






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class  _lowercase   (   _A						):
      _a							:						Dict										=							'Wav2Vec2FeatureExtractor'
      _a							:						List[Any]										=							'AutoTokenizer'
      def __init__(     self				,			a				,			a   ):
       super().__init__(a				,			a   )
       snake_case__  :    int							       =self.feature_extractor
       snake_case__  :    str							       =False
      @classmethod
      def 						lowercase__					(     cls				,			a				,			**a   ):
       try:
        return super().from_pretrained(a				,			**a   )
       except OSError:
        warnings.warn(
            F"Loading a tokenizer inside {cls.__name__} from a config that does not"
            """ include a `tokenizer_class` attribute is deprecated and will be """
            """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
            """ attribute to either your `config.json` or `tokenizer_config.json` """
            """file to suppress this warning: """				,			a				,			)

        snake_case__  :    List[Any]							       =WavaVecaFeatureExtractor.from_pretrained(a				,			**a   )
        snake_case__  :    str							       =WavaVecaCTCTokenizer.from_pretrained(a				,			**a   )

        return cls(feature_extractor=a				,			tokenizer=a   )
      def __call__(     self				,			*a				,			**a   ):
       # For backward compatibility
       if self._in_target_context_manager:
        return self.current_processor(*a				,			**a   )

       if "raw_speech" in kwargs:
        warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""   )
        snake_case__  :    Dict							       =kwargs.pop("""raw_speech"""   )
       else:
        snake_case__  :    List[str]							       =kwargs.pop("""audio"""				,			a   )
       snake_case__  :    List[Any]							       =kwargs.pop("""sampling_rate"""				,			a   )
       snake_case__  :    Tuple							       =kwargs.pop("""text"""				,			a   )
       if len(a   ) > 0:
        snake_case__  :    Optional[int]							       =args[0]
        snake_case__  :    Optional[Any]							       =args[1:]

       if audio is None and text is None:
        raise ValueError("""You need to specify either an `audio` or `text` input to process."""   )

       if audio is not None:
        snake_case__  :    Optional[Any]							       =self.feature_extractor(a				,			*a				,			sampling_rate=a				,			**a   )
       if text is not None:
        snake_case__  :    Tuple							       =self.tokenizer(a				,			**a   )

       if text is None:
        return inputs
       elif audio is None:
        return encodings
       else:
        snake_case__  :    Any							       =encodings["""input_ids"""]
        return inputs
      def 						lowercase__					(     self				,			*a				,			**a   ):
       # For backward compatibility
       if self._in_target_context_manager:
        return self.current_processor.pad(*a				,			**a   )

       snake_case__  :    List[Any]							       =kwargs.pop("""input_features"""				,			a   )
       snake_case__  :    Optional[Any]							       =kwargs.pop("""labels"""				,			a   )
       if len(a   ) > 0:
        snake_case__  :    int							       =args[0]
        snake_case__  :    Optional[int]							       =args[1:]

       if input_features is not None:
        snake_case__  :    Optional[int]							       =self.feature_extractor.pad(a				,			*a				,			**a   )
       if labels is not None:
        snake_case__  :    Dict							       =self.tokenizer.pad(a				,			**a   )

       if labels is None:
        return input_features
       elif input_features is None:
        return labels
       else:
        snake_case__  :    str							       =labels["""input_ids"""]
        return input_features
      def 						lowercase__					(     self				,			*a				,			**a   ):
       return self.tokenizer.batch_decode(*a				,			**a   )
      def 						lowercase__					(     self				,			*a				,			**a   ):
       return self.tokenizer.decode(*a				,			**a   )



      @contextmanager
      def 						lowercase__					(     self   ):
       warnings.warn(
           """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
           """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
           """your audio inputs, or in a separate call."""   )
       snake_case__  :    str							       =True
       snake_case__  :    List[Any]							       =self.tokenizer
       yield
       snake_case__  :    Tuple							       =self.feature_extractor
       snake_case__  :    Any							       =False













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"""





