





def     _UpperCamelCase     (a__   :List[str]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[0] * len(a__       )
							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			0

							for values in graph.values():
														for i in values:
																					indegree[i] += 1

							for i in range(len(a__       )       ):
														if indegree[i] == 0:
																					queue.append(a__       )

							while queue:
														UpperCamelCase__				     =			queue.pop(0       )
														cnt += 1
														topo.append(a__       )
														for x in graph[vertex]:
																					indegree[x] -= 1
																					if indegree[x] == 0:
																												queue.append(a__       )

							if cnt != len(a__       ):
														print("""Cycle exists"""       )
							else:
														print(a__       )


# Adjacency List of Graph
UpperCamelCase__											= {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)











import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxBertModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxRobertaModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """bert-base is not a local folder and is not a valid model identifier"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""bert-base"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  , revision="""aaaaaa"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"""  , ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""  )







							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(__lowerCAmelCase  , """Use `from_pt=True` to load this model"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""  )




