
'''simple docstring'''




import unittest

from transformers import DonutProcessor


lowercase			:				Tuple    		=			'naver-clova-ix/donut-base'





class  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Dict					=						DonutProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Any					=						{
             '''name''': '''John Doe''',
             '''age''': '''99''',
             '''city''': '''Atlanta''',
             '''state''': '''GA''',
             '''zip''': '''30301''',
             '''phone''': '''123-4567''',
             '''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
         }

         A       :   Optional[Any]					=						(
             '''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
             '''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
             '''<s_nicknames><s_nickname>Johnny</s_nickname>'''
             '''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
         )
         A       :   str					=						self.processor.tokenajson(SCREAMING_SNAKE_CASE	)

         self.assertDictEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)








'''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							:   Any  						=	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')

 lowerCamelCase							:   Optional[Any]  						=	tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowerCamelCase							:   Any  						=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 lowerCamelCase							:   List[Any]  						=	train_datagen.flow_from_directory(
     'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

 lowerCamelCase							:   List[str]  						=	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							:   Optional[int]  						=	tf.keras.preprocessing.image.img_to_array(test_image)
 lowerCamelCase							:   str  						=	np.expand_dims(test_image, axis=0)
 lowerCamelCase							:   List[str]  						=	classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  lowerCamelCase							:   Any  						=	'Normal'
 if result[0][0] == 1:
  lowerCamelCase							:   Any  						=	'Abnormality detected'




