




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       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			0






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""			)
								self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE			)
								self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								with tempfile.TemporaryDirectory() as tmpdirname:
														_UpperCAmelCase      =			WavaVecaConfig()

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

														config_dict.pop("""feature_extractor_type"""			)
														_UpperCAmelCase      =			WavaVecaFeatureExtractor(**_SCREAMING_SNAKE_CASE			)

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

														_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE			)

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

								self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE			)
								self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






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






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






		def 				UpperCAmelCase (    self			):
								with self.assertRaisesRegex(
								    _SCREAMING_SNAKE_CASE						,		"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""						,		):
														_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""			)






		def 				UpperCAmelCase (    self			):
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			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			):
														_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained(
														    """hf-internal-testing/test_dynamic_feature_extractor"""						,		trust_remote_code=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			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			)
														_UpperCAmelCase      =			AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE						,		trust_remote_code=_SCREAMING_SNAKE_CASE			)
								self.assertEqual(reloaded_feature_extractor.__class__.__name__						,		"""NewFeatureExtractor"""			)






		def 				UpperCAmelCase (    self			):
								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
														_UpperCAmelCase      =			CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE			)
														with tempfile.TemporaryDirectory() as tmp_dir:
																				feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			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 				UpperCAmelCase (    self			):




								class       _A				(		__lowercase     ):
										__a					    =       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
														_UpperCAmelCase      =			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.
														_UpperCAmelCase      =			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
														_UpperCAmelCase      =			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]





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """Expected the same number of rows for A and B. """
												    f"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """Expected the same number of columns for B and C. """
												    f"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""       )

						return mat_c - mat_b.T @ a_inv @ mat_b




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()