

import numpy

# List of input, output pairs
lowerCAmelCase							:  str   	=						(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase							:  Optional[int]   	=						(((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase							:  int   	=						[2, 4, 1, 5]
lowerCAmelCase							:  Dict   	=						len(train_data)
lowerCAmelCase							:  List[Any]   	=						0.009







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase="train"    ):
						return calculate_hypothesis_value(A__				,	A__    ) - output(
						    A__				,	A__    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: int  		=       0
						for i in range(len(A__    ) - 1    ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):



						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):



						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]    )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]    )
						return None







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=m    ):
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0
						for i in range(A__    ):
												if index == -1:
																		summation_value += _error(A__    )
												else:
																		summation_value += _error(A__    ) * train_data[i][0][index]
						return summation_value







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       summation_of_cost_derivative(A__				,	A__    ) / m
						return cost_derivative_value







def 			A_      (    ):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						SCREAMING_SNAKE_CASE_: Tuple  		=       0.0_0_0_0_0_2
						SCREAMING_SNAKE_CASE_: str  		=       0
						SCREAMING_SNAKE_CASE_: Any  		=       0
						while True:
												j += 1
												SCREAMING_SNAKE_CASE_: Any  		=       [0, 0, 0, 0]
												for i in range(0				,	len(A__    )    ):
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_cost_derivative(i - 1    )
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    A__				,	A__				,	atol=A__				,	rtol=A__				,	):
																		break
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       temp_parameter_vector
						print(("Number of iterations:", j)    )







def 			A_      (    ):



						for i in range(len(A__    )    ):
												print(("Actual output value:", output(A__				,	"test"    ))    )
												print(("Hypothesis output:", calculate_hypothesis_value(A__				,	"test"    ))    )


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()












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):
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						0
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""   )
             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )
             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int   ):
             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(UpperCamelCase_   ).to_dict()

                    config_dict.pop("""feature_extractor_type"""   )
                    __lowerCamelCase   =						WavaVecaFeatureExtractor(**UpperCamelCase_   )

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

                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )

                    # 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(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )
             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int   ):
             with self.assertRaisesRegex(
                 UpperCamelCase_    ,       """bert-base is not a local folder and is not a valid model identifier"""   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained("""bert-base"""   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             with self.assertRaisesRegex(
                 UpperCamelCase_    ,       r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_    ,       revision="""aaaaaa"""   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             with self.assertRaisesRegex(
                 UpperCamelCase_    ,       """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 			lowerCAmelCase__						(				self: Tuple   ):
             # If remote code is not set, we will time out when asking whether to load the model.
             with self.assertRaises(UpperCamelCase_   ):
                    __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(UpperCamelCase_   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                        """hf-internal-testing/test_dynamic_feature_extractor"""    ,       trust_remote_code=UpperCamelCase_   )

             __lowerCamelCase   =						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_   )
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_    ,       trust_remote_code=UpperCamelCase_   )
             self.assertEqual(reloaded_feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )
      def 			lowerCAmelCase__						(				self: Any   ):
             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
                    __lowerCamelCase   =						CustomFeatureExtractor.from_pretrained(UpperCamelCase_   )
                    with tempfile.TemporaryDirectory() as tmp_dir:
                           feature_extractor.save_pretrained(UpperCamelCase_   )
                           __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Dict   ):





             class     lowerCamelCase__(					__lowerCamelCase):
                   UpperCAmelCase__      :	str    		=   True

             try:
                    AutoConfig.register("""custom"""    ,       UpperCamelCase_   )
                    AutoFeatureExtractor.register(UpperCamelCase_    ,       UpperCamelCase_   )
                    # 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=UpperCamelCase_   )
                    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=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]

