





'''simple docstring'''



import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				Optional[Any]											=       OpenAIGPTTokenizer
	a    :				int											=       OpenAIGPTTokenizerFast
	a    :				Tuple											=       True
	a    :				Any											=       False


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				__lowercase										=							[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''w</w>''',
				    '''r</w>''',
				    '''t</w>''',
				    '''lo''',
				    '''low''',
				    '''er</w>''',
				    '''low</w>''',
				    '''lowest</w>''',
				    '''newer</w>''',
				    '''wider</w>''',
				    '''<unk>''',
				]
				__lowercase										=							dict(zip(_lowerCamelCase     ,range(len(_lowerCamelCase ) ) ) )
				__lowercase										=							['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

				__lowercase										=							os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['''vocab_file'''] )
				__lowercase										=							os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['''merges_file'''] )
				with open(self.vocab_file     ,'''w''' ) as fp:
							fp.write(json.dumps(_lowerCamelCase ) )
				with open(self.merges_file     ,'''w''' ) as fp:
							fp.write('''\n'''.join(_lowerCamelCase ) )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				return "lower newer", "lower newer"


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							OpenAIGPTTokenizer(self.vocab_file     ,self.merges_file )

				__lowercase										=							'''lower'''
				__lowercase										=							['''low''', '''er</w>''']
				__lowercase										=							tokenizer.tokenize(_lowerCamelCase )
				self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							tokens + ['''<unk>''']
				__lowercase										=							[14, 15, 20]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=15 )							->							str:







				'''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(_lowerCamelCase     ,**_lowerCamelCase )

										# 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
										self.assertRaises(_lowerCamelCase     ,tokenizer_r.encode     ,_lowerCamelCase     ,max_length=_lowerCamelCase     ,padding='''max_length''' )

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

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

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

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

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


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				pass






@require_ftfy
@require_spacy
@require_tokenizers
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	pass












'''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)
	_SCREAMING_SNAKE_CASE					=    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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='''relu''')
	)

	# Step 2 - Pooling
	classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

	# Adding a second convolutional layer
	classifier.add(layers.ConvaD(3_2, (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_2_8, 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')

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

	_SCREAMING_SNAKE_CASE					=    train_datagen.flow_from_directory(
	    '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	_SCREAMING_SNAKE_CASE					=    test_datagen.flow_from_directory(
	    '''dataset/test_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	classifier.fit_generator(
	    training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
	)

	classifier.save('''cnn.h5''')

	# Part 3 - Making new predictions

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.load_img(
	    '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
	)
	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.img_to_array(test_image)
	_SCREAMING_SNAKE_CASE					=    np.expand_dims(test_image, axis=0)
	_SCREAMING_SNAKE_CASE					=    classifier.predict(test_image)
	# training_set.class_indices
	if result[0][0] == 0:
		_SCREAMING_SNAKE_CASE					=    '''Normal'''
	if result[0][0] == 1:
		_SCREAMING_SNAKE_CASE					=    '''Abnormality detected'''





