



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


UpperCamelCase__    =						get_tests_dir("fixtures")
UpperCamelCase__    =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
UpperCamelCase__    =						get_tests_dir("fixtures/dummy-config.json")
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       List[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				0





			def 		snake_case__			(				self	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""   )
									self.assertIsInstance(_lowercase			,     _lowercase   )





			def 		snake_case__			(				self	:       Any   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				AutoFeatureExtractor.from_pretrained(_lowercase   )
									self.assertIsInstance(_lowercase			,     _lowercase   )





			def 		snake_case__			(				self	:       int   )						->					Dict:
									"""simple docstring"""







									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(_lowercase   ).to_dict()

															config_dict.pop("""feature_extractor_type"""   )
															__lowercase					=				WavaVecaFeatureExtractor(**_lowercase   )

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

															__lowercase					=				AutoFeatureExtractor.from_pretrained(_lowercase   )

															# 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(_lowercase			,     _lowercase   )





			def 		snake_case__			(				self	:       Dict   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				AutoFeatureExtractor.from_pretrained(_lowercase   )
									self.assertIsInstance(_lowercase			,     _lowercase   )





			def 		snake_case__			(				self	:       Dict   )						->					Union[str, Any]:
									"""simple docstring"""







									with self.assertRaisesRegex(
									    _lowercase			,     """bert-base is not a local folder and is not a valid model identifier"""   ):
															__lowercase					=				AutoFeatureExtractor.from_pretrained("""bert-base"""   )





			def 		snake_case__			(				self	:       List[str]   )						->					int:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Any:
									"""simple docstring"""







									with self.assertRaisesRegex(
									    _lowercase			,     """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 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									with self.assertRaises(_lowercase   ):
															__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(_lowercase   ):
															__lowercase					=				AutoFeatureExtractor.from_pretrained(
															    """hf-internal-testing/test_dynamic_feature_extractor"""			,     trust_remote_code=_lowercase   )

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

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





			def 		snake_case__			(				self	:       Any   )						->					Optional[int]:
									"""simple docstring"""







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

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

									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 		snake_case__			(				self	:       Union[str, Any]   )						->					List[Any]:
									"""simple docstring"""







									class 			_lowerCAmelCase   ( UpperCAmelCase_			):






												"""simple docstring"""





												lowercase__      :  Tuple      =			True

									try:
															AutoConfig.register("""custom"""			,     _lowercase   )
															AutoFeatureExtractor.register(_lowercase			,     _lowercase   )
															# 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=_lowercase   )
															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=_lowercase   )
															self.assertEqual(feature_extractor.__class__.__name__			,     """NewFeatureExtractor"""   )
															self.assertTrue(not hasattr(_lowercase			,     """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]










from __future__ import annotations

from collections.abc import Callable

UpperCamelCase__ =						list[list[float | int]]






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Matrix:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[0 for _ in range(size + 1   )] for _ in range(lowercase__   )]
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42

						for row in range(lowercase__   ):
												for col in range(lowercase__   ):
																		__lowercase					=				matrix[row][col]

												__lowercase					=				vector[row][0]

						__lowercase					=				0
						__lowercase					=				0
						while row < size and col < size:
												# pivoting
												__lowercase					=				max((abs(augmented[rowa][col]   ), rowa) for rowa in range(lowercase__						,       lowercase__   )   )[
												    1
												]
												if augmented[pivot_row][col] == 0:
																		col += 1
																		continue
												else:
																		__lowercase	,			__lowercase					=				augmented[pivot_row], augmented[row]

												for rowa in range(row + 1						,       lowercase__   ):
																		__lowercase					=				augmented[rowa][col] / augmented[row][col]
																		__lowercase					=				0
																		for cola in range(col + 1						,       size + 1   ):
																								augmented[rowa][cola] -= augmented[row][cola] * ratio

												row += 1
												col += 1

						# back substitution
						for col in range(1						,       lowercase__   ):
												for row in range(lowercase__   ):
																		__lowercase					=				augmented[row][col] / augmented[col][col]
																		for cola in range(lowercase__						,       size + 1   ):
																								augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
						return [
						    [round(augmented[row][size] / augmented[row][row]						,       10   )] for row in range(lowercase__   )
						]






def  UpperCAmelCase__    (    lowercase__   ) ->      Callable[[int], int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[0 for _ in range(lowercase__   )] for _ in range(lowercase__   )]
						__lowercase					=				[[0] for _ in range(lowercase__   )]
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42

						for x_val, y_val in enumerate(lowercase__   ):
												for col in range(lowercase__   ):
																		__lowercase					=				(x_val + 1) ** (size - col - 1)
												__lowercase					=				y_val

						__lowercase					=				solve(lowercase__						,       lowercase__   )

						def interpolated_func(lowercase__   ) -> int:
												return sum(
												    round(coeffs[x_val][0]   ) * (var ** (size - x_val - 1))
												    for x_val in range(lowercase__   )   )

						return interpolated_func






def  UpperCAmelCase__    (    lowercase__   ) ->      int:
						return (
						    1
						    - variable
						    + variable**2
						    - variable**3
						    + variable**4
						    - variable**5
						    + variable**6
						    - variable**7
						    + variable**8
						    - variable**9
						    + variable**10
						)






def  UpperCAmelCase__    (    lowercase__ = question_function						,       lowercase__ = 10   ) ->      int:
						__lowercase					=				[func(lowercase__   ) for x_val in range(1						,       order + 1   )]

						__lowercase					=				[
						    interpolate(data_points[:max_coeff]   ) for max_coeff in range(1						,       order + 1   )
						]

						__lowercase					=				0
						__lowercase					=				42
						__lowercase					=				42

						for poly in polynomials:
												__lowercase					=				1
												while func(lowercase__   ) == poly(lowercase__   ):
																		x_val += 1

												ret += poly(lowercase__   )

						return ret


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





