
'''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


lowercase_    =		get_tests_dir("""fixtures""")
lowercase_    =		get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
lowercase_    =		get_tests_dir("""fixtures/dummy-config.json""")




class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			0




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoFeatureExtractor.from_pretrained(__A				)
												self.assertIsInstance(__A   ,       __A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



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

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

																		config_dict.pop("""feature_extractor_type"""				)
																		_lowerCAmelCase  						=			WavaVecaFeatureExtractor(**__A				)

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

																		_lowerCAmelCase  						=			AutoFeatureExtractor.from_pretrained(__A				)

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

												self.assertIsInstance(__A   ,       __A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoFeatureExtractor.from_pretrained(__A				)
												self.assertIsInstance(__A   ,       __A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												with self.assertRaises(__A				):
																		_lowerCAmelCase  						=			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				):
																		_lowerCAmelCase  						=			AutoFeatureExtractor.from_pretrained(
																		    """hf-internal-testing/test_dynamic_feature_extractor"""   ,       trust_remote_code=__A				)

												_lowerCAmelCase  						=			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				)
																		_lowerCAmelCase  						=			AutoFeatureExtractor.from_pretrained(__A   ,       trust_remote_code=__A				)
												self.assertEqual(reloaded_feature_extractor.__class__.__name__   ,       """NewFeatureExtractor"""				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												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
																		_lowerCAmelCase  						=			CustomFeatureExtractor.from_pretrained(__A				)
																		with tempfile.TemporaryDirectory() as tmp_dir:
																								feature_extractor.save_pretrained(__A				)
																								_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												class   UpperCAmelCase_					(		A__			):






																		'''simple docstring'''






																		_lowercase		:				List[str]									=       True

												try:
																		AutoConfig.register("""custom"""   ,       __A				)
																		AutoFeatureExtractor.register(__A   ,       __A				)
																		# If remote code is not set, the default is to use local
																		_lowerCAmelCase  						=			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.
																		_lowerCAmelCase  						=			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
																		_lowerCAmelCase  						=			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]


'''simple docstring'''



def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:list[int]		):

						# Check if the input is valid
						if not len(__lowerCamelCase		) == len(__lowerCamelCase		) == 3:
												raise ValueError("""Please enter a valid equation."""		)
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("""Both a & b of two equations can't be zero."""		)

						# Extract the coefficients
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa

						# Calculate the determinants of the matrices
						_lowerCAmelCase  						=			aa * ba - aa * ba
						_lowerCAmelCase  						=			ca * ba - ca * ba
						_lowerCAmelCase  						=			aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("""Infinite solutions. (Consistent system)"""		)
												else:
																		raise ValueError("""No solution. (Inconsistent system)"""		)
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowerCAmelCase  						=			determinant_x / determinant
																		_lowerCAmelCase  						=			determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)
