





import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


_UpperCAmelCase        =				'hf-internal-testing/tiny-random-bert'
_UpperCAmelCase        =				os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
_UpperCAmelCase        =				'9b8c223d42b2188cb49d29af482996f9d0f3e5a6'



class 							_UpperCamelCase					(	unittest.TestCase     ):

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




              """simple docstring"""


              UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              # Should have downloaded the file in here
              self.assertTrue(os.path.isdir(_SCREAMING_SNAKE_CASE		)		)
              # Cache should contain at least those three subfolders:
              for subfolder in ["blobs", "refs", "snapshots"]:
                     self.assertTrue(os.path.isdir(os.path.join(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		)		)
              with open(os.path.join(_SCREAMING_SNAKE_CASE					,   "refs"					,   "main"		)		) as f:
                     UpperCamelCase_          =  f.read()
              self.assertEqual(_SCREAMING_SNAKE_CASE					,   os.path.join(_SCREAMING_SNAKE_CASE					,   "snapshots"					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		)
              self.assertTrue(os.path.isfile(_SCREAMING_SNAKE_CASE		)		)

              # File is cached at the same place the second time.
              UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              self.assertEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              # Using a specific revision to test the full commit hash.
              UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   revision="9b8c223"		)
              self.assertEqual(_SCREAMING_SNAKE_CASE					,   os.path.join(_SCREAMING_SNAKE_CASE					,   "snapshots"					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: int		)       -> int:




              """simple docstring"""


              with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE					,   "is not a valid model identifier"		):
                     UpperCamelCase_          =  cached_file("tiny-random-bert"					,   _SCREAMING_SNAKE_CASE		)

              with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE					,   "is not a valid git identifier"		):
                     UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   revision="aaaa"		)

              with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE					,   "does not appear to have a file named"		):
                     UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   "conf"		)

       def lowercase (		self: int		)       -> str:




              """simple docstring"""


              with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE					,   "does not appear to have a file named"		):
                     UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   "conf"		)

              with open(os.path.join(_SCREAMING_SNAKE_CASE					,   "refs"					,   "main"		)		) as f:
                     UpperCamelCase_          =  f.read()
              self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE					,   ".no_exist"					,   _SCREAMING_SNAKE_CASE					,   "conf"		)		)		)

              UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   "conf"					,   _raise_exceptions_for_missing_entries=_SCREAMING_SNAKE_CASE		)
              self.assertIsNone(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   "conf"					,   local_files_only=_SCREAMING_SNAKE_CASE					,   _raise_exceptions_for_missing_entries=_SCREAMING_SNAKE_CASE		)
              self.assertIsNone(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  mock.Mock()
              UpperCamelCase_          =  500
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  HTTPError
              UpperCamelCase_          =  {}

              # Under the mock environment we get a 500 error when trying to reach the tokenizer.
              with mock.patch("requests.Session.request"					,   return_value=_SCREAMING_SNAKE_CASE		) as mock_head:
                     UpperCamelCase_          =  cached_file(_SCREAMING_SNAKE_CASE					,   "conf"					,   _raise_exceptions_for_connection_errors=_SCREAMING_SNAKE_CASE		)
                     self.assertIsNone(_SCREAMING_SNAKE_CASE		)
                     # This check we did call the fake head request
                     mock_head.assert_called()

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




              """simple docstring"""


              self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only"					,   _SCREAMING_SNAKE_CASE		)		)
              self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"					,   _SCREAMING_SNAKE_CASE		)		)
              self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"					,   _SCREAMING_SNAKE_CASE		)		)

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




              """simple docstring"""


              self.assertIsNone(get_file_from_repo("bert-base-cased"					,   "ahah.txt"		)		)

              # The function raises if the repository does not exist.
              with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE					,   "is not a valid model identifier"		):
                     get_file_from_repo("bert-base-case"					,   _SCREAMING_SNAKE_CASE		)

              # The function raises if the revision does not exist.
              with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE					,   "is not a valid git identifier"		):
                     get_file_from_repo("bert-base-cased"					,   _SCREAMING_SNAKE_CASE					,   revision="ahaha"		)

              UpperCamelCase_          =  get_file_from_repo("bert-base-cased"					,   _SCREAMING_SNAKE_CASE		)
              # The name is the cached name which is not very easy to test, so instead we load the content.
              UpperCamelCase_          =  json.loads(open(_SCREAMING_SNAKE_CASE					,   "r"		).read()		)
              self.assertEqual(config["hidden_size"]					,   768		)



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




              """simple docstring"""


              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase_          =  Path(_SCREAMING_SNAKE_CASE		) / "a.txt"
                     filename.touch()
                     self.assertEqual(get_file_from_repo(_SCREAMING_SNAKE_CASE					,   "a.txt"		)					,   str(_SCREAMING_SNAKE_CASE		)		)

                     self.assertIsNone(get_file_from_repo(_SCREAMING_SNAKE_CASE					,   "b.txt"		)		)








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'

