




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 		__A   :

						def __init__(		self :List[str]     ,			__snake_case :Optional[int]     ,			__snake_case :str=13     ,			__snake_case :int=7     ,			__snake_case :List[str]=True     ,			__snake_case :Tuple=True     ,			__snake_case :List[str]=True     ,			__snake_case :Optional[int]=True     ,			__snake_case :Tuple=99     ,			__snake_case :Tuple=32     ,			__snake_case :Optional[int]=2     ,			__snake_case :str=4     ,			__snake_case :List[Any]=37     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :str=0.1     ,			__snake_case :List[str]=0.1     ,			__snake_case :Tuple=5_12     ,			__snake_case :List[Any]=16     ,			__snake_case :Union[str, Any]=2     ,			__snake_case :Tuple=0.02     ,			__snake_case :int=False     ,			__snake_case :Optional[Any]=True     ,			__snake_case :Union[str, Any]="None"     ,			__snake_case :str=3     ,			__snake_case :Any=4     ,			__snake_case :Dict=None     ,			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=parent
										__magic_name__						:						int										=batch_size
										__magic_name__						:						str										=seq_length
										__magic_name__						:						Optional[int]										=is_training
										__magic_name__						:						Dict										=use_input_mask
										__magic_name__						:						Any										=use_token_type_ids
										__magic_name__						:						List[str]										=use_labels
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						Union[str, Any]										=hidden_size
										__magic_name__						:						str										=num_hidden_layers
										__magic_name__						:						Union[str, Any]										=num_attention_heads
										__magic_name__						:						int										=intermediate_size
										__magic_name__						:						List[Any]										=hidden_act
										__magic_name__						:						int										=hidden_dropout_prob
										__magic_name__						:						Dict										=attention_probs_dropout_prob
										__magic_name__						:						int										=max_position_embeddings
										__magic_name__						:						str										=type_vocab_size
										__magic_name__						:						List[Any]										=type_sequence_label_size
										__magic_name__						:						Union[str, Any]										=initializer_range
										__magic_name__						:						int										=num_labels
										__magic_name__						:						str										=num_choices
										__magic_name__						:						int										=relative_attention
										__magic_name__						:						int										=position_biased_input
										__magic_name__						:						Union[str, Any]										=pos_att_type
										__magic_name__						:						List[str]										=scope

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size			)

										__magic_name__						:						Any										=None
										if self.use_input_mask:
														__magic_name__						:						str										=random_attention_mask([self.batch_size, self.seq_length]			)

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

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

										__magic_name__						:						str										=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=__snake_case     ,			)

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

						def      A__    (		self :int     ,			__snake_case :Any     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[Any]     ,			__snake_case :Tuple     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=TFDebertaVaModel(config=__snake_case			)
										__magic_name__						:						Tuple										={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

										__magic_name__						:						List[str]										=[input_ids, input_mask]
										__magic_name__						:						List[str]										=model(__snake_case			)

										__magic_name__						:						Union[str, Any]										=model(__snake_case			)

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

						def      A__    (		self :List[Any]     ,			__snake_case :List[str]     ,			__snake_case :List[str]     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]     ,			__snake_case :Optional[Any]     ,			__snake_case :Optional[Any]     ,			__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFDebertaVaForMaskedLM(config=__snake_case			)
										__magic_name__						:						Optional[int]										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__magic_name__						:						int										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)			)

						def      A__    (		self :Any     ,			__snake_case :Tuple     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :Any     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.num_labels
										__magic_name__						:						Union[str, Any]										=TFDebertaVaForSequenceClassification(config=__snake_case			)
										__magic_name__						:						Tuple										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__magic_name__						:						int										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)			)

						def      A__    (		self :Dict     ,			__snake_case :List[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :str     ,			__snake_case :Dict     ,			__snake_case :int     ,			__snake_case :str     ,			__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.num_labels
										__magic_name__						:						Optional[Any]										=TFDebertaVaForTokenClassification(config=__snake_case			)
										__magic_name__						:						Optional[Any]										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__magic_name__						:						int										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)			)

						def      A__    (		self :List[str]     ,			__snake_case :str     ,			__snake_case :Dict     ,			__snake_case :int     ,			__snake_case :Dict     ,			__snake_case :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFDebertaVaForQuestionAnswering(config=__snake_case			)
										__magic_name__						:						str										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__magic_name__						:						List[Any]										=model(__snake_case			)
										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      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			
										)						:						Union[str, Any]										=config_and_inputs
										__magic_name__						:						List[Any]										={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict




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

						UpperCamelCase      =       False
						UpperCamelCase      =       False

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFDebertaVaModelTester(self			)
										__magic_name__						:						Any										=ConfigTester(self     ,			config_class=__snake_case     ,			hidden_size=37			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__snake_case			)

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						str										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						str										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__snake_case			)

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__snake_case			)


						@slow
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Any										=TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""			)
										self.assertIsNotNone(__snake_case			)




@require_tf
class 		__A   (      unittest.TestCase      ):

						@unittest.skip(reason="""Model not available yet"""			)
						def      A__    (		self :str			):
										'''simple docstring'''

										pass


						@slow
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""			)
										__magic_name__						:						str										=tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			)
										__magic_name__						:						List[str]										=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
										__magic_name__						:						Tuple										=model(__snake_case     ,			attention_mask=__snake_case			)[0]

										__magic_name__						:						Any										=tf.constant(
										    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]			)
										tf.debugging.assert_near(output[:, 1:4, 1:4]     ,			__snake_case     ,			atol=1E-4			)










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# 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
				__magic_name__						:						Tuple										=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
								__magic_name__						:						Tuple										=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]:
																__magic_name__						:						Tuple										=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()

							UpperCAmelCase_		:					Optional[int]          =  {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)}""")




