






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


__lowerCamelCase	:		List[str]										=   logging.get_logger(__name__)

__lowerCamelCase	:		Optional[Any]										=   {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							'dpr'
      def __init__(     self				,			a=3_0_5_2_2				,			a=7_6_8				,			a=1_2				,			a=1_2				,			a=3_0_7_2				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=2				,			a=0.02				,			a=1e-12				,			a=0				,			a="absolute"				,			a = 0				,			**a				,			):
       super().__init__(pad_token_id=a				,			**a   )

       snake_case__  :    Dict							       =vocab_size
       snake_case__  :    Union[str, Any]							       =hidden_size
       snake_case__  :    Dict							       =num_hidden_layers
       snake_case__  :    List[str]							       =num_attention_heads
       snake_case__  :    Any							       =hidden_act
       snake_case__  :    Union[str, Any]							       =intermediate_size
       snake_case__  :    Tuple							       =hidden_dropout_prob
       snake_case__  :    Union[str, Any]							       =attention_probs_dropout_prob
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Any							       =type_vocab_size
       snake_case__  :    Optional[int]							       =initializer_range
       snake_case__  :    int							       =layer_norm_eps
       snake_case__  :    List[str]							       =projection_dim
       snake_case__  :    Tuple							       =position_embedding_type













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"""





