






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(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
  )

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

  # Adding a second convolutional layer
  classifier.add(layers.ConvaD(32, (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=128, 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 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
  )

  SCREAMING_SNAKE_CASE 							=						tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

  SCREAMING_SNAKE_CASE 							=						train_datagen.flow_from_directory(
      'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
  )

  SCREAMING_SNAKE_CASE 							=						test_datagen.flow_from_directory(
      'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
  )

  classifier.fit_generator(
      training_set, steps_per_epoch=5, epochs=30, 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=(64, 64)
  )
  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'







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

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402


SCREAMING_SNAKE_CASE 							=						get_tests_dir('fixtures/dummy-config.json')
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				0


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

     """simple docstring"""



     self.assertIsNotNone(transformers.models.auto.__spec__)
     self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto'''))


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

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				AutoConfig.from_pretrained('''bert-base-uncased''')
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				AutoConfig.from_pretrained(_A)
     self.assertIsInstance(_A							,  _A)


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				AutoConfig.from_pretrained(_A)
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				AutoConfig.for_model('''roberta''')
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as tmp_dir:
         # This model name contains bert and roberta, but roberta ends up being picked.
         _UpperCAmelCase				:      str			  =				os.path.join(_A							,  '''fake-roberta''')
         os.makedirs(_A							,  exist_ok=_A)
         with open(os.path.join(_A							,  '''config.json''')							,  '''w''') as f:
             f.write(json.dumps({}))
         _UpperCAmelCase				:      Any			  =				AutoConfig.from_pretrained(_A)
         self.assertEqual(type(_A)							,  _A)


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

     """simple docstring"""



     try:
         AutoConfig.register('''custom'''							,  _A)
         # Wrong model type will raise an error
         with self.assertRaises(_A):
             AutoConfig.register('''model'''							,  _A)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(_A):
             AutoConfig.register('''bert'''							,  _A)

         # Now that the config is registered, it can be used as any other config with the auto-API
         _UpperCAmelCase				:      Tuple			  =				CustomConfig()
         with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(_A)
             _UpperCAmelCase				:      Tuple			  =				AutoConfig.from_pretrained(_A)
             self.assertIsInstance(_A							,  _A)

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


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

     """simple docstring"""



     with self.assertRaisesRegex(
         _A							,  '''bert-base is not a local folder and is not a valid model identifier'''):
         _UpperCAmelCase				:      Tuple			  =				AutoConfig.from_pretrained('''bert-base''')


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     with self.assertRaisesRegex(
         _A							,  R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
         _UpperCAmelCase				:      List[Any]			  =				AutoConfig.from_pretrained(_A							,  revision='''aaaaaa''')


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

     """simple docstring"""



     with self.assertRaisesRegex(
         _A							,  '''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.'''							,  ):
         _UpperCAmelCase				:      List[str]			  =				AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo''')


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

     """simple docstring"""



     with self.assertRaises(_A):
         _UpperCAmelCase				:      Dict			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''')
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_A):
         _UpperCAmelCase				:      Optional[int]			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)

     _UpperCAmelCase				:      int			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)
     self.assertEqual(config.__class__.__name__							,  '''NewModelConfig''')

     # Test config can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
         config.save_pretrained(_A)
         _UpperCAmelCase				:      Any			  =				AutoConfig.from_pretrained(_A							,  trust_remote_code=_A)
     self.assertEqual(reloaded_config.__class__.__name__							,  '''NewModelConfig''')


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     class 							A_    (						__lowercase			):






      '''simple docstring'''




      _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					"new-model"

     try:
         AutoConfig.register('''new-model'''							,  _A)
         # If remote code is not set, the default is to use local
         _UpperCAmelCase				:      List[str]			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''')
         self.assertEqual(config.__class__.__name__							,  '''NewModelConfigLocal''')

         # If remote code is disabled, we load the local one.
         _UpperCAmelCase				:      List[Any]			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)
         self.assertEqual(config.__class__.__name__							,  '''NewModelConfigLocal''')

         # If remote is enabled, we load from the Hub
         _UpperCAmelCase				:      Dict			  =				AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model'''							,  trust_remote_code=_A)
         self.assertEqual(config.__class__.__name__							,  '''NewModelConfig''')

     finally:
         if "new-model" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["new-model"]




