


'''simple docstring'''

import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

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





      '''simple docstring'''






      lowerCAmelCase__					:     str  					=							None
      lowerCAmelCase__					:     Optional[int]  					=							BloomTokenizerFast
      lowerCAmelCase__					:     Tuple  					=							BloomTokenizerFast
      lowerCAmelCase__					:     Dict  					=							True
      lowerCAmelCase__					:     List[str]  					=							False
      lowerCAmelCase__					:     Union[str, Any]  					=							"""tokenizer_file"""
      lowerCAmelCase__					:     Dict  					=							{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             super().setUp()
             lowercase__      			= BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''' )
             tokenizer.save_pretrained(self.tmpdirname )



      def       UpperCamelCase__		(self    :    str   ,						**UpperCamelCase    :    int ):

             '''simple docstring'''







             kwargs.update(self.special_tokens_map )
             return BloomTokenizerFast.from_pretrained(self.tmpdirname   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.get_rust_tokenizer()

             lowercase__      			= ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
             lowercase__      			= [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

             lowercase__      			= tokenizer.batch_encode_plus(UpperCamelCase )['''input_ids''']
             self.assertListEqual(UpperCamelCase   ,						UpperCamelCase )

             lowercase__      			= tokenizer.batch_decode(UpperCamelCase )
             self.assertListEqual(UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any]=6 ):

             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                           lowercase__      			= self.rust_tokenizer_class.from_pretrained(UpperCamelCase   ,						**UpperCamelCase )
                           # tokenizer_r.pad_token = None # Hotfixing padding = None
                           # Simple input
                           lowercase__      			= '''This is a simple input'''
                           lowercase__      			= ['''This is a simple input 1''', '''This is a simple input 2''']
                           lowercase__      			= ('''This is a simple input''', '''This is a pair''')
                           lowercase__      			= [
                               ('''This is a simple input 1''', '''This is a simple input 2'''),
                               ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                           ]

                           # Simple input tests
                           try:
                                  tokenizer_r.encode(UpperCamelCase   ,						max_length=UpperCamelCase )
                                  tokenizer_r.encode_plus(UpperCamelCase   ,						max_length=UpperCamelCase )

                                  tokenizer_r.batch_encode_plus(UpperCamelCase   ,						max_length=UpperCamelCase )
                                  tokenizer_r.encode(UpperCamelCase   ,						max_length=UpperCamelCase )
                                  tokenizer_r.batch_encode_plus(UpperCamelCase   ,						max_length=UpperCamelCase )
                           except ValueError:
                                  self.fail('''Bloom Tokenizer should be able to deal with padding''' )

                           lowercase__      			= None  # Hotfixing padding = None
                           self.assertRaises(UpperCamelCase   ,						tokenizer_r.encode   ,						UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding='''max_length''' )

                           # Simple input
                           self.assertRaises(UpperCamelCase   ,						tokenizer_r.encode_plus   ,						UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding='''max_length''' )

                           # Simple input
                           self.assertRaises(
                               UpperCamelCase   ,						tokenizer_r.batch_encode_plus   ,						UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding='''max_length'''   ,						)

                           # Pair input
                           self.assertRaises(UpperCamelCase   ,						tokenizer_r.encode   ,						UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding='''max_length''' )

                           # Pair input
                           self.assertRaises(UpperCamelCase   ,						tokenizer_r.encode_plus   ,						UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding='''max_length''' )

                           # Pair input
                           self.assertRaises(
                               UpperCamelCase   ,						tokenizer_r.batch_encode_plus   ,						UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding='''max_length'''   ,						)



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.get_rust_tokenizer()
             lowercase__      			= load_dataset('''xnli'''   ,						'''all_languages'''   ,						split='''test'''   ,						streaming=UpperCamelCase )

             lowercase__      			= next(iter(UpperCamelCase ) )['''premise''']  # pick up one data
             lowercase__      			= list(sample_data.values() )

             lowercase__      			= list(map(tokenizer.encode   ,						UpperCamelCase ) )
             lowercase__      			= [tokenizer.decode(UpperCamelCase   ,						clean_up_tokenization_spaces=UpperCamelCase ) for x in output_tokens]
             self.assertListEqual(UpperCamelCase   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map )   ,						1 )
             self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] )   ,						1 )








'''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'




