


'''simple docstring'''

import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MegatronBertForCausalLM,
							    MegatronBertForMaskedLM,
							    MegatronBertForMultipleChoice,
							    MegatronBertForNextSentencePrediction,
							    MegatronBertForPreTraining,
							    MegatronBertForQuestionAnswering,
							    MegatronBertForSequenceClassification,
							    MegatronBertForTokenClassification,
							    MegatronBertModel,
							)
class _lowerCamelCase	:



		'''simple docstring'''







		def __init__( self		:						int  ,					_A		:						int  ,					_A		:						Optional[int]=13  ,					_A		:						str=7  ,					_A		:						Union[str, Any]=True  ,					_A		:						Optional[int]=True  ,					_A		:						List[str]=True  ,					_A		:						Optional[int]=True  ,					_A		:						List[Any]=99  ,					_A		:						Any=64  ,					_A		:						List[Any]=32  ,					_A		:						int=5  ,					_A		:						Optional[Any]=4  ,					_A		:						int=37  ,					_A		:						Optional[int]="gelu"  ,					_A		:						Union[str, Any]=0.1  ,					_A		:						Tuple=0.1  ,					_A		:						List[str]=512  ,					_A		:						Optional[int]=16  ,					_A		:						List[str]=2  ,					_A		:						Any=0.02  ,					_A		:						Dict=3  ,					_A		:						Optional[int]=4  ,					_A		:						Any=None  ,					)  ->			Optional[int]:
			__magic_name__	:   Dict					     =					parent
			__magic_name__	:   Any					     =					batch_size
			__magic_name__	:   Dict					     =					seq_length
			__magic_name__	:   Any					     =					is_training
			__magic_name__	:   List[str]					     =					use_input_mask
			__magic_name__	:   Tuple					     =					use_token_type_ids
			__magic_name__	:   Union[str, Any]					     =					use_labels
			__magic_name__	:   Any					     =					vocab_size
			__magic_name__	:   List[str]					     =					hidden_size
			__magic_name__	:   str					     =					embedding_size
			__magic_name__	:   Optional[Any]					     =					num_hidden_layers
			__magic_name__	:   Union[str, Any]					     =					num_attention_heads
			__magic_name__	:   Any					     =					intermediate_size
			__magic_name__	:   Optional[Any]					     =					hidden_act
			__magic_name__	:   Dict					     =					hidden_dropout_prob
			__magic_name__	:   Any					     =					attention_probs_dropout_prob
			__magic_name__	:   int					     =					max_position_embeddings
			__magic_name__	:   List[str]					     =					type_vocab_size
			__magic_name__	:   List[Any]					     =					type_sequence_label_size
			__magic_name__	:   Union[str, Any]					     =					initializer_range
			__magic_name__	:   Optional[Any]					     =					num_labels
			__magic_name__	:   Any					     =					num_choices
			__magic_name__	:   str					     =					scope







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Optional[Any]:
			__magic_name__	:   Dict					     =					ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size      )

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

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

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

			__magic_name__	:   Any					     =					self.get_config()

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







		def        __lowerCAmelCase		( self		:						Any      )  ->			List[Any]:
			return MegatronBertConfig(
			    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  ,					embedding_size=self.embedding_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=lowercase_  ,					initializer_range=self.initializer_range  ,					)







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						Any  ,					_A		:						int  ,					_A		:						Any  ,					_A		:						Optional[int]  ,					_A		:						List[Any]  ,					_A		:						Dict  ,					_A		:						int      )  ->			List[Any]:
			__magic_name__	:   Tuple					     =					MegatronBertModel(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   Optional[int]					     =					model(lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_      )
			__magic_name__	:   str					     =					model(lowercase_  ,					token_type_ids=lowercase_      )
			__magic_name__	:   Dict					     =					model(lowercase_      )

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







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						int  ,					_A		:						Any  ,					_A		:						str  ,					_A		:						Optional[Any]  ,					_A		:						Tuple  ,					_A		:						Union[str, Any]  ,					_A		:						Dict      )  ->			Optional[int]:
			__magic_name__	:   int					     =					MegatronBertForMaskedLM(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   List[Any]					     =					model(lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_      )
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Any  ,					_A		:						Optional[Any]  ,					_A		:						List[str]  ,					_A		:						int  ,					_A		:						Tuple  ,					_A		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			str:
			__magic_name__	:   Any					     =					MegatronBertForCausalLM(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   str					     =					model(lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_      )
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						List[Any]  ,					_A		:						Optional[Any]  ,					_A		:						Dict  ,					_A		:						str  ,					_A		:						Tuple  ,					_A		:						Any      )  ->			List[str]:
			__magic_name__	:   List[str]					     =					MegatronBertForNextSentencePrediction(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   Tuple					     =					model(
			    lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_  ,					)
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, 2)      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int  ,					_A		:						Union[str, Any]  ,					_A		:						Dict  ,					_A		:						Dict  ,					_A		:						List[str]      )  ->			Optional[Any]:
			__magic_name__	:   Union[str, Any]					     =					MegatronBertForPreTraining(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   List[Any]					     =					model(
			    lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_  ,					next_sentence_label=lowercase_  ,					)
			self.parent.assertEqual(result.prediction_logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)      )
			self.parent.assertEqual(result.seq_relationship_logits.shape  ,					(self.batch_size, 2)      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						int  ,					_A		:						List[Any]  ,					_A		:						Union[str, Any]  ,					_A		:						Optional[int]      )  ->			Optional[Any]:
			__magic_name__	:   Dict					     =					MegatronBertForQuestionAnswering(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   Optional[Any]					     =					model(
			    lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					start_positions=lowercase_  ,					end_positions=lowercase_  ,					)
			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		:						Optional[int]  ,					_A		:						List[str]  ,					_A		:						List[Any]  ,					_A		:						Optional[Any]  ,					_A		:						Dict  ,					_A		:						Union[str, Any]  ,					_A		:						List[Any]  ,					_A		:						Union[str, Any]      )  ->			str:
			__magic_name__	:   Optional[Any]					     =					self.num_labels
			__magic_name__	:   int					     =					MegatronBertForSequenceClassification(lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   List[Any]					     =					model(lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_      )
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						int  ,					_A		:						Dict  ,					_A		:						Any  ,					_A		:						Any  ,					_A		:						Tuple      )  ->			List[Any]:
			__magic_name__	:   Dict					     =					self.num_labels
			__magic_name__	:   str					     =					MegatronBertForTokenClassification(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   str					     =					model(lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_      )
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						str  ,					_A		:						int  ,					_A		:						Dict  ,					_A		:						List[str]  ,					_A		:						Tuple  ,					_A		:						List[Any]  ,					_A		:						List[Any]      )  ->			List[str]:
			__magic_name__	:   List[str]					     =					self.num_choices
			__magic_name__	:   Any					     =					MegatronBertForMultipleChoice(config=lowercase_      )
			model.to(lowercase_      )
			model.eval()
			__magic_name__	:   Optional[int]					     =					input_ids.unsqueeze(1      ).expand(-1  ,					self.num_choices  ,					-1      ).contiguous()
			__magic_name__	:   Union[str, Any]					     =					token_type_ids.unsqueeze(1      ).expand(-1  ,					self.num_choices  ,					-1      ).contiguous()
			__magic_name__	:   Union[str, Any]					     =					input_mask.unsqueeze(1      ).expand(-1  ,					self.num_choices  ,					-1      ).contiguous()
			__magic_name__	:   Any					     =					model(
			    lowercase_  ,					attention_mask=lowercase_  ,					token_type_ids=lowercase_  ,					labels=lowercase_  ,					)
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_choices)      )







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







@require_torch
class _lowerCamelCase	(						_UpperCAmelCase		,					_UpperCAmelCase		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      str				   =    (
		    (
		        MegatronBertModel,
		        MegatronBertForMaskedLM,
		        MegatronBertForCausalLM,
		        MegatronBertForMultipleChoice,
		        MegatronBertForNextSentencePrediction,
		        MegatronBertForPreTraining,
		        MegatronBertForQuestionAnswering,
		        MegatronBertForSequenceClassification,
		        MegatronBertForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		A_     :      Dict				   =    (
		    {
		        """feature-extraction""": MegatronBertModel,
		        """fill-mask""": MegatronBertForMaskedLM,
		        """question-answering""": MegatronBertForQuestionAnswering,
		        """text-classification""": MegatronBertForSequenceClassification,
		        """text-generation""": MegatronBertForCausalLM,
		        """token-classification""": MegatronBertForTokenClassification,
		        """zero-shot""": MegatronBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A_     :      List[Any]				   =    True
		# test_resize_embeddings = False
		A_     :      Optional[Any]				   =    False







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Optional[int]  ,					_A		:						List[str]  ,					_A		:						Union[str, Any]=False      )  ->			Optional[int]:
			__magic_name__	:   List[str]					     =					super()._prepare_for_class(lowercase_  ,					lowercase_  ,					return_labels=lowercase_      )

			if return_labels:
				if model_class in get_values(lowercase_      ):
					__magic_name__	:   Optional[Any]					     =					torch.zeros(
					    (self.model_tester.batch_size, self.model_tester.seq_length)  ,					dtype=torch.long  ,					device=lowercase_      )
					__magic_name__	:   int					     =					torch.zeros(
					    self.model_tester.batch_size  ,					dtype=torch.long  ,					device=lowercase_      )
			return inputs_dict







		def        __lowerCAmelCase		( self		:						Any      )  ->			str:
			__magic_name__	:   Optional[int]					     =					MegatronBertModelTester(self      )
			__magic_name__	:   Optional[Any]					     =					ConfigTester(self  ,					config_class=lowercase_  ,					hidden_size=37      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Union[str, Any]:
			self.config_tester.run_common_tests()







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Dict:
			__magic_name__	:   Tuple					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_model(*lowercase_      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[int]:
			__magic_name__	:   str					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowercase_      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			__magic_name__	:   Any					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowercase_      )







		def        __lowerCAmelCase		( self		:						int      )  ->			Union[str, Any]:
			__magic_name__	:   Union[str, Any]					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowercase_      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[Any]:
			__magic_name__	:   Dict					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowercase_      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			List[str]:
			__magic_name__	:   Any					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowercase_      )







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[Any]:
			__magic_name__	:   List[str]					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowercase_      )







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[str]:
			__magic_name__	:   Any					     =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowercase_      )







def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	return torch.tensor(
	    UpperCAmelCase__		,		dtype=torch.long		,		device=UpperCAmelCase__		,		)


lowerCAmelCase				:List[Any]        =      1E-4





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



		'''simple docstring'''







		@slow
		@unittest.skip('Model is not available.'      )
		def        __lowerCAmelCase		( self		:						int      )  ->			Dict:
			__magic_name__	:   List[str]					     =					"""nvidia/megatron-bert-uncased-345m"""
			if "MYDIR" in os.environ:
				__magic_name__	:   str					     =					os.path.join(os.environ['MYDIR']  ,					lowercase_      )
			__magic_name__	:   Optional[int]					     =					MegatronBertModel.from_pretrained(lowercase_      )
			model.to(lowercase_      )
			model.half()
			__magic_name__	:   List[str]					     =					_long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]      )
			with torch.no_grad():
				__magic_name__	:   str					     =					model(lowercase_      )[0]
			__magic_name__	:   int					     =					torch.Size((1, 9, 1024)      )
			self.assertEqual(output.shape  ,					lowercase_      )

			__magic_name__	:   Optional[int]					     =					[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
			for ii in range(3      ):
				for jj in range(3      ):
					__magic_name__	:   Dict					     =					output[0, ii, jj]
					__magic_name__	:   Optional[Any]					     =					expected[3 * ii + jj]
					__magic_name__	:   Optional[Any]					     =					"""ii={} jj={} a={} b={}""".format(lowercase_  ,					lowercase_  ,					lowercase_  ,					lowercase_      )
					self.assertTrue(math.isclose(lowercase_  ,					lowercase_  ,					rel_tol=lowercase_  ,					abs_tol=lowercase_      )  ,					msg=lowercase_      )



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase				:Any        =      TypeVar('''T''')





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (position - 1) // 2





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 1





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 2
class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   list[tuple[T, int]]					     =					[]
			__magic_name__	:   dict[T, int]					     =					{}
			__magic_name__	:   int					     =					0







		def __len__( self		:						Optional[int]      )  ->			int:
			return self.elements







		def __repr__( self		:						Optional[Any]      )  ->			str:
			return str(self.heap      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			bool:
			# Check if the priority queue is empty
			return self.elements == 0







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)      )
			__magic_name__	:   List[str]					     =					self.elements
			self.elements += 1
			self._bubble_up(_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
				self._swap_nodes(0  ,					self.elements - 1      )
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
				__magic_name__     ,    __magic_name__	:   Tuple					     =					self.heap[0]
				self._bubble_down(_A      )
			return elem







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Update the weight of the given key
			__magic_name__	:   Optional[Any]					     =					self.position_map[elem]
			__magic_name__	:   Any					     =					(elem, weight)
			if position > 0:
				__magic_name__	:   Optional[int]					     =					get_parent_position(_A      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[parent_position]
				if parent_weight > weight:
					self._bubble_up(_A      )
				else:
					self._bubble_down(_A      )
			else:
				self._bubble_down(_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			__magic_name__	:   Union[str, Any]					     =					self.position_map[elem]
			if curr_pos == 0:
				return None
			__magic_name__	:   Optional[Any]					     =					get_parent_position(_A      )
			__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[curr_pos]
			__magic_name__     ,    __magic_name__	:   int					     =					self.heap[parent_position]
			if parent_weight > weight:
				self._swap_nodes(_A  ,					_A      )
				return self._bubble_up(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			__magic_name__	:   Dict					     =					self.position_map[elem]
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.heap[curr_pos]
			__magic_name__	:   str					     =					get_child_left_position(_A      )
			__magic_name__	:   List[str]					     =					get_child_right_position(_A      )
			if child_left_position < self.elements and child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_left_position]
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_right_position]
				if child_right_weight < child_left_weight and child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			if child_left_position < self.elements:
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[child_left_position]
				if child_left_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			else:
				return None
			if child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   str					     =					self.heap[child_right_position]
				if child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						int  ,					_A		:						int      )  ->			None:
			# Swap the nodes at the given positions
			__magic_name__	:   List[Any]					     =					self.heap[nodea_pos][0]
			__magic_name__	:   Union[str, Any]					     =					self.heap[nodea_pos][0]
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					(
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__magic_name__	:   Optional[Any]					     =					nodea_pos
			__magic_name__	:   Optional[Any]					     =					nodea_pos







class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   dict[T, dict[T, int]]					     =					{}
			__magic_name__	:   int					     =					0







		def __repr__( self		:						str      )  ->			str:
			return str(self.connections      )







		def __len__( self		:						Union[str, Any]      )  ->			int:
			return self.nodes







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T      )  ->			None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
				__magic_name__	:   str					     =					{}
				self.nodes += 1







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						T  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_A      )
			self.add_node(_A      )
			__magic_name__	:   Any					     =					weight
			__magic_name__	:   Dict					     =					weight







def lowerCamelCase      (      lowerCAmelCase    :     GraphUndirectedWeighted[T]		,		):





	"""simple docstring"""







	__magic_name__	:   dict[T, int]					     =					{node: maxsize for node in graph.connections}
	__magic_name__	:   dict[T, T | None]					     =					{node: None for node in graph.connections}

	__magic_name__	:   MinPriorityQueue[T]					     =					MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowerCAmelCase		,		lowerCAmelCase		)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	__magic_name__	:   Any					     =					priority_queue.extract_min()
	__magic_name__	:   List[str]					     =					0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			__magic_name__	:   Any					     =					dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
			__magic_name__	:   Tuple					     =					node

    # running prim's algorithm
	while not priority_queue.is_empty():
		__magic_name__	:   Tuple					     =					priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				__magic_name__	:   int					     =					dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
				__magic_name__	:   Union[str, Any]					     =					node
	return dist, parent