





'''simple docstring'''



import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers import AutoModelForCausalLM






@require_torch
class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

				__lowercase										=							ids_tensor((1, 5)     ,vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
				__lowercase										=							model.generate(_lowerCamelCase     ,max_new_tokens=10     ,do_sample=_lowerCamelCase )
				__lowercase										=							tokenizer.decode(greedy_ids[0] )

				with CaptureStdout() as cs:
							__lowercase										=							TextStreamer(_lowerCamelCase )
							model.generate(_lowerCamelCase     ,max_new_tokens=10     ,do_sample=_lowerCamelCase     ,streamer=_lowerCamelCase )
				# The greedy text should be printed to stdout, except for the final "\n" in the streamer
				__lowercase										=							cs.out[:-1]

				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

				__lowercase										=							ids_tensor((1, 5)     ,vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
				__lowercase										=							model.generate(_lowerCamelCase     ,max_new_tokens=10     ,do_sample=_lowerCamelCase )
				__lowercase										=							tokenizer.decode(greedy_ids[0] )

				__lowercase										=							TextIteratorStreamer(_lowerCamelCase )
				__lowercase										=							{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
				__lowercase										=							Thread(target=model.generate     ,kwargs=_lowerCamelCase )
				thread.start()
				__lowercase										=							''''''
				for new_text in streamer:
							streamer_text += new_text

				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

				__lowercase										=							ids_tensor((1, 5)     ,vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
				__lowercase										=							model.generate(_lowerCamelCase     ,max_new_tokens=10     ,do_sample=_lowerCamelCase )
				__lowercase										=							greedy_ids[:, input_ids.shape[1] :]
				__lowercase										=							tokenizer.decode(new_greedy_ids[0] )

				with CaptureStdout() as cs:
							__lowercase										=							TextStreamer(_lowerCamelCase     ,skip_prompt=_lowerCamelCase )
							model.generate(_lowerCamelCase     ,max_new_tokens=10     ,do_sample=_lowerCamelCase     ,streamer=_lowerCamelCase )
				# The greedy text should be printed to stdout, except for the final "\n" in the streamer
				__lowercase										=							cs.out[:-1]

				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''distilgpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

				__lowercase										=							torch.ones((1, 5)     ,device=_lowerCamelCase ).long() * model.config.bos_token_id
				with CaptureStdout() as cs:
							__lowercase										=							TextStreamer(_lowerCamelCase     ,skip_special_tokens=_lowerCamelCase )
							model.generate(_lowerCamelCase     ,max_new_tokens=1     ,do_sample=_lowerCamelCase     ,streamer=_lowerCamelCase )

				# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
				# re-tokenized, must only contain one token
				__lowercase										=							cs.out[:-1]  # Remove the final "\n"
				__lowercase										=							tokenizer(_lowerCamelCase     ,return_tensors='''pt''' )
				self.assertEqual(streamer_text_tokenized.input_ids.shape     ,(1, 1) )


	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
				__lowercase										=							AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
				__lowercase										=							-1

				__lowercase										=							ids_tensor((1, 5)     ,vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
				__lowercase										=							TextIteratorStreamer(_lowerCamelCase     ,timeout=0.0_0_1 )
				__lowercase										=							{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
				__lowercase										=							Thread(target=model.generate     ,kwargs=_lowerCamelCase )
				thread.start()

				# The streamer will timeout after 0.001 seconds, so an exception will be raised
				with self.assertRaises(_lowerCamelCase ):
							__lowercase										=							''''''
							for new_text in streamer:
										streamer_text += new_text












'''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'''





