


'''simple docstring'''






import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class     __UpperCamelCase						(    unittest.TestCase				):





							def 		a__ (	self		:List[Any]      ):
									snake_case_		:				Dict      							=    0





							def 		a__ (	self		:List[str]      ):
									snake_case_		:				Dict      							=    AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""      )
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase      )





							def 		a__ (	self		:Optional[int]      ):






									with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:				Union[str, Any]      							=    Path(_UpperCAmelCase      ) / 'preprocessor_config.json'
											snake_case_		:				Dict      							=    Path(_UpperCAmelCase      ) / 'config.json'
											json.dump(
											    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}	,open(_UpperCAmelCase	,"""w"""      )	,)
											json.dump({"""model_type""": """clip"""}	,open(_UpperCAmelCase	,"""w"""      )      )

											snake_case_		:				List[Any]      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase      )
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase      )





							def 		a__ (	self		:List[Any]      ):






									with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:				Tuple      							=    Path(_UpperCAmelCase      ) / 'preprocessor_config.json'
											snake_case_		:				Tuple      							=    Path(_UpperCAmelCase      ) / 'config.json'
											json.dump(
											    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}	,open(_UpperCAmelCase	,"""w"""      )	,)
											json.dump({"""model_type""": """clip"""}	,open(_UpperCAmelCase	,"""w"""      )      )

											snake_case_		:				int      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase      )
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase      )





							def 		a__ (	self		:Tuple      ):






									with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:				Optional[int]      							=    CLIPConfig()

											# Create a dummy config file with image_proceesor_type
											snake_case_		:				Union[str, Any]      							=    Path(_UpperCAmelCase      ) / 'preprocessor_config.json'
											snake_case_		:				List[Any]      							=    Path(_UpperCAmelCase      ) / 'config.json'
											json.dump(
											    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}	,open(_UpperCAmelCase	,"""w"""      )	,)
											json.dump({"""model_type""": """clip"""}	,open(_UpperCAmelCase	,"""w"""      )      )

											# remove image_processor_type to make sure config.json alone is enough to load image processor locally
											snake_case_		:				int      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase      ).to_dict()

											config_dict.pop("""image_processor_type"""      )
											snake_case_		:				List[Any]      							=    CLIPImageProcessor(**_UpperCAmelCase      )

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

											snake_case_		:				str      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase      )

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

									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase      )





							def 		a__ (	self		:Union[str, Any]      ):






									with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:				Dict      							=    Path(_UpperCAmelCase      ) / 'preprocessor_config.json'
											json.dump(
											    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}	,open(_UpperCAmelCase	,"""w"""      )	,)

											snake_case_		:				int      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase      )
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase      )





							def 		a__ (	self		:int      ):






									with self.assertRaisesRegex(
									    _UpperCAmelCase	,"""clip-base is not a local folder and is not a valid model identifier"""      ):
											snake_case_		:				Optional[Any]      							=    AutoImageProcessor.from_pretrained("""clip-base"""      )





							def 		a__ (	self		:Tuple      ):






									with self.assertRaisesRegex(
									    _UpperCAmelCase	,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""      ):
											snake_case_		:				List[Any]      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase	,revision="""aaaaaa"""      )





							def 		a__ (	self		:Optional[int]      ):






									with self.assertRaisesRegex(
									    _UpperCAmelCase	,"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""	,):
											snake_case_		:				Dict      							=    AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""      )





							def 		a__ (	self		:Optional[int]      ):






									with self.assertRaises(_UpperCAmelCase      ):
											snake_case_		:				int      							=    AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""      )
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(_UpperCAmelCase      ):
											snake_case_		:				Dict      							=    AutoImageProcessor.from_pretrained(
											    """hf-internal-testing/test_dynamic_image_processor"""	,trust_remote_code=_UpperCAmelCase      )

									snake_case_		:				Union[str, Any]      							=    AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""	,trust_remote_code=_UpperCAmelCase      )
									self.assertEqual(image_processor.__class__.__name__	,"""NewImageProcessor"""      )

									# Test image processor can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(_UpperCAmelCase      )
											snake_case_		:				List[Any]      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase	,trust_remote_code=_UpperCAmelCase      )
									self.assertEqual(reloaded_image_processor.__class__.__name__	,"""NewImageProcessor"""      )





							def 		a__ (	self		:Optional[int]      ):






									try:
											AutoConfig.register("""custom"""	,_UpperCAmelCase      )
											AutoImageProcessor.register(_UpperCAmelCase	,_UpperCAmelCase      )
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(_UpperCAmelCase      ):
													AutoImageProcessor.register(_UpperCAmelCase	,_UpperCAmelCase      )

											with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case_		:				Optional[int]      							=    Path(_UpperCAmelCase      ) / 'preprocessor_config.json'
													snake_case_		:				Optional[Any]      							=    Path(_UpperCAmelCase      ) / 'config.json'
													json.dump(
													    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}	,open(_UpperCAmelCase	,"""w"""      )	,)
													json.dump({"""model_type""": """clip"""}	,open(_UpperCAmelCase	,"""w"""      )      )

													snake_case_		:				Union[str, Any]      							=    CustomImageProcessor.from_pretrained(_UpperCAmelCase      )

											# Now that the config is registered, it can be used as any other config with the auto-API
											with tempfile.TemporaryDirectory() as tmp_dir:
													image_processor.save_pretrained(_UpperCAmelCase      )
													snake_case_		:				List[str]      							=    AutoImageProcessor.from_pretrained(_UpperCAmelCase      )
													self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase      )

									finally:
											if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
													del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





							def 		a__ (	self		:Any      ):
									class     __UpperCamelCase						(    a__				):
																lowercase      :		str 		=	True

									try:
											AutoConfig.register("""custom"""	,_UpperCAmelCase      )
											AutoImageProcessor.register(_UpperCAmelCase	,_UpperCAmelCase      )
											# If remote code is not set, the default is to use local
											snake_case_		:				Dict      							=    AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""      )
											self.assertEqual(image_processor.__class__.__name__	,"""NewImageProcessor"""      )
											self.assertTrue(image_processor.is_local      )

											# If remote code is disabled, we load the local one.
											snake_case_		:				Dict      							=    AutoImageProcessor.from_pretrained(
											    """hf-internal-testing/test_dynamic_image_processor"""	,trust_remote_code=_UpperCAmelCase      )
											self.assertEqual(image_processor.__class__.__name__	,"""NewImageProcessor"""      )
											self.assertTrue(image_processor.is_local      )

											# If remote is enabled, we load from the Hub
											snake_case_		:				Tuple      							=    AutoImageProcessor.from_pretrained(
											    """hf-internal-testing/test_dynamic_image_processor"""	,trust_remote_code=_UpperCAmelCase      )
											self.assertEqual(image_processor.__class__.__name__	,"""NewImageProcessor"""      )
											self.assertTrue(not hasattr(_UpperCAmelCase	,"""is_local"""      )      )

									finally:
											if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
													del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[Any]:  # noqa: E741
     __A		:					Tuple						=			len(__snake_case     )
     __A		:					Optional[int]						=			0
     __A		:					str						=			[0] * n
     __A		:					int						=			[False] * n
     __A		:					Tuple						=			[False] * n

     def dfs(__snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					int     ):
          if parent == root:
               out_edge_count += 1
          __A		:					str						=			True
          __A		:					Tuple						=			at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    __A		:					Optional[int]						=			dfs(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
                    __A		:					int						=			min(low[at]					,				low[to]     )

                    # AP found via bridge
                    if at < low[to]:
                         __A		:					Tuple						=			True
                    # AP found via cycle
                    if at == low[to]:
                         __A		:					Optional[Any]						=			True
               else:
                    __A		:					Any						=			min(low[at]					,				__snake_case     )
          return out_edge_count

     for i in range(__snake_case     ):
          if not visited[i]:
               __A		:					Tuple						=			0
               __A		:					List[Any]						=			dfs(__snake_case					,				__snake_case					,				-1					,				__snake_case     )
               __A		:					Union[str, Any]						=			out_edge_count > 1

     for x in range(len(__snake_case     )     ):
          if is_art[x] is True:
               print(__snake_case     )


# Adjacency list of graph
lowercase__       : Tuple         =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)