

"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  # FIXME: add fast tests
  pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   __lowercase      (     unittest.TestCase       ):

  @property
  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       ort.SessionOptions()
   UpperCAmelCase__       :	int        =       False
   return options

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""     )
   UpperCAmelCase__       :	Union[str, Any]        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""     )
   UpperCAmelCase__       :	int        =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
       """runwayml/stable-diffusion-inpainting"""       ,revision="""onnx"""       ,safety_checker=A       ,feature_extractor=A       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
   pipe.set_progress_bar_config(disable=A     )

   UpperCAmelCase__       :	Optional[int]        =       """A red cat sitting on a park bench"""

   UpperCAmelCase__       :	str        =       np.random.RandomState(0     )
   UpperCAmelCase__       :	Dict        =       pipe(
       prompt=A       ,image=A       ,mask_image=A       ,guidance_scale=7.5       ,num_inference_steps=10       ,generator=A       ,output_type="""np"""       ,)
   UpperCAmelCase__       :	Any        =       output.images
   UpperCAmelCase__       :	List[str]        =       images[0, 255:258, 255:258, -1]

   assert images.shape == (1, 512, 512, 3)
   UpperCAmelCase__       :	str        =       np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]     )

   assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3




  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""     )
   UpperCAmelCase__       :	Optional[Any]        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""     )
   UpperCAmelCase__       :	List[str]        =       LMSDiscreteScheduler.from_pretrained(
       """runwayml/stable-diffusion-inpainting"""       ,subfolder="""scheduler"""       ,revision="""onnx"""     )
   UpperCAmelCase__       :	Optional[Any]        =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
       """runwayml/stable-diffusion-inpainting"""       ,revision="""onnx"""       ,scheduler=A       ,safety_checker=A       ,feature_extractor=A       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
   pipe.set_progress_bar_config(disable=A     )

   UpperCAmelCase__       :	str        =       """A red cat sitting on a park bench"""

   UpperCAmelCase__       :	Tuple        =       np.random.RandomState(0     )
   UpperCAmelCase__       :	Dict        =       pipe(
       prompt=A       ,image=A       ,mask_image=A       ,guidance_scale=7.5       ,num_inference_steps=20       ,generator=A       ,output_type="""np"""       ,)
   UpperCAmelCase__       :	Dict        =       output.images
   UpperCAmelCase__       :	Optional[Any]        =       images[0, 255:258, 255:258, -1]

   assert images.shape == (1, 512, 512, 3)
   UpperCAmelCase__       :	Tuple        =       np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]     )

   assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3













'''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)
      UpperCamelCase      					=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')

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

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

      UpperCamelCase      					=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

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






