





'''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


_A:					int							=				get_tests_dir("""fixtures""")
_A:					str							=				get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_A:					List[Any]							=				get_tests_dir("""fixtures/dummy-config.json""")






class UpperCAmelCase							(		unittest.TestCase							):



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   0



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'					)
					self.assertIsInstance(A_     ,			A_					)



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained(A_					)
					self.assertIsInstance(A_     ,			A_					)



				def 					__lowerCamelCase						(  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(A_					).to_dict()

						config_dict.pop('feature_extractor_type'					)
						__UpperCAmelCase					=   WavaVecaFeatureExtractor(**A_					)

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

						__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained(A_					)

						# 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(A_     ,			A_					)



				def 					__lowerCamelCase						(  self					):
					__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained(A_					)
					self.assertIsInstance(A_     ,			A_					)



				def 					__lowerCamelCase						(  self					):
					with self.assertRaisesRegex(
					    A_     ,			'bert-base is not a local folder and is not a valid model identifier'					):
						__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained('bert-base'					)



				def 					__lowerCamelCase						(  self					):
					with self.assertRaisesRegex(
					    A_     ,			r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'					):
						__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained(A_     ,			revision='aaaaaa'					)



				def 					__lowerCamelCase						(  self					):
					with self.assertRaisesRegex(
					    A_     ,			'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 					__lowerCamelCase						(  self					):
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(A_					):
						__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(A_					):
						__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained(
						    'hf-internal-testing/test_dynamic_feature_extractor'     ,			trust_remote_code=A_					)

					__UpperCAmelCase					=   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_					)
						__UpperCAmelCase					=   AutoFeatureExtractor.from_pretrained(A_     ,			trust_remote_code=A_					)
					self.assertEqual(reloaded_feature_extractor.__class__.__name__     ,			'NewFeatureExtractor'					)



				def 					__lowerCamelCase						(  self					):
					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
						__UpperCAmelCase					=   CustomFeatureExtractor.from_pretrained(A_					)
						with tempfile.TemporaryDirectory() as tmp_dir:
							feature_extractor.save_pretrained(A_					)
							__UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):






					class UpperCAmelCase							(		_UpperCAmelCase							):
									_A :			Optional[int]						   =       True

					try:
						AutoConfig.register('custom'     ,			A_					)
						AutoFeatureExtractor.register(A_     ,			A_					)
						# 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=A_					)
						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=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'''




from collections.abc import Sequence







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 __UpperCAmelCase					=   0.0
 for coeff in reversed(_lowerCAmelCase    ):
  __UpperCAmelCase					=   result * x + coeff
 return result


if __name__ == "__main__":
    _A:					str							=				(0.0, 0.0, 5.0, 9.3, 7.0)
    _A:					Dict							=				10.0
    print(evaluate_poly(poly, x))
    print(horner(poly, x))





