


from __future__ import annotations

from decimal import Decimal

from numpy import array


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		Decimal

						# Check if the provided matrix has 2 rows and 2 columns
						# since this implementation only works for 2x2 matrices
						if len(lowercase__      ) == 2 and len(matrix[0]      ) == 2 and len(matrix[1]      ) == 2:
												# Calculate the determinant of the matrix
												__SCREAMING_SNAKE_CASE						:     Dict					=		float(
												    d(matrix[0][0]      ) * d(matrix[1][1]      ) - d(matrix[1][0]      ) * d(matrix[0][1]      )      )
												if determinant == 0:
																		raise ValueError('''This matrix has no inverse.'''      )

												# Creates a copy of the matrix with swapped positions of the elements
												__SCREAMING_SNAKE_CASE						:     Dict					=		[[0.0, 0.0], [0.0, 0.0]]
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		matrix[1][1], matrix[0][0]
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		-matrix[1][0], -matrix[0][1]

												# Calculate the inverse of the matrix
												return [
												    [(float(d(lowercase__      )      ) / determinant) or 0.0 for n in row] for row in swapped_matrix
												]
						elif (
						    len(lowercase__      ) == 3
						    and len(matrix[0]      ) == 3
						    and len(matrix[1]      ) == 3
						    and len(matrix[2]      ) == 3
						):
												# Calculate the determinant of the matrix using Sarrus rule
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		float(
												    (
												        (d(matrix[0][0]      ) * d(matrix[1][1]      ) * d(matrix[2][2]      ))
												        + (d(matrix[0][1]      ) * d(matrix[1][2]      ) * d(matrix[2][0]      ))
												        + (d(matrix[0][2]      ) * d(matrix[1][0]      ) * d(matrix[2][1]      ))
												    )
												    - (
												        (d(matrix[0][2]      ) * d(matrix[1][1]      ) * d(matrix[2][0]      ))
												        + (d(matrix[0][1]      ) * d(matrix[1][0]      ) * d(matrix[2][2]      ))
												        + (d(matrix[0][0]      ) * d(matrix[1][2]      ) * d(matrix[2][1]      ))
												    )      )
												if determinant == 0:
																		raise ValueError('''This matrix has no inverse.'''      )

												# Creating cofactor matrix
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[
												    [d(0.0      ), d(0.0      ), d(0.0      )],
												    [d(0.0      ), d(0.0      ), d(0.0      )],
												    [d(0.0      ), d(0.0      ), d(0.0      )],
												]
												__SCREAMING_SNAKE_CASE						:     Any					=		(d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
												    d(matrix[1][2]      ) * d(matrix[2][1]      )
												)
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		-(
												    (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
												)
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		(d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
												    d(matrix[1][1]      ) * d(matrix[2][0]      )
												)
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		-(
												    (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
												)
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		(d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
												    d(matrix[0][2]      ) * d(matrix[2][0]      )
												)
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		-(
												    (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
												)
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		(d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
												    d(matrix[0][2]      ) * d(matrix[1][1]      )
												)
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		-(
												    (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
												)
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		(d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
												    d(matrix[0][1]      ) * d(matrix[1][0]      )
												)

												# Transpose the cofactor matrix (Adjoint matrix)
												__SCREAMING_SNAKE_CASE						:     List[str]					=		array(lowercase__      )
												for i in range(3      ):
																		for j in range(3      ):
																								__SCREAMING_SNAKE_CASE						:     Dict					=		cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
												__SCREAMING_SNAKE_CASE						:     int					=		array(lowercase__      )
												for i in range(3      ):
																		for j in range(3      ):
																								inverse_matrix[i][j] /= d(lowercase__      )

        # Calculate the inverse of the matrix
												return [[float(d(lowercase__      )      ) or 0.0 for n in row] for row in inverse_matrix]
						raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''      )







'''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_					:    Any      	=			get_tests_dir('fixtures')
lowerCAmelCase_					:    Union[str, Any]      	=			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase_					:    Dict      	=			get_tests_dir('fixtures/dummy-config.json')
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0




				def   UpperCamelCase__   (  self     :					str					):
								_a								= AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								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     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								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     :					Optional[Any]					):
								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[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     :					List[Any]					):
								# 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     :					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     :					Tuple					):
								class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






												"""simple docstring"""







												__a            =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]
