       snake_case__  :    str							       =torch.Generator(device=a   ).manual_seed(0   )
       snake_case__  :    List[str]							       =alt_pipe(
           [prompt]				,			generator=a				,			guidance_scale=6.0				,			num_inference_steps=2				,			output_type="""np"""				,			image=a				,			return_dict=a				,			)[0]

       snake_case__  :    Tuple							       =image[0, -3:, -3:, -1]
       snake_case__  :    str							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 3_2, 3_2, 3)
       snake_case__  :    str							       =np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5e-3
      @unittest.skipIf(torch_device != """cuda"""				,			"""This test requires a GPU"""   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.dummy_cond_unet
       snake_case__  :    List[Any]							       =PNDMScheduler(skip_prk_steps=a   )
       snake_case__  :    Optional[int]							       =self.dummy_vae
       snake_case__  :    Union[str, Any]							       =self.dummy_text_encoder
       snake_case__  :    int							       =XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
       snake_case__  :    Union[str, Any]							       =7_7

       snake_case__  :    Dict							       =self.dummy_image.to(a   )

       # put models in fp16
       snake_case__  :    str							       =unet.half()
       snake_case__  :    int							       =vae.half()
       snake_case__  :    Tuple							       =bert.half()

       # make sure here that pndm scheduler skips prk
       snake_case__  :    Optional[int]							       =AltDiffusionImgaImgPipeline(
           unet=a				,			scheduler=a				,			vae=a				,			text_encoder=a				,			tokenizer=a				,			safety_checker=a				,			feature_extractor=self.dummy_extractor				,			)
       snake_case__  :    Optional[int]							       =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,			do_normalize=a   )
       snake_case__  :    Tuple							       =alt_pipe.to(a   )
       alt_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    str							       ="""A painting of a squirrel eating a burger"""
       snake_case__  :    List[str]							       =torch.manual_seed(0   )
       snake_case__  :    Any							       =alt_pipe(
           [prompt]				,			generator=a				,			num_inference_steps=2				,			output_type="""np"""				,			image=a				,			).images

       assert image.shape == (1, 3_2, 3_2, 3)



      @unittest.skipIf(torch_device != """cuda"""				,			"""This test requires a GPU"""   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/img2img/sketch-mountains-input.jpg"""   )
       # resize to resolution that is divisible by 8 but not 16 or 32
       snake_case__  :    Tuple							       =init_image.resize((7_6_0, 5_0_4)   )

       snake_case__  :    Any							       ="""BAAI/AltDiffusion"""
       snake_case__  :    Optional[int]							       =AltDiffusionImgaImgPipeline.from_pretrained(
           a				,			safety_checker=a				,			)
       pipe.to(a   )
       pipe.set_progress_bar_config(disable=a   )
       pipe.enable_attention_slicing()

       snake_case__  :    Tuple							       ="""A fantasy landscape, trending on artstation"""

       snake_case__  :    Optional[Any]							       =torch.manual_seed(0   )
       snake_case__  :    Any							       =pipe(
           prompt=a				,			image=a				,			strength=0.75				,			guidance_scale=7.5				,			generator=a				,			output_type="""np"""				,			)
       snake_case__  :    Optional[int]							       =output.images[0]

       snake_case__  :    Union[str, Any]							       =image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

       assert image.shape == (5_0_4, 7_6_0, 3)
       snake_case__  :    Optional[int]							       =np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







@slow
@require_torch_gpu
class  _lowercase   (			unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/img2img/sketch-mountains-input.jpg"""   )
       snake_case__  :    Optional[int]							       =init_image.resize((7_6_8, 5_1_2)   )
       snake_case__  :    Dict							       =load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""   )

       snake_case__  :    List[Any]							       ="""BAAI/AltDiffusion"""
       snake_case__  :    str							       =AltDiffusionImgaImgPipeline.from_pretrained(
           a				,			safety_checker=a				,			)
       pipe.to(a   )
       pipe.set_progress_bar_config(disable=a   )
       pipe.enable_attention_slicing()

       snake_case__  :    Tuple							       ="""A fantasy landscape, trending on artstation"""

       snake_case__  :    Optional[int]							       =torch.manual_seed(0   )
       snake_case__  :    str							       =pipe(
           prompt=a				,			image=a				,			strength=0.75				,			guidance_scale=7.5				,			generator=a				,			output_type="""np"""				,			)
       snake_case__  :    int							       =output.images[0]

       assert image.shape == (5_1_2, 7_6_8, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image   ).max() < 1e-2













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=1_28, 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	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

       __lowerCamelCase	:		str										=   train_datagen.flow_from_directory(
           """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
       )

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





