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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
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
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__a         =   get_tests_dir('fixtures')
__a         =   get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__a         =   get_tests_dir('fixtures/dummy-config.json')
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     int			)			->	Dict:
					lowercase_	    =   0
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					lowercase_	    =   AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''			)
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	List[str]:
					lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Tuple:
					with tempfile.TemporaryDirectory() as tmpdirname:
									lowercase_	    =   WavaVecaConfig()

									# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			).to_dict()

									config_dict.pop('''feature_extractor_type'''			)
									lowercase_	    =   WavaVecaFeatureExtractor(**SCREAMING_SNAKE_CASE_			)

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

									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)

									# make sure private variable is not incorrectly saved
									lowercase_	    =   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    :     List[str]			)			->	List[str]:
					lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	Optional[int]:
					with self.assertRaisesRegex(
					    SCREAMING_SNAKE_CASE_			,				'''bert-base is not a local folder and is not a valid model identifier'''			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained('''bert-base'''			)
	def 		_lowercase    (			self    :     str			)			->	str:
					with self.assertRaisesRegex(
					    SCREAMING_SNAKE_CASE_			,				R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			,				revision='''aaaaaa'''			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					with self.assertRaisesRegex(
					    SCREAMING_SNAKE_CASE_			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''			)
	def 		_lowercase    (			self    :     str			)			->	Dict:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			)
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)

					# Test feature extractor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
									feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
	def 		_lowercase    (			self    :     Dict			)			->	Union[str, Any]:
					try:
									AutoConfig.register('''custom'''			,				SCREAMING_SNAKE_CASE_			)
									AutoFeatureExtractor.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_			):
													AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									# Now that the config is registered, it can be used as any other config with the auto-API
									lowercase_	    =   CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
									with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)
													lowercase_	    =   AutoFeatureExtractor.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 FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




	def 		_lowercase    (			self    :     str			)			->	List[str]:
					class 					lowercase__(							UpperCAmelCase				):



						"""simple docstring"""




						a     :Union[str, Any]       	=      True

					try:
									AutoConfig.register('''custom'''			,				SCREAMING_SNAKE_CASE_			)
									AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									# If remote code is not set, the default is to use local
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			)
									self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
									self.assertTrue(feature_extractor.is_local			)

									# If remote code is disabled, we load the local one.
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
									self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
									self.assertTrue(feature_extractor.is_local			)

									# If remote is enabled, we load from the Hub
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
									self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
									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 FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]












'''simple docstring'''

def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

      if n == 1 or not isinstance(a_     , a_						):
            return 0
      elif n == 2:
            return 1
      else:
            UpperCamelCase_					: Dict            =				[0, 1]
            for i in range(2     , n + 1						):
                  sequence.append(sequence[i - 1] + sequence[i - 2]						)

            return sequence[n]



def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: Any            =				0
      UpperCamelCase_					: Union[str, Any]            =				2

      while digits < n:
            index += 1
            UpperCamelCase_					: Any            =				len(str(fibonacci(a_						)						)						)

      return index



def 				snake_case       (  a_				:			int = 1_000						)							->					int:





      """simple docstring"""

      return fibonacci_digits_index(a_						)


if __name__ == "__main__":
      print(solution(int(str(input()).strip())))






