






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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		List[Any]										=   {
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}







class  _lowercase   (   _A						):
      _a							:						Any										=							'xglm'
      _a							:						Optional[Any]										=							['past_key_values']

      _a							:						Dict										=							{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(     self				,			a=2_5_6_0_0_8				,			a=2_0_4_8				,			a=1_0_2_4				,			a=4_0_9_6				,			a=2_4				,			a=1_6				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=0.0				,			a=0.0				,			a=0.02				,			a=True				,			a=True				,			a=2				,			a=1				,			a=0				,			a=2				,			**a				,			):
       snake_case__  :    Tuple							       =vocab_size
       snake_case__  :    int							       =max_position_embeddings
       snake_case__  :    Optional[int]							       =d_model
       snake_case__  :    Tuple							       =ffn_dim
       snake_case__  :    int							       =num_layers
       snake_case__  :    Dict							       =attention_heads
       snake_case__  :    Optional[int]							       =activation_function
       snake_case__  :    List[str]							       =dropout
       snake_case__  :    int							       =attention_dropout
       snake_case__  :    Optional[int]							       =activation_dropout
       snake_case__  :    Any							       =layerdrop
       snake_case__  :    int							       =init_std
       snake_case__  :    Union[str, Any]							       =scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case__  :    Dict							       =use_cache

       super().__init__(
           pad_token_id=a				,			bos_token_id=a				,			eos_token_id=a				,			decoder_start_token_id=a				,			**a				,			)













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

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

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





