

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 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    0







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'      )
						self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    AutoFeatureExtractor.from_pretrained(__UpperCamelCase      )
						self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						with tempfile.TemporaryDirectory() as tmpdirname:
											__magic_name__							=    WavaVecaConfig()

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

											config_dict.pop('feature_extractor_type'      )
											__magic_name__							=    WavaVecaFeatureExtractor(**__UpperCamelCase      )

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

											__magic_name__							=    AutoFeatureExtractor.from_pretrained(__UpperCamelCase      )

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

						self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    AutoFeatureExtractor.from_pretrained(__UpperCamelCase      )
						self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'bert-base is not a local folder and is not a valid model identifier'      ):
											__magic_name__							=    AutoFeatureExtractor.from_pretrained('bert-base'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						with self.assertRaises(__UpperCamelCase      ):
											__magic_name__							=    AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor'      )
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(__UpperCamelCase      ):
											__magic_name__							=    AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor'  , trust_remote_code=__UpperCamelCase      )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





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

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

						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 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
							_lowercase  :   str       				=  True

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

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







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


if __name__ == "__main__":
						print(f'''{solution() = }''')




