






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


SCREAMING_SNAKE_CASE 							=						get_tests_dir('fixtures')
SCREAMING_SNAKE_CASE 							=						get_tests_dir('fixtures/dummy_feature_extractor_config.json')
SCREAMING_SNAKE_CASE 							=						get_tests_dir('fixtures/dummy-config.json')
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				0


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				AutoFeatureExtractor.from_pretrained(_A)
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as tmpdirname:
         _UpperCAmelCase				:      Tuple			  =				WavaVecaConfig()

         # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
         _UpperCAmelCase				:      List[Any]			  =				AutoFeatureExtractor.from_pretrained(_A).to_dict()

         config_dict.pop('''feature_extractor_type''')
         _UpperCAmelCase				:      Optional[int]			  =				WavaVecaFeatureExtractor(**_A)

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

         _UpperCAmelCase				:      Optional[Any]			  =				AutoFeatureExtractor.from_pretrained(_A)

         # make sure private variable is not incorrectly saved
         _UpperCAmelCase				:      List[Any]			  =				json.loads(config.to_json_string())
         self.assertTrue('''_processor_class''' not in dict_as_saved)

     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				AutoFeatureExtractor.from_pretrained(_A)
     self.assertIsInstance(_A							,  _A)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     with self.assertRaisesRegex(
         _A							,  R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
         _UpperCAmelCase				:      Optional[Any]			  =				AutoFeatureExtractor.from_pretrained(_A							,  revision='''aaaaaa''')


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     with self.assertRaises(_A):
         _UpperCAmelCase				:      List[str]			  =				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				:      Optional[Any]			  =				AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''							,  trust_remote_code=_A)

     _UpperCAmelCase				:      Tuple			  =				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				:      Any			  =				AutoFeatureExtractor.from_pretrained(_A							,  trust_remote_code=_A)
     self.assertEqual(reloaded_feature_extractor.__class__.__name__							,  '''NewFeatureExtractor''')


 def  snake_case__				(	self) ->		Union[str, Any]:

     """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
         _UpperCAmelCase				:      Optional[Any]			  =				CustomFeatureExtractor.from_pretrained(_A)
         with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(_A)
             _UpperCAmelCase				:      Optional[int]			  =				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  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     class 							A_    (						__lowercase			):






      '''simple docstring'''




      _SCREAMING_SNAKE_CASE							:			List[str] =					True

     try:
         AutoConfig.register('''custom'''							,  _A)
         AutoFeatureExtractor.register(_A							,  _A)
         # If remote code is not set, the default is to use local
         _UpperCAmelCase				:      List[Any]			  =				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				:      Tuple			  =				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				:      int			  =				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]












import heapq

def      _lowerCamelCase	(						__A    :							dict )      ->					set[int]:
    _UpperCAmelCase				:      list[list]			  =				[]

    # for each node and his adjacency list add them and the rank of the node to queue
    # using heapq module the queue will be filled like a Priority Queue
    # heapq works with a min priority queue, so I used -1*len(v) to build it
    for key, value in graph.items():
        # O(log(n))
        heapq.heappush(__A		,		[-1 * len(__A ), (key, value)] )

    # chosen_vertices = set of chosen vertices
    _UpperCAmelCase				:      Union[str, Any]			  =				set()

    # while queue isn't empty and there are still edges
    #   (queue[0][0] is the rank of the node with max rank)
    while queue and queue[0][0] != 0:
        # extract vertex with max rank from queue and add it to chosen_vertices
        _UpperCAmelCase				:      Tuple			  =				heapq.heappop(__A )[1][0]
        chosen_vertices.add(__A )

        # Remove all arcs adjacent to argmax
        for elem in queue:
            # if v haven't adjacent node, skip
            if elem[0] == 0:
                continue
            # if argmax is reachable from elem
            # remove argmax from elem's adjacent list and update his rank
            if argmax in elem[1][1]:
                _UpperCAmelCase				:      Dict			  =				elem[1][1].index(__A )
                del elem[1][1][index]
                elem[0] += 1
        # re-order the queue
        heapq.heapify(__A )
    return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE 							=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')




