





'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Tuple											=       "convbert"


	def __init__(self     ,_lowerCamelCase=30522     ,_lowerCamelCase=768     ,_lowerCamelCase=12     ,_lowerCamelCase=12     ,_lowerCamelCase=3072     ,_lowerCamelCase="gelu"     ,_lowerCamelCase=0.1     ,_lowerCamelCase=0.1     ,_lowerCamelCase=512     ,_lowerCamelCase=2     ,_lowerCamelCase=0.0_2     ,_lowerCamelCase=1E-1_2     ,_lowerCamelCase=1     ,_lowerCamelCase=0     ,_lowerCamelCase=2     ,_lowerCamelCase=768     ,_lowerCamelCase=2     ,_lowerCamelCase=9     ,_lowerCamelCase=1     ,_lowerCamelCase=None     ,**_lowerCamelCase     ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(
				    pad_token_id=_lowerCamelCase     ,bos_token_id=_lowerCamelCase     ,eos_token_id=_lowerCamelCase     ,**_lowerCamelCase     ,)

				__lowercase										=							vocab_size
				__lowercase										=							hidden_size
				__lowercase										=							num_hidden_layers
				__lowercase										=							num_attention_heads
				__lowercase										=							intermediate_size
				__lowercase										=							hidden_act
				__lowercase										=							hidden_dropout_prob
				__lowercase										=							attention_probs_dropout_prob
				__lowercase										=							max_position_embeddings
				__lowercase										=							type_vocab_size
				__lowercase										=							initializer_range
				__lowercase										=							layer_norm_eps
				__lowercase										=							embedding_size
				__lowercase										=							head_ratio
				__lowercase										=							conv_kernel_size
				__lowercase										=							num_groups
				__lowercase										=							classifier_dropout






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	@property
	def 						_UpperCAmelCase     (self )							->							Mapping[str, Mapping[int, str]]:







				'''simple docstring'''





				if self.task == "multiple-choice":
							__lowercase										=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							__lowercase										=							{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				        ('''token_type_ids''', dynamic_axis),
				    ] )












'''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)
	_SCREAMING_SNAKE_CASE					=    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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='''relu''')
	)

	# Step 2 - Pooling
	classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

	# Adding a second convolutional layer
	classifier.add(layers.ConvaD(3_2, (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_2_8, 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')

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

	_SCREAMING_SNAKE_CASE					=    train_datagen.flow_from_directory(
	    '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	_SCREAMING_SNAKE_CASE					=    test_datagen.flow_from_directory(
	    '''dataset/test_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	classifier.fit_generator(
	    training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
	)

	classifier.save('''cnn.h5''')

	# Part 3 - Making new predictions

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.load_img(
	    '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
	)
	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.img_to_array(test_image)
	_SCREAMING_SNAKE_CASE					=    np.expand_dims(test_image, axis=0)
	_SCREAMING_SNAKE_CASE					=    classifier.predict(test_image)
	# training_set.class_indices
	if result[0][0] == 0:
		_SCREAMING_SNAKE_CASE					=    '''Normal'''
	if result[0][0] == 1:
		_SCREAMING_SNAKE_CASE					=    '''Abnormality detected'''





