



"""simple docstring"""







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 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					0



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






									'''simple docstring'''

									A         =					AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'				)
									self.assertIsInstance(snake_case      ,							snake_case				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

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

																A         =					AutoImageProcessor.from_pretrained(snake_case				)
																self.assertIsInstance(snake_case      ,							snake_case				)



		def 					A_		( self  :    Any				)			->						Dict:






									'''simple docstring'''

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

																A         =					AutoImageProcessor.from_pretrained(snake_case				)
																self.assertIsInstance(snake_case      ,							snake_case				)



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






									'''simple docstring'''

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

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

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

																config_dict.pop('image_processor_type'				)
																A         =					CLIPImageProcessor(**snake_case				)

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

																A         =					AutoImageProcessor.from_pretrained(snake_case				)

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

									self.assertIsInstance(snake_case      ,							snake_case				)



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






									'''simple docstring'''

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

																A         =					AutoImageProcessor.from_pretrained(snake_case				)
																self.assertIsInstance(snake_case      ,							snake_case				)



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






									'''simple docstring'''

									with self.assertRaisesRegex(
									    snake_case      ,							'clip-base is not a local folder and is not a valid model identifier'				):
																A         =					AutoImageProcessor.from_pretrained('clip-base'				)



		def 					A_		( self  :    Any				)			->						Tuple:






									'''simple docstring'''

									with self.assertRaisesRegex(
									    snake_case      ,							r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'				):
																A         =					AutoImageProcessor.from_pretrained(snake_case      ,							revision='aaaaaa'				)



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






									'''simple docstring'''

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



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






									'''simple docstring'''

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

									A         =					AutoImageProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_image_processor'      ,							trust_remote_code=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(snake_case				)
																A         =					AutoImageProcessor.from_pretrained(snake_case      ,							trust_remote_code=snake_case				)
									self.assertEqual(reloaded_image_processor.__class__.__name__      ,							'NewImageProcessor'				)



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

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

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

																							A         =					CustomImageProcessor.from_pretrained(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(snake_case				)
																							A         =					AutoImageProcessor.from_pretrained(snake_case				)
																							self.assertIsInstance(snake_case      ,							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 					A_		( self  :    List[str]				)			->						Tuple:






									'''simple docstring'''

									class      UpperCAmelCase__		(      UpperCamelCase     ):
											lowerCAmelCase_       :	int 							=     True

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

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








"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('multiplicative_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('multiplicative_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					1
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total *= numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('additive_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('additive_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					0
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total += numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps


if __name__ == "__main__":
							import doctest

							doctest.testmod()



