





'''simple docstring'''



import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.distilbert.modeling_flax_distilbert import (
	    FlaxDistilBertForMaskedLM,
	    FlaxDistilBertForMultipleChoice,
	    FlaxDistilBertForQuestionAnswering,
	    FlaxDistilBertForSequenceClassification,
	    FlaxDistilBertForTokenClassification,
	    FlaxDistilBertModel,
	)






class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=13       ,_lowerCamelCase=7       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=99       ,_lowerCamelCase=32       ,_lowerCamelCase=5       ,_lowerCamelCase=4       ,_lowerCamelCase=37       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=512       ,_lowerCamelCase=16       ,_lowerCamelCase=2       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=4       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							parent
				__lowercase			  =							batch_size
				__lowercase			  =							seq_length
				__lowercase			  =							is_training
				__lowercase			  =							use_attention_mask
				__lowercase			  =							use_token_type_ids
				__lowercase			  =							use_labels
				__lowercase			  =							vocab_size
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_size
				__lowercase			  =							hidden_act
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_probs_dropout_prob
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							type_vocab_size
				__lowercase			  =							type_sequence_label_size
				__lowercase			  =							initializer_range
				__lowercase			  =							num_choices


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







				'''simple docstring'''





				__lowercase			  =							ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size	)

				__lowercase			  =							None
				if self.use_attention_mask:
							__lowercase			  =							random_attention_mask([self.batch_size, self.seq_length]	)

				__lowercase			  =							DistilBertConfig(
				    vocab_size=self.vocab_size       ,dim=self.hidden_size       ,n_layers=self.num_hidden_layers       ,n_heads=self.num_attention_heads       ,hidden_dim=self.intermediate_size       ,hidden_act=self.hidden_act       ,dropout=self.hidden_dropout_prob       ,attention_dropout=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,initializer_range=self.initializer_range       ,tie_weights_=_lowerCamelCase       ,)

				return config, input_ids, attention_mask


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				__lowercase  ,						__lowercase  ,						__lowercase			  =							config_and_inputs
				__lowercase			  =							{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict






@require_flax
class 							__lowercase    ( lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			Optional[int]     =    (
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							FlaxDistilBertModelTester(self	)


	@slow
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				for model_class_name in self.all_model_classes:
							__lowercase			  =							model_class_name.from_pretrained('''distilbert-base-uncased'''	)
							__lowercase			  =							model(np.ones((1, 1)	)	)
							self.assertIsNotNone(_lowerCamelCase	)






@require_flax
class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	@slow
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''	)
				__lowercase			  =							np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]	)
				__lowercase			  =							np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
				__lowercase			  =							model(_lowerCamelCase       ,attention_mask=_lowerCamelCase	)[0]
				__lowercase			  =							(1, 11, 768)
				self.assertEqual(output.shape       ,_lowerCamelCase	)
				__lowercase			  =							np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]	)

				self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,_lowerCamelCase       ,atol=1E-4	)	)












'''simple docstring'''



import heapq




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict			):
			__lowercase			  =							[]

			# 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(lowerCamelCase_						,    [-1 * len(lowerCamelCase_			), (key, value)]			)

			# chosen_vertices = set of chosen vertices
			__lowercase			  =							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
						__lowercase			  =							heapq.heappop(lowerCamelCase_			)[1][0]
						chosen_vertices.add(lowerCamelCase_			)

						# 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]:
												__lowercase			  =							elem[1][1].index(lowerCamelCase_			)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(lowerCamelCase_			)
			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)}''')





