





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE     :   str					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   List[Any]					=    {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Dict											=       "open-llama"


	def __init__(self     ,_lowerCamelCase=100000     ,_lowerCamelCase=4096     ,_lowerCamelCase=11008     ,_lowerCamelCase=32     ,_lowerCamelCase=32     ,_lowerCamelCase="silu"     ,_lowerCamelCase=2048     ,_lowerCamelCase=0.0_2     ,_lowerCamelCase=1E-6     ,_lowerCamelCase=True     ,_lowerCamelCase=0     ,_lowerCamelCase=1     ,_lowerCamelCase=2     ,_lowerCamelCase=False     ,_lowerCamelCase=True     ,_lowerCamelCase=0.1     ,_lowerCamelCase=0.1     ,_lowerCamelCase=True     ,_lowerCamelCase=True     ,_lowerCamelCase=None     ,**_lowerCamelCase     ,)							->							int:







				'''simple docstring'''





				__lowercase										=							vocab_size
				__lowercase										=							max_position_embeddings
				__lowercase										=							hidden_size
				__lowercase										=							intermediate_size
				__lowercase										=							num_hidden_layers
				__lowercase										=							num_attention_heads
				__lowercase										=							hidden_act
				__lowercase										=							initializer_range
				__lowercase										=							rms_norm_eps
				__lowercase										=							use_cache
				__lowercase										=							kwargs.pop(
				    '''use_memorry_efficient_attention'''     ,_lowerCamelCase )
				__lowercase										=							hidden_dropout_prob
				__lowercase										=							attention_dropout_prob
				__lowercase										=							use_stable_embedding
				__lowercase										=							shared_input_output_embedding
				__lowercase										=							rope_scaling
				self._rope_scaling_validation()

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


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				if self.rope_scaling is None:
							return

				if not isinstance(self.rope_scaling     ,_lowerCamelCase ) or len(self.rope_scaling ) != 2:
							raise ValueError(
							    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
							    f"got {self.rope_scaling}" )
				__lowercase										=							self.rope_scaling.get('''type'''     ,_lowerCamelCase )
				__lowercase										=							self.rope_scaling.get('''factor'''     ,_lowerCamelCase )
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
							raise ValueError(
							    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
				if rope_scaling_factor is None or not isinstance(_lowerCamelCase     ,_lowerCamelCase ) or rope_scaling_factor <= 1.0:
							raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )












'''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'''





