






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)

class _lowercase			:




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[Any]=13    ,							UpperCamelCase__:       Optional[int]=7    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       int=99    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Tuple=2    ,							UpperCamelCase__:       Tuple=4    ,							UpperCamelCase__:       Optional[Any]=37    ,							UpperCamelCase__:       str="gelu"    ,							UpperCamelCase__:       Optional[int]=0.1    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       List[Any]=512    ,							UpperCamelCase__:       str=16    ,							UpperCamelCase__:       Dict=2    ,							UpperCamelCase__:       List[Any]=0.02    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       Optional[int]=True    ,							UpperCamelCase__:       List[str]="None"    ,							UpperCamelCase__:       List[str]=3    ,							UpperCamelCase__:       Dict=4    ,							UpperCamelCase__:       List[Any]=None    ,							):
														lowerCamelCase__      :			str 						= parent
														lowerCamelCase__      :			Any 						= batch_size
														lowerCamelCase__      :			Optional[Any] 						= seq_length
														lowerCamelCase__      :			Optional[Any] 						= is_training
														lowerCamelCase__      :			Any 						= use_input_mask
														lowerCamelCase__      :			int 						= use_token_type_ids
														lowerCamelCase__      :			str 						= use_labels
														lowerCamelCase__      :			Any 						= vocab_size
														lowerCamelCase__      :			Union[str, Any] 						= hidden_size
														lowerCamelCase__      :			Optional[int] 						= num_hidden_layers
														lowerCamelCase__      :			Dict 						= num_attention_heads
														lowerCamelCase__      :			List[Any] 						= intermediate_size
														lowerCamelCase__      :			str 						= hidden_act
														lowerCamelCase__      :			Optional[int] 						= hidden_dropout_prob
														lowerCamelCase__      :			Optional[Any] 						= attention_probs_dropout_prob
														lowerCamelCase__      :			List[str] 						= max_position_embeddings
														lowerCamelCase__      :			Dict 						= type_vocab_size
														lowerCamelCase__      :			Dict 						= type_sequence_label_size
														lowerCamelCase__      :			List[str] 						= initializer_range
														lowerCamelCase__      :			Dict 						= num_labels
														lowerCamelCase__      :			str 						= num_choices
														lowerCamelCase__      :			List[Any] 						= relative_attention
														lowerCamelCase__      :			Tuple 						= position_biased_input
														lowerCamelCase__      :			Union[str, Any] 						= pos_att_type
														lowerCamelCase__      :			Optional[Any] 						= scope




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Dict 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )

														lowerCamelCase__      :			int 						= None
														if self.use_input_mask:
																					lowerCamelCase__      :			Optional[int] 						= random_attention_mask([self.batch_size, self.seq_length]  )

														lowerCamelCase__      :			Optional[Any] 						= None
														if self.use_token_type_ids:
																					lowerCamelCase__      :			List[str] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size  )

														lowerCamelCase__      :			List[str] 						= None
														lowerCamelCase__      :			Tuple 						= None
														lowerCamelCase__      :			Optional[Any] 						= None
														if self.use_labels:
																					lowerCamelCase__      :			Tuple 						= ids_tensor([self.batch_size]    ,							self.type_sequence_label_size  )
																					lowerCamelCase__      :			str 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.num_labels  )

														lowerCamelCase__      :			Optional[int] 						= DebertaVaConfig(
														    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    ,							relative_attention=self.relative_attention    ,							position_biased_input=self.position_biased_input    ,							initializer_range=self.initializer_range    ,							return_dict=UpperCamelCase__    ,							)

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			List[Any] 						= TFDebertaVaModel(config=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

														lowerCamelCase__      :			List[str] 						= [input_ids, input_mask]
														lowerCamelCase__      :			Any 						= model(UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= model(UpperCamelCase__  )

														self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Dict 						= TFDebertaVaForMaskedLM(config=UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}
														lowerCamelCase__      :			Dict 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size)  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			Optional[int] 						= self.num_labels
														lowerCamelCase__      :			int 						= TFDebertaVaForSequenceClassification(config=UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}

														lowerCamelCase__      :			Optional[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= self.num_labels
														lowerCamelCase__      :			Optional[Any] 						= TFDebertaVaForTokenClassification(config=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]  ):
														lowerCamelCase__      :			Tuple 						= TFDebertaVaForQuestionAnswering(config=UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}

														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length)  )
														self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length)  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Dict 						= self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	
														)      :			List[Any] 						= config_and_inputs
														lowerCamelCase__      :			Dict 						= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
														return config, inputs_dict



@require_tf
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (
							        TFDebertaVaModel,
							        TFDebertaVaForMaskedLM,
							        TFDebertaVaForQuestionAnswering,
							        TFDebertaVaForSequenceClassification,
							        TFDebertaVaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							a          = (
							    {
							        """feature-extraction""": TFDebertaVaModel,
							        """fill-mask""": TFDebertaVaForMaskedLM,
							        """question-answering""": TFDebertaVaForQuestionAnswering,
							        """text-classification""": TFDebertaVaForSequenceClassification,
							        """token-classification""": TFDebertaVaForTokenClassification,
							        """zero-shot""": TFDebertaVaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			int 						= TFDebertaVaModelTester(self  )
														lowerCamelCase__      :			Tuple 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														self.config_tester.run_common_tests()




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			List[str] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Tuple 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Optional[int] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Any 						= TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""  )
														self.assertIsNotNone(UpperCamelCase__  )



@require_tf
class _lowercase			(       unittest.TestCase	):




							@unittest.skip(reason="""Model not available yet"""  )
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														pass




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""  )
														lowerCamelCase__      :			Dict 						= tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
														lowerCamelCase__      :			List[str] 						= tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
														lowerCamelCase__      :			Optional[int] 						= model(UpperCamelCase__    ,							attention_mask=UpperCamelCase__  )[0]

														lowerCamelCase__      :			str 						= tf.constant(
														    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]  )
														tf.debugging.assert_near(output[:, 1:4, 1:4]    ,							UpperCamelCase__    ,							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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			str 						= [False] * len(UpperCamelCase   )
							lowerCamelCase__      :			str 						= [-1] * len(UpperCamelCase   )

							def dfs(UpperCamelCase       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[int] 						= True
														lowerCamelCase__      :			Union[str, Any] 						= c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase       ,    1 - c   )

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

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

							return True


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



print(check_bipartite_dfs(graph))

