






'''simple docstring'''







import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    DebertaForMaskedLM,
			    DebertaForQuestionAnswering,
			    DebertaForSequenceClassification,
			    DebertaForTokenClassification,
			    DebertaModel,
			)
			from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST

class _lowercase			(       _lowercase	):




							def __init__(							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]=13    ,							UpperCamelCase__:       Dict=7    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       int=99    ,							UpperCamelCase__:       Union[str, Any]=32    ,							UpperCamelCase__:       List[str]=5    ,							UpperCamelCase__:       Tuple=4    ,							UpperCamelCase__:       Optional[int]=37    ,							UpperCamelCase__:       Optional[int]="gelu"    ,							UpperCamelCase__:       Optional[Any]=0.1    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Optional[Any]=512    ,							UpperCamelCase__:       Optional[Any]=16    ,							UpperCamelCase__:       Optional[int]=2    ,							UpperCamelCase__:       Union[str, Any]=0.02    ,							UpperCamelCase__:       Any=False    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       List[Any]="None"    ,							UpperCamelCase__:       Dict=3    ,							UpperCamelCase__:       int=4    ,							UpperCamelCase__:       Optional[int]=None    ,							):
														lowerCamelCase__      :			List[str] 						= parent
														lowerCamelCase__      :			Tuple 						= batch_size
														lowerCamelCase__      :			Tuple 						= seq_length
														lowerCamelCase__      :			Union[str, Any] 						= is_training
														lowerCamelCase__      :			Tuple 						= use_input_mask
														lowerCamelCase__      :			Dict 						= use_token_type_ids
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			Optional[int] 						= hidden_size
														lowerCamelCase__      :			str 						= num_hidden_layers
														lowerCamelCase__      :			str 						= num_attention_heads
														lowerCamelCase__      :			Optional[int] 						= intermediate_size
														lowerCamelCase__      :			Tuple 						= hidden_act
														lowerCamelCase__      :			Union[str, Any] 						= hidden_dropout_prob
														lowerCamelCase__      :			int 						= attention_probs_dropout_prob
														lowerCamelCase__      :			str 						= max_position_embeddings
														lowerCamelCase__      :			int 						= type_vocab_size
														lowerCamelCase__      :			Optional[int] 						= type_sequence_label_size
														lowerCamelCase__      :			List[Any] 						= initializer_range
														lowerCamelCase__      :			List[Any] 						= num_labels
														lowerCamelCase__      :			str 						= num_choices
														lowerCamelCase__      :			Optional[Any] 						= relative_attention
														lowerCamelCase__      :			int 						= position_biased_input
														lowerCamelCase__      :			int 						= pos_att_type
														lowerCamelCase__      :			int 						= scope




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

														lowerCamelCase__      :			Optional[Any] 						= None
														if self.use_input_mask:
																					lowerCamelCase__      :			Any 						= ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2  )

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

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

														lowerCamelCase__      :			List[Any] 						= self.get_config()

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




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														return DebertaConfig(
														    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    ,							initializer_range=self.initializer_range    ,							relative_attention=self.relative_attention    ,							position_biased_input=self.position_biased_input    ,							pos_att_type=self.pos_att_type    ,							)




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			List[str] 						= self.get_config()
														lowerCamelCase__      :			str 						= 300
														return config




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]  ):
														self.parent.assertListEqual(list(result.loss.size()  )    ,							[]  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			str 						= DebertaModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							token_type_ids=UpperCamelCase__  )[0]
														lowerCamelCase__      :			List[str] 						= model(UpperCamelCase__    ,							token_type_ids=UpperCamelCase__  )[0]
														lowerCamelCase__      :			int 						= model(UpperCamelCase__  )[0]

														self.parent.assertListEqual(list(sequence_output.size()  )    ,							[self.batch_size, self.seq_length, self.hidden_size]  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Dict  ):
														lowerCamelCase__      :			int 						= DebertaForMaskedLM(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							token_type_ids=UpperCamelCase__    ,							labels=UpperCamelCase__  )

														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size)  )




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       List[Any]  ):
														lowerCamelCase__      :			str 						= self.num_labels
														lowerCamelCase__      :			str 						= DebertaForSequenceClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Any 						= model(UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							token_type_ids=UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertListEqual(list(result.logits.size()  )    ,							[self.batch_size, self.num_labels]  )
														self.check_loss_output(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			int 						= self.num_labels
														lowerCamelCase__      :			Tuple 						= DebertaForTokenClassification(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							token_type_ids=UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Dict  ):
														lowerCamelCase__      :			List[str] 						= DebertaForQuestionAnswering(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			int 						= model(
														    UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							token_type_ids=UpperCamelCase__    ,							start_positions=UpperCamelCase__    ,							end_positions=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:       Dict  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	
														)      :			Optional[int] 						= config_and_inputs
														lowerCamelCase__      :			Dict 						= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (
							        DebertaModel,
							        DebertaForMaskedLM,
							        DebertaForSequenceClassification,
							        DebertaForTokenClassification,
							        DebertaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							a          = (
							    {
							        """feature-extraction""": DebertaModel,
							        """fill-mask""": DebertaForMaskedLM,
							        """question-answering""": DebertaForQuestionAnswering,
							        """text-classification""": DebertaForSequenceClassification,
							        """token-classification""": DebertaForTokenClassification,
							        """zero-shot""": DebertaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)

							a          = True
							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Tuple 						= DebertaModelTester(self  )
														lowerCamelCase__      :			List[str] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														self.config_tester.run_common_tests()




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




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Dict 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Dict 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase__  )




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




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




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			int 						= DebertaModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )



@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase			(       unittest.TestCase	):




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




							@slow
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			str 						= DebertaModel.from_pretrained("""microsoft/deberta-base"""  )

														lowerCamelCase__      :			List[Any] 						= torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
														lowerCamelCase__      :			str 						= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
														with torch.no_grad():
																					lowerCamelCase__      :			List[str] 						= model(UpperCamelCase__    ,							attention_mask=UpperCamelCase__  )[0]
														# compare the actual values for a slice.
														lowerCamelCase__      :			Any 						= torch.tensor(
														    [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]  )
														self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,							UpperCamelCase__    ,							atol=1e-4  )    ,							F'''{output[:, 1:4, 1:4]}'''  )









'''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))

