

import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
         GPTNeoXModel,
     )



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=13   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=99   ,SCREAMING_SNAKE_CASE_=64   ,SCREAMING_SNAKE_CASE_=5   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=37   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=512   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=None   ,):

      '''simple docstring'''
      snake_case							:		Dict        =		parent
      snake_case							:		Dict        =		batch_size
      snake_case							:		List[str]        =		seq_length
      snake_case							:		Dict        =		is_training
      snake_case							:		int        =		use_input_mask
      snake_case							:		Any        =		use_token_type_ids
      snake_case							:		str        =		use_labels
      snake_case							:		List[str]        =		vocab_size
      snake_case							:		int        =		hidden_size
      snake_case							:		List[str]        =		num_hidden_layers
      snake_case							:		Optional[Any]        =		num_attention_heads
      snake_case							:		Optional[int]        =		intermediate_size
      snake_case							:		Optional[int]        =		hidden_act
      snake_case							:		Union[str, Any]        =		hidden_dropout_prob
      snake_case							:		List[str]        =		attention_probs_dropout_prob
      snake_case							:		Union[str, Any]        =		max_position_embeddings
      snake_case							:		Dict        =		type_vocab_size
      snake_case							:		str        =		type_sequence_label_size
      snake_case							:		Dict        =		initializer_range
      snake_case							:		Optional[int]        =		num_labels
      snake_case							:		List[str]        =		num_choices
      snake_case							:		Tuple        =		scope
      snake_case							:		Union[str, Any]        =		vocab_size - 1





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size    )

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

      snake_case							:		int        =		None
      if self.use_labels:
          snake_case							:		Tuple        =		ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels    )

      snake_case							:		Dict        =		self.get_config()

      return config, input_ids, input_mask, token_labels





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return GPTNeoXConfig(
          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   ,is_decoder=_UpperCAmelCase   ,initializer_range=self.initializer_range   ,pad_token_id=self.pad_token_id   ,)





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		List[str]        =		self.prepare_config_and_inputs()

      snake_case							:		Dict        =		True

      return config, input_ids, input_mask, token_labels





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		GPTNeoXModel(config=_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()
      snake_case							:		str        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase    )
      snake_case							:		str        =		model(_UpperCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		True
      snake_case							:		Optional[Any]        =		GPTNeoXModel(_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()
      snake_case							:		Union[str, Any]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		GPTNeoXForCausalLM(config=_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()
      snake_case							:		List[Any]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,labels=_UpperCAmelCase    )
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		self.num_labels
      snake_case							:		Dict        =		GPTNeoXForQuestionAnswering(_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()
      snake_case							:		Optional[int]        =		model(_UpperCAmelCase   ,attention_mask=_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    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.num_labels
      snake_case							:		int        =		GPTNeoXForSequenceClassification(_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()
      snake_case							:		int        =		ids_tensor([self.batch_size]   ,self.type_sequence_label_size    )
      snake_case							:		Optional[Any]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,labels=_UpperCAmelCase    )
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.num_labels
      snake_case							:		Optional[int]        =		GPTNeoXForTokenClassification(_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()
      snake_case							:		List[str]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,labels=_UpperCAmelCase    )
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		True
      snake_case							:		Optional[int]        =		GPTNeoXForCausalLM(config=_UpperCAmelCase    )
      model.to(_UpperCAmelCase    )
      model.eval()

      # first forward pass
      snake_case							:		List[str]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,use_cache=_UpperCAmelCase    )
      snake_case							:		str        =		outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      snake_case							:		Optional[int]        =		ids_tensor((self.batch_size, 3)   ,config.vocab_size    )
      snake_case							:		Optional[int]        =		ids_tensor((self.batch_size, 3)   ,vocab_size=2    )

      # append to next input_ids and
      snake_case							:		Union[str, Any]        =		torch.cat([input_ids, next_tokens]   ,dim=-1    )
      snake_case							:		str        =		torch.cat([input_mask, next_mask]   ,dim=-1    )

      snake_case							:		List[str]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,output_hidden_states=_UpperCAmelCase    )
      snake_case							:		Union[str, Any]        =		output_from_no_past["""hidden_states"""][0]
      snake_case							:		Tuple        =		model(
          _UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,past_key_values=_UpperCAmelCase   ,output_hidden_states=_UpperCAmelCase   ,)["""hidden_states"""][0]

      # select random slice
      snake_case							:		Dict        =		ids_tensor((1,)   ,output_from_past.shape[-1]    ).item()
      snake_case							:		int        =		output_from_no_past[:, -3:, random_slice_idx].detach()
      snake_case							:		Union[str, Any]        =		output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(_UpperCAmelCase   ,_UpperCAmelCase   ,atol=1E-3    )    )





  def    snake_case_       (  self    ):

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







@require_torch
class 						_A     (				UpperCAmelCase__						,    UpperCAmelCase__						,    UpperCAmelCase__						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Tuple					=   (
      (
          GPTNeoXModel,
          GPTNeoXForCausalLM,
          GPTNeoXForQuestionAnswering,
          GPTNeoXForSequenceClassification,
          GPTNeoXForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  __lowerCamelCase			:      str					=   (GPTNeoXForCausalLM,) if is_torch_available() else ()
  __lowerCamelCase			:      Optional[Any]					=   (
      {
          '''feature-extraction''': GPTNeoXModel,
          '''question-answering''': GPTNeoXForQuestionAnswering,
          '''text-classification''': GPTNeoXForSequenceClassification,
          '''text-generation''': GPTNeoXForCausalLM,
          '''token-classification''': GPTNeoXForTokenClassification,
          '''zero-shot''': GPTNeoXForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  __lowerCamelCase			:      str					=   False
  __lowerCamelCase			:      Tuple					=   False
  __lowerCamelCase			:      Any					=   False
  __lowerCamelCase			:      int					=   False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		GPTNeoXModelTester(self    )
      snake_case							:		Optional[int]        =		ConfigTester(self   ,config_class=_UpperCAmelCase   ,hidden_size=64   ,num_attention_heads=8    )





  def    snake_case_       (  self    ):

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Dict        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Optional[Any]        =		self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # This regression test was failing with PyTorch < 1.3
      snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Optional[Any]        =		self.model_tester.prepare_config_and_inputs_for_decoder()

      snake_case							:		Dict        =		None

      self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		int        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase    )





  @unittest.skip(reason="""Feed forward chunking is not implemented"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @parameterized.expand([("""linear""",), ("""dynamic""",)]    )
  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Union[str, Any]        =		self.model_tester.prepare_config_and_inputs_for_common()
      snake_case							:		Optional[Any]        =		ids_tensor([1, 10]   ,config.vocab_size    )
      snake_case							:		Optional[Any]        =		ids_tensor([1, int(config.max_position_embeddings * 1.5    )]   ,config.vocab_size    )

      set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
      snake_case							:		Optional[Any]        =		GPTNeoXModel(_UpperCAmelCase    )
      original_model.to(_UpperCAmelCase    )
      original_model.eval()
      snake_case							:		Optional[Any]        =		original_model(_UpperCAmelCase    ).last_hidden_state
      snake_case							:		str        =		original_model(_UpperCAmelCase    ).last_hidden_state

      set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
      snake_case							:		str        =		{"""type""": scaling_type, """factor""": 10.0}
      snake_case							:		Optional[Any]        =		GPTNeoXModel(_UpperCAmelCase    )
      scaled_model.to(_UpperCAmelCase    )
      scaled_model.eval()
      snake_case							:		List[str]        =		scaled_model(_UpperCAmelCase    ).last_hidden_state
      snake_case							:		Union[str, Any]        =		scaled_model(_UpperCAmelCase    ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
          self.assertTrue(torch.allclose(_UpperCAmelCase   ,_UpperCAmelCase   ,atol=1E-5    )    )
      else:
          self.assertFalse(torch.allclose(_UpperCAmelCase   ,_UpperCAmelCase   ,atol=1E-5    )    )

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(_UpperCAmelCase   ,_UpperCAmelCase   ,atol=1E-5    )    )







@require_torch
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )
      for checkpointing in [True, False]:
          snake_case							:		Any        =		GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )

          if checkpointing:
              model.gradient_checkpointing_enable()
          else:
              model.gradient_checkpointing_disable()
          model.to(_UpperCAmelCase    )

          snake_case							:		Tuple        =		tokenizer("""My favorite food is"""   ,return_tensors="""pt"""    ).to(_UpperCAmelCase    )
          # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
          # See: https://github.com/huggingface/transformers/pull/24193
          snake_case							:		Union[str, Any]        =		"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure"""

          snake_case							:		str        =		model.generate(**_UpperCAmelCase   ,do_sample=_UpperCAmelCase   ,max_new_tokens=20    )
          snake_case							:		Tuple        =		tokenizer.batch_decode(_UpperCAmelCase    )[0]

          self.assertEqual(_UpperCAmelCase   ,_UpperCAmelCase    )











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




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




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




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




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




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





