






'''simple docstring'''



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

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


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

from test_module.custom_configuration import CustomConfig  # noqa E402


__UpperCamelCase   :							List[Any]         =   {
    """return_dict""": False,
    """output_hidden_states""": True,
    """output_attentions""": True,
    """torchscript""": True,
    """torch_dtype""": """float16""",
    """use_bfloat16""": True,
    """tf_legacy_loss""": True,
    """pruned_heads""": {"""a""": 1},
    """tie_word_embeddings""": False,
    """is_decoder""": True,
    """cross_attention_hidden_size""": 128,
    """add_cross_attention""": True,
    """tie_encoder_decoder""": True,
    """max_length""": 50,
    """min_length""": 3,
    """do_sample""": True,
    """early_stopping""": True,
    """num_beams""": 3,
    """num_beam_groups""": 3,
    """diversity_penalty""": 0.5,
    """temperature""": 2.0,
    """top_k""": 10,
    """top_p""": 0.7,
    """typical_p""": 0.2,
    """repetition_penalty""": 0.8,
    """length_penalty""": 0.8,
    """no_repeat_ngram_size""": 5,
    """encoder_no_repeat_ngram_size""": 5,
    """bad_words_ids""": [1, 2, 3],
    """num_return_sequences""": 3,
    """chunk_size_feed_forward""": 5,
    """output_scores""": True,
    """return_dict_in_generate""": True,
    """forced_bos_token_id""": 2,
    """forced_eos_token_id""": 3,
    """remove_invalid_values""": True,
    """architectures""": ["""BertModel"""],
    """finetuning_task""": """translation""",
    """id2label""": {0: """label"""},
    """label2id""": {"""label""": """0"""},
    """tokenizer_class""": """BertTokenizerFast""",
    """prefix""": """prefix""",
    """bos_token_id""": 6,
    """pad_token_id""": 7,
    """eos_token_id""": 8,
    """sep_token_id""": 9,
    """decoder_start_token_id""": 10,
    """exponential_decay_length_penalty""": (5, 1.01),
    """suppress_tokens""": [0, 1],
    """begin_suppress_tokens""": 2,
    """task_specific_params""": {"""translation""": """some_params"""},
    """problem_type""": """regression""",
}
@is_staging_test
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      @classmethod
      def 			__UpperCamelCase				(	cls   )				->Tuple:



         '''simple docstring'''
         __a					  =     TOKEN
         HfFolder.save_token(lowerCamelCase   )




      @classmethod
      def 			__UpperCamelCase				(	cls   )				->Optional[Any]:



         '''simple docstring'''
         try:
            delete_repo(token=cls._token			, repo_id='test-config'   )
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token			, repo_id='valid_org/test-config-org'   )
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token			, repo_id='test-dynamic-config'   )
         except HTTPError:
            pass




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



         '''simple docstring'''
         __a					  =     BertConfig(
             vocab_size=99			, hidden_size=32			, num_hidden_layers=5			, num_attention_heads=4			, intermediate_size=37   )
         config.push_to_hub('test-config'			, use_auth_token=self._token   )

         __a					  =     BertConfig.from_pretrained(F"""{USER}/test-config"""   )
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(lowerCamelCase			, getattr(lowerCamelCase			, lowerCamelCase   )   )

        # Reset repo
         delete_repo(token=self._token			, repo_id='test-config'   )

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            config.save_pretrained(lowerCamelCase			, repo_id='test-config'			, push_to_hub=lowerCamelCase			, use_auth_token=self._token   )

         __a					  =     BertConfig.from_pretrained(F"""{USER}/test-config"""   )
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(lowerCamelCase			, getattr(lowerCamelCase			, lowerCamelCase   )   )




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



         '''simple docstring'''
         __a					  =     BertConfig(
             vocab_size=99			, hidden_size=32			, num_hidden_layers=5			, num_attention_heads=4			, intermediate_size=37   )
         config.push_to_hub('valid_org/test-config-org'			, use_auth_token=self._token   )

         __a					  =     BertConfig.from_pretrained('valid_org/test-config-org'   )
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(lowerCamelCase			, getattr(lowerCamelCase			, lowerCamelCase   )   )

        # Reset repo
         delete_repo(token=self._token			, repo_id='valid_org/test-config-org'   )

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            config.save_pretrained(
                lowerCamelCase			, repo_id='valid_org/test-config-org'			, push_to_hub=lowerCamelCase			, use_auth_token=self._token   )

         __a					  =     BertConfig.from_pretrained('valid_org/test-config-org'   )
         for k, v in config.to_dict().items():
            if k != "transformers_version":
               self.assertEqual(lowerCamelCase			, getattr(lowerCamelCase			, lowerCamelCase   )   )






      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         CustomConfig.register_for_auto_class()
         __a					  =     CustomConfig(attribute=42   )

         config.push_to_hub('test-dynamic-config'			, use_auth_token=self._token   )

         # This has added the proper auto_map field to the config
         self.assertDictEqual(config.auto_map			, {'AutoConfig': 'custom_configuration.CustomConfig'}   )

         __a					  =     AutoConfig.from_pretrained(F"""{USER}/test-dynamic-config"""			, trust_remote_code=lowerCamelCase   )
         # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
         self.assertEqual(new_config.__class__.__name__			, 'CustomConfig'   )
         self.assertEqual(new_config.attribute			, 42   )







class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




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



         '''simple docstring'''
         __a					  =     GPTaConfig()

         # attempt to modify each of int/float/bool/str config records and verify they were updated
         __a					  =     c.n_embd + 1  # int
         __a					  =     c.resid_pdrop + 1.0  # float
         __a					  =     not c.scale_attn_weights  # bool
         __a					  =     c.summary_type + 'foo'  # str
         c.update_from_string(
             F"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"""   )
         self.assertEqual(lowerCamelCase			, c.n_embd			, 'mismatch for key: n_embd'   )
         self.assertEqual(lowerCamelCase			, c.resid_pdrop			, 'mismatch for key: resid_pdrop'   )
         self.assertEqual(lowerCamelCase			, c.scale_attn_weights			, 'mismatch for key: scale_attn_weights'   )
         self.assertEqual(lowerCamelCase			, c.summary_type			, 'mismatch for key: summary_type'   )




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



         '''simple docstring'''
         __a					  =     PretrainedConfig()
         __a					  =     [key for key in base_config.__dict__ if key not in config_common_kwargs]
         # If this part of the test fails, you have arguments to addin config_common_kwargs above.
         self.assertListEqual(
             lowerCamelCase			, ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']   )
         __a					  =     [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase			, lowerCamelCase   )]
         if len(lowerCamelCase   ) > 0:
            raise ValueError(
                'The following keys are set with the default values in'
                ' `test_configuration_common.config_common_kwargs` pick another value for them:'
                F""" {", ".join(lowerCamelCase   )}."""   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         with self.assertRaises(lowerCamelCase   ):
            # config is in subfolder, the following should not work without specifying the subfolder
            __a					  =     BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'   )

         __a					  =     BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'			, subfolder='bert'   )

         self.assertIsNotNone(lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         # A mock response for an HTTP head request to emulate server down
         __a					  =     mock.Mock()
         __a					  =     500
         __a					  =     {}
         __a					  =     HTTPError
         __a					  =     {}

         # Download this model to make sure it's in the cache.
         __a					  =     BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'   )

         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch('requests.Session.request'			, return_value=lowerCamelCase   ) as mock_head:
            __a					  =     BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'   )
            # This check we did call the fake head request
            mock_head.assert_called()




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



         '''simple docstring'''
         # This test is for deprecated behavior and can be removed in v5
         __a					  =     BertConfig.from_pretrained(
             'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     AutoConfig.from_pretrained('bert-base-cased'   )
         __a					  =     ['config.4.0.0.json']

         with tempfile.TemporaryDirectory() as tmp_dir:
            configuration.save_pretrained(lowerCamelCase   )
            __a					  =     2
            json.dump(configuration.to_dict()			, open(os.path.join(lowerCamelCase			, 'config.4.0.0.json'   )			, 'w'   )   )

            # This should pick the new configuration file as the version of Transformers is > 4.0.0
            __a					  =     AutoConfig.from_pretrained(lowerCamelCase   )
            self.assertEqual(new_configuration.hidden_size			, 2   )

            # Will need to be adjusted if we reach v42 and this test is still here.
            # Should pick the old configuration file as the version of Transformers is < 4.42.0
            __a					  =     ['config.42.0.0.json']
            __a					  =     768
            configuration.save_pretrained(lowerCamelCase   )
            shutil.move(os.path.join(lowerCamelCase			, 'config.4.0.0.json'   )			, os.path.join(lowerCamelCase			, 'config.42.0.0.json'   )   )
            __a					  =     AutoConfig.from_pretrained(lowerCamelCase   )
            self.assertEqual(new_configuration.hidden_size			, 768   )






      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
         __a					  =     'hf-internal-testing/test-two-configs'

         import transformers as new_transformers

         __a					  =     'v4.0.0'
         __a  ,      __a					  =     new_transformers.models.auto.AutoConfig.from_pretrained(
             lowerCamelCase			, return_unused_kwargs=lowerCamelCase   )
         self.assertEqual(new_configuration.hidden_size			, 2   )
         # This checks `_configuration_file` ia not kept in the kwargs by mistake.
         self.assertDictEqual(lowerCamelCase			, {}   )

         # Testing an older version by monkey-patching the version in the module it's used.
         import transformers as old_transformers

         __a					  =     'v3.0.0'
         __a					  =     old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase   )
         self.assertEqual(old_configuration.hidden_size			, 768   )







'''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)
 __UpperCamelCase   :							Union[str, Any]         =   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')

 __UpperCamelCase   :							Optional[Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 __UpperCamelCase   :							Any         =   train_datagen.flow_from_directory(
     """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
 )

 __UpperCamelCase   :							str         =   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

 __UpperCamelCase   :							Optional[Any]         =   tf.keras.preprocessing.image.load_img(
     """dataset/single_prediction/image.png""", target_size=(64, 64)
 )
 __UpperCamelCase   :							List[Any]         =   tf.keras.preprocessing.image.img_to_array(test_image)
 __UpperCamelCase   :							Dict         =   np.expand_dims(test_image, axis=0)
 __UpperCamelCase   :							Optional[Any]         =   classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  __UpperCamelCase   :							Optional[int]         =   """Normal"""
 if result[0][0] == 1:
  __UpperCamelCase   :							Optional[int]         =   """Abnormality detected"""