






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__lowerCamelCase	:		Any										=   logging.get_logger(__name__)







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





 '''simple docstring'''







 snake_case__  :    List[Any]							       =b.T
 snake_case__  :    Tuple							       =np.sum(np.square(UpperCamelCase__	)		,  axis=1	)
 snake_case__  :    Optional[int]							       =np.sum(np.square(UpperCamelCase__	)		,  axis=0	)
 snake_case__  :    List[Any]							       =np.matmul(UpperCamelCase__		,  UpperCamelCase__	)
 snake_case__  :    List[Any]							       =aa[:, None] - 2 * ab + ba[None, :]
 return d







def 			A__							(      _a  :					Any		,  _a  :					Union[str, Any]	):





 '''simple docstring'''







 snake_case__  :    str							       =x.reshape(-1		,  3	)
 snake_case__  :    int							       =squared_euclidean_distance(UpperCamelCase__		,  UpperCamelCase__	)
 return np.argmin(UpperCamelCase__		,  axis=1	)







class  _lowercase   (			lowercase_						):
      _a							:						Optional[Any]										=							['''pixel_values''']
      def __init__(     self				,			a = None				,			a = True				,			a = None				,			a = PILImageResampling.BILINEAR				,			a = True				,			a = True				,			**a				,			):
       super().__init__(**a   )
       snake_case__  :    List[Any]							       =size if size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
       snake_case__  :    Any							       =get_size_dict(a   )
       snake_case__  :    Optional[Any]							       =np.array(a   ) if clusters is not None else None
       snake_case__  :    List[str]							       =do_resize
       snake_case__  :    Tuple							       =size
       snake_case__  :    Tuple							       =resample
       snake_case__  :    Dict							       =do_normalize
       snake_case__  :    Union[str, Any]							       =do_color_quantize
      def 						lowercase__					(     self				,			a				,			a				,			a = PILImageResampling.BILINEAR				,			a = None				,			**a				,			):
       snake_case__  :    Optional[int]							       =get_size_dict(a   )
       if "height" not in size or "width" not in size:
        raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}"   )
       return resize(
           a				,			size=(size["""height"""], size["""width"""])				,			resample=a				,			data_format=a				,			**a   )
      def 						lowercase__					(     self				,			a				,			a = None				,			):
       snake_case__  :    Optional[int]							       =rescale(image=a				,			scale=1 / 1_2_7.5				,			data_format=a   )
       snake_case__  :    List[Any]							       =image - 1
       return image



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Tuple							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    Optional[int]							       =size if size is not None else self.size
       snake_case__  :    int							       =get_size_dict(a   )
       snake_case__  :    Dict							       =resample if resample is not None else self.resample
       snake_case__  :    Dict							       =do_normalize if do_normalize is not None else self.do_normalize
       snake_case__  :    Any							       =do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       snake_case__  :    int							       =clusters if clusters is not None else self.clusters
       snake_case__  :    Union[str, Any]							       =np.array(a   )

       snake_case__  :    Dict							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError(
            """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
            """torch.Tensor, tf.Tensor or jax.ndarray."""   )

       if do_resize and size is None or resample is None:
        raise ValueError("""Size and resample must be specified if do_resize is True."""   )

       if do_color_quantize and clusters is None:
        raise ValueError("""Clusters must be specified if do_color_quantize is True."""   )

       # All transformations expect numpy arrays.
       snake_case__  :    Union[str, Any]							       =[to_numpy_array(a   ) for image in images]

       if do_resize:
        snake_case__  :    List[Any]							       =[self.resize(image=a				,			size=a				,			resample=a   ) for image in images]

       if do_normalize:
        snake_case__  :    Any							       =[self.normalize(image=a   ) for image in images]

       if do_color_quantize:
        snake_case__  :    Dict							       =[to_channel_dimension_format(a				,			ChannelDimension.LAST   ) for image in images]
        # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
        snake_case__  :    int							       =np.array(a   )
        snake_case__  :    str							       =color_quantize(a				,			a   ).reshape(images.shape[:-1]   )

        # flatten to (batch_size, height*width)
        snake_case__  :    str							       =images.shape[0]
        snake_case__  :    int							       =images.reshape(a				,			-1   )

        # We need to convert back to a list of images to keep consistent behaviour across processors.
        snake_case__  :    Union[str, Any]							       =list(a   )
       else:
        snake_case__  :    Optional[Any]							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Union[str, Any]							       ={"""input_ids""": images}
       return BatchFeature(data=a				,			tensor_type=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"""





