
"""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)
							lowerCAmelCase								=		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								=		tf.keras.preprocessing.image.ImageDataGenerator(
							    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
							)

							lowerCAmelCase								=		tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

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







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowercase_					(     __A			:							dict       ,   __A			:							str       ,   __A			:							set       ,   __A			:							set       ,   __A			:							dict       ,   __A			:							dict       ,   __A			:							PriorityQueue       ,   __A			:							dict       ,   __A			:							float | int       ,   )							->   float | int:



 """simple docstring"""




 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowercase       : str						=cst_fwd.get(__A       ,   np.inf      )
  lowercase       : str						=cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)      )
   lowercase       : int						=new_cost_f
   lowercase       : Optional[int]						=v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    lowercase       : List[Any]						=cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance


def 				lowercase_					(     __A			:							str       ,   __A			:							str       ,   __A			:							dict       ,   __A			:							dict      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=-1

 lowercase       : Tuple						=set()
 lowercase       : str						=set()
 lowercase       : List[str]						={source: 0}
 lowercase       : List[str]						={destination: 0}
 lowercase       : Tuple						={source: None}
 lowercase       : List[Any]						={destination: None}
 lowercase       : PriorityQueue[Any]						=PriorityQueue()
 lowercase       : PriorityQueue[Any]						=PriorityQueue()

 lowercase       : Any						=np.inf

 queue_forward.put((0, source)      )
 queue_backward.put((0, destination)      )

 if source == destination:
  return 0

 while not queue_forward.empty() and not queue_backward.empty():
  lowercase	,		lowercase       : int						=queue_forward.get()
  visited_forward.add(__A      )

  lowercase	,		lowercase       : int						=queue_backward.get()
  visited_backward.add(__A      )

  lowercase       : Dict						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  lowercase       : List[Any]						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
   break

 if shortest_distance != np.inf:
  lowercase       : Union[str, Any]						=shortest_distance
 return shortest_path_distance


SCREAMING_SNAKE_CASE							=						{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE							=						{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()



