






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class  _lowercase   :
      def __init__(     self				,			a				,			a=sys.maxsize   ):
       snake_case__  :    int							       ="""bilinear"""
       snake_case__  :    Optional[Any]							       =max_size
       snake_case__  :    List[str]							       =short_edge_length



      def __call__(     self				,			a   ):
       snake_case__  :    List[str]							       =[]
       for img in imgs:
        snake_case__       ,   snake_case__  :    Any							       =img.shape[:2]
        # later: provide list and randomly choose index for resize
        snake_case__  :    Any							       =np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1   )
        if size == 0:
         return img
        snake_case__  :    Optional[int]							       =size * 1.0 / min(a				,			a   )
        if h < w:
         snake_case__       ,   snake_case__  :    int							       =size, scale * w
        else:
         snake_case__       ,   snake_case__  :    List[str]							       =scale * h, size
        if max(a				,			a   ) > self.max_size:
         snake_case__  :    int							       =self.max_size * 1.0 / max(a				,			a   )
         snake_case__  :    Any							       =newh * scale
         snake_case__  :    List[str]							       =neww * scale
        snake_case__  :    int							       =int(neww + 0.5   )
        snake_case__  :    Any							       =int(newh + 0.5   )

        if img.dtype == np.uinta:
         snake_case__  :    List[str]							       =Image.fromarray(a   )
         snake_case__  :    List[Any]							       =pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR   )
         snake_case__  :    List[str]							       =np.asarray(a   )
        else:
         snake_case__  :    Tuple							       =img.permute(2				,			0				,			1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
         snake_case__  :    str							       =nn.functional.interpolate(
             a				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=a   ).squeeze(0   )
        img_augs.append(a   )

       return img_augs







class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Tuple							       =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST   )
       snake_case__  :    str							       =cfg.INPUT.FORMAT
       snake_case__  :    Optional[Any]							       =cfg.SIZE_DIVISIBILITY
       snake_case__  :    Tuple							       =cfg.PAD_VALUE
       snake_case__  :    Any							       =cfg.INPUT.MAX_SIZE_TEST
       snake_case__  :    Union[str, Any]							       =cfg.MODEL.DEVICE
       snake_case__  :    Any							       =torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,			1				,			1   )
       snake_case__  :    List[Any]							       =torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,			1				,			1   )
       snake_case__  :    Optional[int]							       =lambda a   : (x - self.pixel_mean) / self.pixel_std
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    int							       =tuple(max(a   ) for s in zip(*[img.shape for img in images]   )   )
       snake_case__  :    Any							       =[im.shape[-2:] for im in images]
       snake_case__  :    List[Any]							       =[
           nn.functional.pad(
               a				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
           for size, im in zip(a				,			a   )
       ]

       return torch.stack(a   ), torch.tensor(a   )



      def __call__(     self				,			a				,			a=False   ):
       with torch.no_grad():
        if not isinstance(a				,			a   ):
         snake_case__  :    str							       =[images]
        if single_image:
         assert len(a   ) == 1
        for i in range(len(a   )   ):
         if isinstance(images[i]				,			torch.Tensor   ):
          images.insert(a				,			images.pop(a   ).to(self.device   ).float()   )
         elif not isinstance(images[i]				,			torch.Tensor   ):
          images.insert(
              a				,			torch.as_tensor(img_tensorize(images.pop(a   )				,			input_format=self.input_format   )   )
              .to(self.device   )
              .float()				,			)
            # resize smallest edge
        snake_case__  :    Union[str, Any]							       =torch.tensor([im.shape[:2] for im in images]   )
        snake_case__  :    str							       =self.aug(a   )
        # transpose images and convert to torch tensors
        # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
        # now normalize before pad to avoid useless arithmetic
        snake_case__  :    Optional[int]							       =[self.normalizer(a   ) for x in images]
        # now pad them to do the following operations
        snake_case__       ,   snake_case__  :    Tuple							       =self.pad(a   )
        # Normalize

        if self.size_divisibility > 0:
         raise NotImplementedError()
        # pad
        snake_case__  :    Optional[int]							       =torch.true_divide(a				,			a   )
        if single_image:
         return images[0], sizes[0], scales_yx[0]
        else:
         return images, sizes, scales_yx







def 			A__							(      _a  :					Optional[Any]		,  _a  :					List[str]	):





 '''simple docstring'''







 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes







def 			A__							(      _a  :					List[str]		,  _a  :					Tuple[int, int]	):





 '''simple docstring'''







 assert torch.isfinite(_a	).all(), "Box tensor contains infinite or NaN!"
 snake_case__       ,   snake_case__  :    str							       =box_size
 tensor[:, 0].clamp_(min=0		,  max=_a	)
 tensor[:, 1].clamp_(min=0		,  max=_a	)
 tensor[:, 2].clamp_(min=0		,  max=_a	)
 tensor[:, 3].clamp_(min=0		,  max=_a	)













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"""





