





'''simple docstring'''



import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ImageGPTImageProcessor






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase=7     ,_lowerCamelCase=3     ,_lowerCamelCase=18     ,_lowerCamelCase=30     ,_lowerCamelCase=400     ,_lowerCamelCase=True     ,_lowerCamelCase=None     ,_lowerCamelCase=True     ,)							->							Dict:







				'''simple docstring'''





				__lowercase										=							size if size is not None else {'''height''': 18, '''width''': 18}
				__lowercase										=							parent
				__lowercase										=							batch_size
				__lowercase										=							num_channels
				__lowercase										=							image_size
				__lowercase										=							min_resolution
				__lowercase										=							max_resolution
				__lowercase										=							do_resize
				__lowercase										=							size
				__lowercase										=							do_normalize


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
				            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
				        ] ),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}






@require_torch
@require_vision
class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				Any											=       ImageGPTImageProcessor if is_vision_available() else None


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							ImageGPTImageProcessingTester(self )


	@property
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''clusters''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_resize''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''size''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_normalize''' ) )


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







				'''simple docstring'''





				__lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size     ,{'''height''': 18, '''width''': 18} )

				__lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict     ,size=42 )
				self.assertEqual(image_processor.size     ,{'''height''': 42, '''width''': 42} )


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				__lowercase										=							json.loads(image_processor.to_json_string() )
				for key, value in self.image_processor_dict.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(_lowerCamelCase     ,obj[key] ) )
							else:
										self.assertEqual(obj[key]     ,_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowercase										=							os.path.join(_lowerCamelCase     ,'''image_processor.json''' )
							image_processor_first.to_json_file(_lowerCamelCase )
							__lowercase										=							self.image_processing_class.from_json_file(_lowerCamelCase ).to_dict()

				__lowercase										=							image_processor_first.to_dict()
				for key, value in image_processor_first.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(_lowerCamelCase     ,image_processor_second[key] ) )
							else:
										self.assertEqual(image_processor_first[key]     ,_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )

				with tempfile.TemporaryDirectory() as tmpdirname:
							image_processor_first.save_pretrained(_lowerCamelCase )
							__lowercase										=							self.image_processing_class.from_pretrained(_lowerCamelCase ).to_dict()

				__lowercase										=							image_processor_first.to_dict()
				for key, value in image_processor_first.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(_lowerCamelCase     ,image_processor_second[key] ) )
							else:
										self.assertEqual(image_processor_first[key]     ,_lowerCamelCase )


	@unittest.skip('''ImageGPT requires clusters at initialization''' )
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				pass





def    _lowerCAmelCase						(				):
			__lowercase										=							load_dataset('''hf-internal-testing/fixtures_image_utils'''		,       split='''test'''				)

			__lowercase										=							Image.open(dataset[4]['''file''']				)
			__lowercase										=							Image.open(dataset[5]['''file''']				)

			__lowercase										=							[imagea, imagea]

			return images






@require_vision
@require_torch
class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

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







				'''simple docstring'''





				__lowercase										=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''' )

				__lowercase										=							prepare_images()

				# test non-batched
				__lowercase										=							image_processing(images[0]     ,return_tensors='''pt''' )

				self.assertIsInstance(encoding.input_ids     ,torch.LongTensor )
				self.assertEqual(encoding.input_ids.shape     ,(1, 1024) )

				__lowercase										=							[306, 191, 191]
				self.assertEqual(encoding.input_ids[0, :3].tolist()     ,_lowerCamelCase )

				# test batched
				__lowercase										=							image_processing(_lowerCamelCase     ,return_tensors='''pt''' )

				self.assertIsInstance(encoding.input_ids     ,torch.LongTensor )
				self.assertEqual(encoding.input_ids.shape     ,(2, 1024) )

				__lowercase										=							[303, 13, 13]
				self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,_lowerCamelCase )












'''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'''





