





'''simple docstring'''





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments



@require_tf
class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Union[str, Any]:




              """simple docstring"""


              for model_result in results.values():
                     for batch_size, sequence_length in zip(model_result["bs"]					,   model_result["ss"]		):
                            UpperCamelCase_          =  model_result['result'][batch_size][sequence_length]
                            self.assertIsNotNone(UpperCAmelCase_		)

       def lowercase (		self: Tuple		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   eager_mode=UpperCAmelCase_					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

       def lowercase (		self: Optional[Any]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  'sgugger/tiny-distilbert-classification'
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   multi_process=UpperCAmelCase_					,   only_pretrain_model=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

       def lowercase (		self: Tuple		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

       def lowercase (		self: Optional[Any]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  AutoConfig.from_pretrained(UpperCAmelCase_		)
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   eager_mode=UpperCAmelCase_					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_					,   [config]		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

       def lowercase (		self: Optional[Any]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  AutoConfig.from_pretrained(UpperCAmelCase_		)
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_					,   [config]		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

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




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_train_result		)
              self.check_results_dict_not_empty(results.memory_train_result		)

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




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  AutoConfig.from_pretrained(UpperCAmelCase_		)
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_					,   [config]		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_train_result		)
              self.check_results_dict_not_empty(results.memory_train_result		)

       def lowercase (		self: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  'patrickvonplaten/t5-tiny-random'
              UpperCamelCase_          =  AutoConfig.from_pretrained(UpperCAmelCase_		)
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_					,   configs=[config]		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

       @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"		)		) == 0					,   "Cannot do xla on CPU."		)
       def lowercase (		self: int		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]					,   training=UpperCAmelCase_					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   use_xla=UpperCAmelCase_					,   multi_process=UpperCAmelCase_					,   )
              UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
              UpperCamelCase_          =  benchmark.run()
              self.check_results_dict_not_empty(results.time_inference_result		)
              self.check_results_dict_not_empty(results.memory_inference_result		)

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




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'
              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                         models=[MODEL_ID]					,   inference=UpperCAmelCase_					,   save_to_csv=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   inference_time_csv_file=os.path.join(UpperCAmelCase_					,   "inf_time.csv"		)					,   inference_memory_csv_file=os.path.join(UpperCAmelCase_					,   "inf_mem.csv"		)					,   env_info_csv_file=os.path.join(UpperCAmelCase_					,   "env.csv"		)					,   multi_process=UpperCAmelCase_					,   )
                     UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
                     benchmark.run()
                     self.assertTrue(Path(os.path.join(UpperCAmelCase_					,   "inf_time.csv"		)		).exists()		)
                     self.assertTrue(Path(os.path.join(UpperCAmelCase_					,   "inf_mem.csv"		)		).exists()		)
                     self.assertTrue(Path(os.path.join(UpperCAmelCase_					,   "env.csv"		)		).exists()		)



       def lowercase (		self: str		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  'sshleifer/tiny-gpt2'

              def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     self.assertTrue(hasattr(UpperCAmelCase_					,   "sequential"		)		)
                     self.assertTrue(hasattr(UpperCAmelCase_					,   "cumulative"		)		)
                     self.assertTrue(hasattr(UpperCAmelCase_					,   "current"		)		)
                     self.assertTrue(hasattr(UpperCAmelCase_					,   "total"		)		)

              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase_          =  TensorFlowBenchmarkArguments(
                         models=[MODEL_ID]					,   inference=UpperCAmelCase_					,   sequence_lengths=[8]					,   batch_sizes=[1]					,   log_filename=os.path.join(UpperCAmelCase_					,   "log.txt"		)					,   log_print=UpperCAmelCase_					,   trace_memory_line_by_line=UpperCAmelCase_					,   eager_mode=UpperCAmelCase_					,   multi_process=UpperCAmelCase_					,   )
                     UpperCamelCase_          =  TensorFlowBenchmark(UpperCAmelCase_		)
                     UpperCamelCase_          =  benchmark.run()
                     _check_summary_is_not_empty(result.inference_summary		)
                     self.assertTrue(Path(os.path.join(UpperCAmelCase_					,   "log.txt"		)		).exists()		)








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)
     _UpperCAmelCase        =				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')

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

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

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

     _UpperCAmelCase        =				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

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

