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       snake_case					:




		def __init__(      self     :	List[str]     , a_     :	Tuple     , a_     :	Any=13     , a_     :	Any=7     , a_     :	int=True     , a_     :	Tuple=True     , a_     :	Dict=True     , a_     :	str=True     , a_     :	str=99     , a_     :	Tuple=32     , a_     :	Dict=2     , a_     :	Tuple=4     , a_     :	List[Any]=37     , a_     :	Optional[Any]="gelu"     , a_     :	Union[str, Any]=0.1     , a_     :	Optional[Any]=0.1     , a_     :	int=512     , a_     :	List[Any]=16     , a_     :	Tuple=2     , a_     :	Union[str, Any]=0.02     , a_     :	Union[str, Any]=False     , a_     :	List[str]=True     , a_     :	str="None"     , a_     :	Union[str, Any]=3     , a_     :	List[Any]=4     , a_     :	List[str]=None     , )->       int:




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




		def 	__lowercase(      self     :	Any				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size				)

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

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

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

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		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=a_     , )

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




		def 	__lowercase(      self     :	Any     , a_     :	Union[str, Any]     , a_     :	List[Any]     , a_     :	Dict     , a_     :	str     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , a_     :	int				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		TFDebertaVaModel(config=a_				)
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[input_ids, input_mask]
							SCREAMING_SNAKE_CASE__				:				Any			=		model(a_				)

							SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_				)

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




		def 	__lowercase(      self     :	int     , a_     :	Optional[Any]     , a_     :	int     , a_     :	Dict     , a_     :	int     , a_     :	str     , a_     :	Any     , a_     :	Any				)->       List[str]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		TFDebertaVaForMaskedLM(config=a_				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							SCREAMING_SNAKE_CASE__				:				int			=		model(a_				)
							self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)




		def 	__lowercase(      self     :	int     , a_     :	Optional[Any]     , a_     :	int     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , a_     :	Tuple     , a_     :	str     , a_     :	List[Any]				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		self.num_labels
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		TFDebertaVaForSequenceClassification(config=a_				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		{
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_				)
							self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)




		def 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	Tuple     , a_     :	int     , a_     :	Any     , a_     :	Optional[int]     , a_     :	Any     , a_     :	int				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.num_labels
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		TFDebertaVaForTokenClassification(config=a_				)
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							SCREAMING_SNAKE_CASE__				:				Any			=		model(a_				)
							self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)				)




		def 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	int     , a_     :	int     , a_     :	Union[str, Any]     , a_     :	str     , a_     :	Optional[int]     , a_     :	Any				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		TFDebertaVaForQuestionAnswering(config=a_				)
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		{
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_				)
							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 	__lowercase(      self     :	List[str]				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.prepare_config_and_inputs()
							(
							    SCREAMING_SNAKE_CASE__							
							)				:				Optional[int]			=		config_and_inputs
							SCREAMING_SNAKE_CASE__				:				List[str]			=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict




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

		lowercase_			=     False
		lowercase_			=     False




		def 	__lowercase(      self     :	str				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		TFDebertaVaModelTester(self				)
							SCREAMING_SNAKE_CASE__				:				List[str]			=		ConfigTester(self     , config_class=a_     , hidden_size=37				)




		def 	__lowercase(      self     :	Any				)->       Union[str, Any]:




							"""simple docstring"""
							self.config_tester.run_common_tests()




		def 	__lowercase(      self     :	Dict				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a_				)




		def 	__lowercase(      self     :	Union[str, Any]				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*a_				)




		def 	__lowercase(      self     :	Tuple				)->       Optional[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				int			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*a_				)




		def 	__lowercase(      self     :	Union[str, Any]				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*a_				)




		def 	__lowercase(      self     :	Optional[int]				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*a_				)




		@slow
		def 	__lowercase(      self     :	Tuple				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'				)
							self.assertIsNotNone(a_				)




@require_tf
class       snake_case					(      unittest.TestCase						):




		@unittest.skip(reason='Model not available yet'				)
		def 	__lowercase(      self     :	str				)->       List[str]:




							"""simple docstring"""
							pass




		@slow
		def 	__lowercase(      self     :	Dict				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				str			=		TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				)
							SCREAMING_SNAKE_CASE__				:				List[str]			=		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
							SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_     , attention_mask=a_				)[0]

							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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]     , a_     , atol=1e-4				)




import heapq as hq
import math
from collections.abc import Iterator

class       snake_case					:




  def __init__(      self     :	str     , a_     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		str(id_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		None
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				Any			=		[]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}  # {vertex:distance}




  def __lt__(      self     :	int     , a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       return self.key < other.key




  def __repr__(      self     :	Any				)->       Dict:




       """simple docstring"""
       return self.id




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	int				)->       List[str]:




       """simple docstring"""
       self.neighbors.append(a_				)




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		weight







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Tuple     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     graph[a - 1].add_neighbor(graph[b - 1] )
     graph[b - 1].add_neighbor(graph[a - 1] )
     # add the edges:
     graph[a - 1].add_edge(graph[b - 1]     ,  lowercase__ )
     graph[b - 1].add_edge(graph[a - 1]     ,  lowercase__ )







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     for u in graph:
          SCREAMING_SNAKE_CASE__				:				Dict			=		math.inf
          SCREAMING_SNAKE_CASE__				:				str			=		None
     SCREAMING_SNAKE_CASE__				:				List[str]			=		0
     SCREAMING_SNAKE_CASE__				:				int			=		graph[:]
     while q:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		min(lowercase__ )
          q.remove(lowercase__ )
          for v in u.neighbors:
               if (v in q) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				int			=		u
                    SCREAMING_SNAKE_CASE__				:				Any			=		u.edges[v.id]
     for i in range(1     ,  len(lowercase__ ) ):
          a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
     return a







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     for u in graph:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		math.inf
          SCREAMING_SNAKE_CASE__				:				int			=		None
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0

     SCREAMING_SNAKE_CASE__				:				Tuple			=		list(lowercase__ )
     hq.heapify(lowercase__ )

     while h:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hq.heappop(lowercase__ )
          for v in u.neighbors:
               if (v in h) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				List[str]			=		u
                    SCREAMING_SNAKE_CASE__				:				Dict			=		u.edges[v.id]
                    hq.heapify(lowercase__ )

     for i in range(1     ,  len(lowercase__ ) ):
          yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def     _a				(      ):







     '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()



