


'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Any  					=							["""image_processor""", """tokenizer"""]
      lowerCAmelCase__					:     Union[str, Any]  					=							"""BlipImageProcessor"""
      lowerCAmelCase__					:     Optional[int]  					=							"""AutoTokenizer"""



      def __init__(self    :    List[Any]   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    int ):

             '''simple docstring'''







             lowercase__      			= False
             super().__init__(UpperCamelCase   ,						UpperCamelCase )
             lowercase__      			= self.image_processor



      def __call__(self    :    int   ,						UpperCamelCase    :    ImageInput = None   ,						UpperCamelCase    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Union[bool, str, PaddingStrategy] = False   ,						UpperCamelCase    :    Union[bool, str, TruncationStrategy] = None   ,						UpperCamelCase    :    Optional[int] = None   ,						UpperCamelCase    :    int = 0   ,						UpperCamelCase    :    Optional[int] = None   ,						UpperCamelCase    :    Optional[bool] = None   ,						UpperCamelCase    :    bool = False   ,						UpperCamelCase    :    bool = False   ,						UpperCamelCase    :    bool = False   ,						UpperCamelCase    :    bool = False   ,						UpperCamelCase    :    bool = False   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Optional[Union[str, TensorType]] = None   ,						**UpperCamelCase    :    Tuple   ,						):

             '''simple docstring'''







             if images is None and text is None:
                    raise ValueError('''You have to specify either images or text.''' )

             # Get only text
             if images is None:
                    lowercase__      			= self.tokenizer
                    lowercase__      			= self.tokenizer(
                        text=UpperCamelCase   ,						add_special_tokens=UpperCamelCase   ,						padding=UpperCamelCase   ,						truncation=UpperCamelCase   ,						max_length=UpperCamelCase   ,						stride=UpperCamelCase   ,						pad_to_multiple_of=UpperCamelCase   ,						return_attention_mask=UpperCamelCase   ,						return_overflowing_tokens=UpperCamelCase   ,						return_special_tokens_mask=UpperCamelCase   ,						return_offsets_mapping=UpperCamelCase   ,						return_token_type_ids=UpperCamelCase   ,						return_length=UpperCamelCase   ,						verbose=UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase   ,						)
                    return text_encoding

             # add pixel_values
             lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase )

             if text is not None:
                    lowercase__      			= self.tokenizer(
                        text=UpperCamelCase   ,						add_special_tokens=UpperCamelCase   ,						padding=UpperCamelCase   ,						truncation=UpperCamelCase   ,						max_length=UpperCamelCase   ,						stride=UpperCamelCase   ,						pad_to_multiple_of=UpperCamelCase   ,						return_attention_mask=UpperCamelCase   ,						return_overflowing_tokens=UpperCamelCase   ,						return_special_tokens_mask=UpperCamelCase   ,						return_offsets_mapping=UpperCamelCase   ,						return_token_type_ids=UpperCamelCase   ,						return_length=UpperCamelCase   ,						verbose=UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase   ,						)
             else:
                    lowercase__      			= None

             if text_encoding is not None:
                    encoding_image_processor.update(UpperCamelCase )

             return encoding_image_processor



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						*UpperCamelCase    :    Dict   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             return self.tokenizer.batch_decode(*UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    str   ,						*UpperCamelCase    :    Dict   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             return self.tokenizer.decode(*UpperCamelCase   ,						**UpperCamelCase )






      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.tokenizer.model_input_names
             lowercase__      			= self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )








'''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)
 lowerCamelCase							:   Any  						=	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=128, 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							:   Optional[Any]  						=	tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowerCamelCase							:   Any  						=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 lowerCamelCase							:   List[Any]  						=	train_datagen.flow_from_directory(
     'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

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




