





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Tuple		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  0

       def lowercase (		self: Dict		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"		)
              self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "preprocessor_config.json"
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "config.json"
                     json.dump(
                         {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)					,   )
                     json.dump({"model_type": "clip"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)		)

                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Tuple		)       -> Dict:




              """simple docstring"""


              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "preprocessor_config.json"
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "config.json"
                     json.dump(
                         {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)					,   )
                     json.dump({"model_type": "clip"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)		)

                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase_          =  CLIPConfig()

                     # Create a dummy config file with image_proceesor_type
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "preprocessor_config.json"
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "config.json"
                     json.dump(
                         {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)					,   )
                     json.dump({"model_type": "clip"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)		)

                     # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		).to_dict()

                     config_dict.pop("image_processor_type"		)
                     UpperCamelCase_          =  CLIPImageProcessor(**_SCREAMING_SNAKE_CASE		)

                     # save in new folder
                     model_config.save_pretrained(_SCREAMING_SNAKE_CASE		)
                     config.save_pretrained(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		)

                     # make sure private variable is not incorrectly saved
                     UpperCamelCase_          =  json.loads(config.to_json_string()		)
                     self.assertTrue("_processor_class" not in dict_as_saved		)

              self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "preprocessor_config.json"
                     json.dump(
                         {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)					,   )

                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              with self.assertRaisesRegex(
                  _SCREAMING_SNAKE_CASE					,   "clip-base is not a local folder and is not a valid model identifier"		):
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained("clip-base"		)

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




              """simple docstring"""


              with self.assertRaisesRegex(
                  _SCREAMING_SNAKE_CASE					,   R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE					,   revision="aaaaaa"		)

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




              """simple docstring"""


              with self.assertRaisesRegex(
                  _SCREAMING_SNAKE_CASE					,   "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."					,   ):
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"		)

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




              """simple docstring"""


              with self.assertRaises(_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"		)
              # If remote code is disabled, we can't load this config.
              with self.assertRaises(_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_image_processor"					,   trust_remote_code=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  AutoImageProcessor.from_pretrained(
                  "hf-internal-testing/test_dynamic_image_processor"					,   trust_remote_code=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(image_processor.__class__.__name__					,   "NewImageProcessor"		)

              # Test image processor can be reloaded.
              with tempfile.TemporaryDirectory() as tmp_dir:
                     image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE					,   trust_remote_code=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(reloaded_image_processor.__class__.__name__					,   "NewImageProcessor"		)

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




              """simple docstring"""


              try:
                     AutoConfig.register("custom"					,   _SCREAMING_SNAKE_CASE		)
                     AutoImageProcessor.register(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     # Trying to register something existing in the Transformers library will raise an error
                     with self.assertRaises(_SCREAMING_SNAKE_CASE		):
                            AutoImageProcessor.register(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "preprocessor_config.json"
                            UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "config.json"
                            json.dump(
                                {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)					,   )
                            json.dump({"model_type": "clip"}					,   open(_SCREAMING_SNAKE_CASE					,   "w"		)		)

                            UpperCamelCase_          =  CustomImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		)

                     # Now that the config is registered, it can be used as any other config with the auto-API
                     with tempfile.TemporaryDirectory() as tmp_dir:
                            image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE		)
                            self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              finally:
                     if "custom" in CONFIG_MAPPING._extra_content:
                            del CONFIG_MAPPING._extra_content["custom"]
                     if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



       def lowercase (		self: Dict		)       -> Dict:




              """simple docstring"""


              class 							_UpperCamelCase					(	lowerCAmelCase_     ):
                     _UpperCamelCase				:					Any         =   True

              try:
                     AutoConfig.register("custom"					,   _SCREAMING_SNAKE_CASE		)
                     AutoImageProcessor.register(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     # If remote code is not set, the default is to use local
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"		)
                     self.assertEqual(image_processor.__class__.__name__					,   "NewImageProcessor"		)
                     self.assertTrue(image_processor.is_local		)

                     # If remote code is disabled, we load the local one.
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_image_processor"					,   trust_remote_code=_SCREAMING_SNAKE_CASE		)
                     self.assertEqual(image_processor.__class__.__name__					,   "NewImageProcessor"		)
                     self.assertTrue(image_processor.is_local		)

                     # If remote is enabled, we load from the Hub
                     UpperCamelCase_          =  AutoImageProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_image_processor"					,   trust_remote_code=_SCREAMING_SNAKE_CASE		)
                     self.assertEqual(image_processor.__class__.__name__					,   "NewImageProcessor"		)
                     self.assertTrue(not hasattr(_SCREAMING_SNAKE_CASE					,   "is_local"		)		)

              finally:
                     if "custom" in CONFIG_MAPPING._extra_content:
                            del CONFIG_MAPPING._extra_content["custom"]
                     if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








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'

