




"""simple docstring"""




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=13	,				_a=7	,				_a=True	,				_a=True	,				_a=True	,				_a=True	,				_a=99	,				_a=32	,				_a=5	,				_a=4	,				_a=37	,				_a="gelu"	,				_a=0.1	,				_a=0.1	,				_a=512	,				_a=16	,				_a=2	,				_a=0.02	,				_a=4	,				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size				)

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

							lowerCamelCase       =      None
							if self.use_token_type_ids:
										lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size				)

							lowerCamelCase       =      RobertaPreLayerNormConfig(
							    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				is_decoder=__lowerCamelCase	,				initializer_range=self.initializer_range	,				)

							return config, input_ids, token_type_ids, attention_mask





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.prepare_config_and_inputs()
							lowerCamelCase       =      config_and_inputs
							lowerCamelCase       =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.prepare_config_and_inputs()
							lowerCamelCase       =      config_and_inputs

							lowerCamelCase       =      True
							lowerCamelCase       =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
							lowerCamelCase       =      ids_tensor([self.batch_size, self.seq_length]	,				vocab_size=2				)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 					__magic_name__							( _A		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		True

				__UpperCamelCase    						=		(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      FlaxRobertaPreLayerNormModelTester(self				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase       =      model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,				from_pt=__lowerCamelCase				)
										lowerCamelCase       =      model(np.ones((1, 1)				)				)
										self.assertIsNotNone(__lowerCamelCase				)






@require_flax
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,				from_pt=__lowerCamelCase				)

							lowerCamelCase       =      np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	,				dtype=jnp.intaa				)
							lowerCamelCase       =      model(__lowerCamelCase				)[0]
							lowerCamelCase       =      [1, 11, 50_265]
							self.assertEqual(list(output.shape				)	,				__lowerCamelCase				)
							# compare the actual values for a slice.
							lowerCamelCase       =      np.array(
							    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]	,				dtype=np.floataa				)
							self.assertTrue(np.allclose(output[:, :3, :3]	,				__lowerCamelCase	,				atol=1e-4				)				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,				from_pt=__lowerCamelCase				)

							lowerCamelCase       =      np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	,				dtype=jnp.intaa				)
							lowerCamelCase       =      model(__lowerCamelCase				)[0]
							# compare the actual values for a slice.
							lowerCamelCase       =      np.array(
							    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]	,				dtype=np.floataa				)
							self.assertTrue(np.allclose(output[:, :3, :3]	,				__lowerCamelCase	,				atol=1e-4				)				)









"""simple docstring"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def 					a__				(       snake_case__       )		->      Dict:
			lowerCamelCase       =      [False] * len(snake_case__       )
			lowerCamelCase       =      [-1] * len(snake_case__       )

			def dfs(snake_case__ ,				snake_case__       ):
						lowerCamelCase       =      True
						lowerCamelCase       =      c
						for u in graph[v]:
									if not visited[u]:
												dfs(snake_case__ ,				1 - c       )

			for i in range(len(snake_case__       )       ):
						if not visited[i]:
									dfs(snake_case__ ,				0       )

			for i in range(len(snake_case__       )       ):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
lowerCAmelCase							:    str  							=							{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))



