

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


_snake_case						: int											=							get_tests_dir('fixtures')
_snake_case						: Union[str, Any]											=							get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_snake_case						: str											=							get_tests_dir('fixtures/dummy-config.json')





class __SCREAMING_SNAKE_CASE     (	unittest.TestCase		):






    def 						__lowerCAmelCase    (       self )   ->					Dict:
       __SCREAMING_SNAKE_CASE       =				0






    def 						__lowerCAmelCase    (       self )   ->					Any:
       __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h" )
       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					List[Any]:
       __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(lowerCamelCase_ )
       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					str:
       with tempfile.TemporaryDirectory() as tmpdirname:
          __SCREAMING_SNAKE_CASE       =				WavaVecaConfig()

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

          config_dict.pop("feature_extractor_type" )
          __SCREAMING_SNAKE_CASE       =				WavaVecaFeatureExtractor(**lowerCamelCase_ )

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

          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(lowerCamelCase_ )

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

       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					Union[str, Any]:
       __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(lowerCamelCase_ )
       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					Union[str, Any]:
       with self.assertRaisesRegex(
           lowerCamelCase_,		"bert-base is not a local folder and is not a valid model identifier" ):
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained("bert-base" )






    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       with self.assertRaisesRegex(
           lowerCamelCase_,		r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(lowerCamelCase_,		revision="aaaaaa" )






    def 						__lowerCAmelCase    (       self )   ->					Dict:
       with self.assertRaisesRegex(
           lowerCamelCase_,		"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.",		):
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model" )






    def 						__lowerCAmelCase    (       self )   ->					Union[str, Any]:
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(lowerCamelCase_ ):
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(
              "hf-internal-testing/test_dynamic_feature_extractor" )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(lowerCamelCase_ ):
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(
              "hf-internal-testing/test_dynamic_feature_extractor",		trust_remote_code=lowerCamelCase_ )

       __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(
           "hf-internal-testing/test_dynamic_feature_extractor",		trust_remote_code=lowerCamelCase_ )
       self.assertEqual(feature_extractor.__class__.__name__,		"NewFeatureExtractor" )

       # Test feature extractor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
          feature_extractor.save_pretrained(lowerCamelCase_ )
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(lowerCamelCase_,		trust_remote_code=lowerCamelCase_ )
       self.assertEqual(reloaded_feature_extractor.__class__.__name__,		"NewFeatureExtractor" )






    def 						__lowerCAmelCase    (       self )   ->					int:
       try:
          AutoConfig.register("custom",		lowerCamelCase_ )
          AutoFeatureExtractor.register(lowerCamelCase_,		lowerCamelCase_ )
          # Trying to register something existing in the Transformers library will raise an error
          with self.assertRaises(lowerCamelCase_ ):
             AutoFeatureExtractor.register(lowerCamelCase_,		lowerCamelCase_ )

          # Now that the config is registered, it can be used as any other config with the auto-API
          __SCREAMING_SNAKE_CASE       =				CustomFeatureExtractor.from_pretrained(lowerCamelCase_ )
          with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(lowerCamelCase_ )
             __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(lowerCamelCase_ )
             self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )

       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 )   ->					str:





       class __SCREAMING_SNAKE_CASE     (	a__		):
           SCREAMING_SNAKE_CASE__      =True

       try:
          AutoConfig.register("custom",		lowerCamelCase_ )
          AutoFeatureExtractor.register(lowerCamelCase_,		lowerCamelCase_ )
          # If remote code is not set, the default is to use local
          __SCREAMING_SNAKE_CASE       =				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.
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(
              "hf-internal-testing/test_dynamic_feature_extractor",		trust_remote_code=lowerCamelCase_ )
          self.assertEqual(feature_extractor.__class__.__name__,		"NewFeatureExtractor" )
          self.assertTrue(feature_extractor.is_local )

          # If remote is enabled, we load from the Hub
          __SCREAMING_SNAKE_CASE       =				AutoFeatureExtractor.from_pretrained(
              "hf-internal-testing/test_dynamic_feature_extractor",		trust_remote_code=lowerCamelCase_ )
          self.assertEqual(feature_extractor.__class__.__name__,		"NewFeatureExtractor" )
          self.assertTrue(not hasattr(lowerCamelCase_,		"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 __future__ import annotations

from random import choice


def    _snake_case  (	A			)		->       int:
							return choice(A			)


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      random_pivot(A			)

							# partition based on pivot
							# linear time
							lowerCAmelCase__			  =      [e for e in lst if e < pivot]
							lowerCAmelCase__			  =      [e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(A			) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(A			) < k - 1:
														return kth_number(A					,       k - len(A			) - 1			)
							# pivot is in elements smaller than k
							else:
														return kth_number(A					,       A			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()