




'''simple docstring'''





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


__lowerCAmelCase	:       int     =	get_tests_dir('fixtures')
__lowerCAmelCase	:       str     =	get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__lowerCAmelCase	:       List[Any]     =	get_tests_dir('fixtures/dummy-config.json')




class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  str:
							a					=    0





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Any:
							a					=    AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"							)
							self.assertIsInstance(A_      ,   A_							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  str:
							a					=    AutoFeatureExtractor.from_pretrained(A_							)
							self.assertIsInstance(A_      ,   A_							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  List[str]:
							with tempfile.TemporaryDirectory() as tmpdirname:
										a					=    WavaVecaConfig()

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

										config_dict.pop("feature_extractor_type"							)
										a					=    WavaVecaFeatureExtractor(**A_							)

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

										a					=    AutoFeatureExtractor.from_pretrained(A_							)

										# 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(A_      ,   A_							)





				def 							__UpperCAmelCase							(   self       :  int							)      ->  Optional[Any]:
							a					=    AutoFeatureExtractor.from_pretrained(A_							)
							self.assertIsInstance(A_      ,   A_							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							with self.assertRaisesRegex(
							    A_      ,   "bert-base is not a local folder and is not a valid model identifier"							):
										a					=    AutoFeatureExtractor.from_pretrained("bert-base"							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[str]:
							with self.assertRaisesRegex(
							    A_      ,   r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"							):
										a					=    AutoFeatureExtractor.from_pretrained(A_      ,   revision="aaaaaa"							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Any:
							with self.assertRaisesRegex(
							    A_      ,   "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."      ,   ):
										a					=    AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"							)





				def 							__UpperCAmelCase							(   self       :  int							)      ->  int:
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(A_							):
										a					=    AutoFeatureExtractor.from_pretrained(
										    "hf-internal-testing/test_dynamic_feature_extractor"							)
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(A_							):
										a					=    AutoFeatureExtractor.from_pretrained(
										    "hf-internal-testing/test_dynamic_feature_extractor"      ,   trust_remote_code=A_							)

							a					=    AutoFeatureExtractor.from_pretrained(
							    "hf-internal-testing/test_dynamic_feature_extractor"      ,   trust_remote_code=A_							)
							self.assertEqual(feature_extractor.__class__.__name__      ,   "NewFeatureExtractor"							)

							# Test feature extractor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
										feature_extractor.save_pretrained(A_							)
										a					=    AutoFeatureExtractor.from_pretrained(A_      ,   trust_remote_code=A_							)
							self.assertEqual(reloaded_feature_extractor.__class__.__name__      ,   "NewFeatureExtractor"							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
							try:
										AutoConfig.register("custom"      ,   A_							)
										AutoFeatureExtractor.register(A_      ,   A_							)
										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(A_							):
													AutoFeatureExtractor.register(A_      ,   A_							)

										# Now that the config is registered, it can be used as any other config with the auto-API
										a					=    CustomFeatureExtractor.from_pretrained(A_							)
										with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(A_							)
													a					=    AutoFeatureExtractor.from_pretrained(A_							)
													self.assertIsInstance(A_      ,   A_							)

							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 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[Any]:




							class 					snake_case__							(_UpperCAmelCase     ):
											"""simple docstring"""



											SCREAMING_SNAKE_CASE_ :							Optional[int]											=      True

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

										# If remote is enabled, we load from the Hub
										a					=    AutoFeatureExtractor.from_pretrained(
										    "hf-internal-testing/test_dynamic_feature_extractor"      ,   trust_remote_code=A_							)
										self.assertEqual(feature_extractor.__class__.__name__      ,   "NewFeatureExtractor"							)
										self.assertTrue(not hasattr(A_      ,   "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]












def     __magic_name__       (				A	:      list     ):






			'''simple docstring'''







			for i in range(len(A     ) - 1,						0,						-1     ):
						a					=    False

						for j in range(A,						0,						-1     ):
									if unsorted[j] < unsorted[j - 1]:
												a					,	a					=    unsorted[j - 1], unsorted[j]
												a					=    True

						for j in range(A     ):
									if unsorted[j] > unsorted[j + 1]:
												a					,	a					=    unsorted[j + 1], unsorted[j]
												a					=    True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__lowerCAmelCase	:       Tuple     =	input('Enter numbers separated by a comma:\n').strip()
		__lowerCAmelCase	:       List[Any]     =	[int(item) for item in user_input.split(',')]
		print(F'''{cocktail_shaker_sort(unsorted) = }''')






