





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






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     0

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''  )
     self.assertIsInstance(__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     AutoFeatureExtractor.from_pretrained(__lowerCamelCase  )
     self.assertIsInstance(__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
         __A    :						Optional[int]						 =     WavaVecaConfig()

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

         config_dict.pop('''feature_extractor_type'''  )
         __A    :						Optional[int]						 =     WavaVecaFeatureExtractor(**__lowerCamelCase  )

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

         __A    :						Tuple						 =     AutoFeatureExtractor.from_pretrained(__lowerCamelCase  )

         # make sure private variable is not incorrectly saved
         __A    :						str						 =     json.loads(config.to_json_string()  )
         self.assertTrue('''_processor_class''' not in dict_as_saved  )

     self.assertIsInstance(__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     AutoFeatureExtractor.from_pretrained(__lowerCamelCase  )
     self.assertIsInstance(__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with self.assertRaisesRegex(
         __lowerCamelCase					,						'''bert-base is not a local folder and is not a valid model identifier'''  ):
         __A    :						Optional[Any]						 =     AutoFeatureExtractor.from_pretrained('''bert-base'''  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with self.assertRaisesRegex(
         __lowerCamelCase					,						'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''					,						):
         __A    :						Union[str, Any]						 =     AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with self.assertRaises(__lowerCamelCase  ):
         __A    :						Optional[int]						 =     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  ):
         __A    :						Optional[Any]						 =     AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''					,						trust_remote_code=__lowerCamelCase  )

     __A    :						Dict						 =     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  )
         __A    :						Optional[Any]						 =     AutoFeatureExtractor.from_pretrained(__lowerCamelCase					,						trust_remote_code=__lowerCamelCase  )
     self.assertEqual(reloaded_feature_extractor.__class__.__name__					,						'''NewFeatureExtractor'''  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     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
         __A    :						int						 =     CustomFeatureExtractor.from_pretrained(__lowerCamelCase  )
         with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(__lowerCamelCase  )
             __A    :						Optional[Any]						 =     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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





      """simple docstring"""


      _lowerCamelCase        =     True

     try:
         AutoConfig.register('''custom'''					,						__lowerCamelCase  )
         AutoFeatureExtractor.register(__lowerCamelCase					,						__lowerCamelCase  )
         # If remote code is not set, the default is to use local
         __A    :						Tuple						 =     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    :						Optional[int]						 =     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
         __A    :						Tuple						 =     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"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




