


'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Tuple  					=							FunnelTokenizer
      lowerCAmelCase__					:     Tuple  					=							FunnelTokenizerFast
      lowerCAmelCase__					:     int  					=							True
      lowerCAmelCase__					:     Tuple  					=							True



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             super().setUp()

             lowercase__      			= [
                 '''<unk>''',
                 '''<cls>''',
                 '''<sep>''',
                 '''want''',
                 '''##want''',
                 '''##ed''',
                 '''wa''',
                 '''un''',
                 '''runn''',
                 '''##ing''',
                 ''',''',
                 '''low''',
                 '''lowest''',
             ]
             lowercase__      			= os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''vocab_file'''] )
             with open(self.vocab_file   ,						'''w'''   ,						encoding='''utf-8''' ) as vocab_writer:
                    vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )



      def       UpperCamelCase__		(self    :    int   ,						**UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







             return FunnelTokenizer.from_pretrained(self.tmpdirname   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int]   ,						**UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             return FunnelTokenizerFast.from_pretrained(self.tmpdirname   ,						**UpperCamelCase )



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

             '''simple docstring'''







             lowercase__      			= '''UNwant\u00E9d,running'''
             lowercase__      			= '''unwanted, running'''
             return input_text, output_text



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.tokenizer_class(self.vocab_file )

             lowercase__      			= tokenizer.tokenize('''UNwant\u00E9d,running''' )
             self.assertListEqual(UpperCamelCase   ,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase )   ,						[7, 4, 5, 10, 8, 9] )






      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             lowercase__      			= self.get_tokenizers(do_lower_case=UpperCamelCase )
             for tokenizer in tokenizers:
                    lowercase__      			= tokenizer('''UNwant\u00E9d,running''' )
                    lowercase__      			= len(inputs['''input_ids'''] ) - 1
                    self.assertListEqual(inputs['''token_type_ids''']   ,						[2] + [0] * sentence_len )

                    lowercase__      			= tokenizer('''UNwant\u00E9d,running'''   ,						'''UNwant\u00E9d,running''' )
                    self.assertListEqual(inputs['''token_type_ids''']   ,						[2] + [0] * sentence_len + [1] * sentence_len )








'''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'




