
'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase			:				Tuple    		=			logging.get_logger(__name__)

lowercase			:				Any    		=			{
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}





class  A				(							lowercase_						):
     __magic_name__						=		"""markuplm"""





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE=1024						,			SCREAMING_SNAKE_CASE=216						,			SCREAMING_SNAKE_CASE=1001						,			SCREAMING_SNAKE_CASE=32						,			SCREAMING_SNAKE_CASE=50						,			SCREAMING_SNAKE_CASE="absolute"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Any:






         """simple docstring"""






         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE						,			bos_token_id=SCREAMING_SNAKE_CASE						,			eos_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)
         A       :   Optional[Any]					=						vocab_size
         A       :   List[str]					=						hidden_size
         A       :   Dict					=						num_hidden_layers
         A       :   Tuple					=						num_attention_heads
         A       :   int					=						hidden_act
         A       :   Tuple					=						intermediate_size
         A       :   str					=						hidden_dropout_prob
         A       :   Union[str, Any]					=						attention_probs_dropout_prob
         A       :   Optional[int]					=						max_position_embeddings
         A       :   str					=						type_vocab_size
         A       :   int					=						initializer_range
         A       :   Dict					=						layer_norm_eps
         A       :   Dict					=						position_embedding_type
         A       :   Optional[int]					=						use_cache
         A       :   Optional[int]					=						classifier_dropout
         # additional properties
         A       :   Any					=						max_depth
         A       :   Union[str, Any]					=						max_xpath_tag_unit_embeddings
         A       :   str					=						max_xpath_subs_unit_embeddings
         A       :   int					=						tag_pad_id
         A       :   Union[str, Any]					=						subs_pad_id
         A       :   Union[str, Any]					=						xpath_unit_hidden_size








'''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'




