


'''simple docstring'''






import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class 	a       (       unittest.TestCase    ):
 _lowerCAmelCase	   =		MODEL_FOR_MASKED_LM_MAPPING
 _lowerCAmelCase	   =		TF_MODEL_FOR_MASKED_LM_MAPPING
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      if is_torch_available():
           import torch

           torch.cuda.empty_cache()
 @require_tf
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							pipeline(task='fill-mask' ,			model='sshleifer/tiny-distilroberta-base' ,			top_k=2 ,			framework='tf'  )
      _a      =							unmasker('My name is <mask>'  )
      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              {'sequence': 'My name is grouped', 'score': 2.1e-05, 'token': 3_80_15, 'token_str': ' grouped'},
              {'sequence': 'My name is accuser', 'score': 2.1e-05, 'token': 2_55_06, 'token_str': ' accuser'},
          ] ,			)

      _a      =							unmasker('The largest city in France is <mask>'  )
      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              {
                  'sequence': 'The largest city in France is grouped',
                  'score': 2.1e-05,
                  'token': 3_80_15,
                  'token_str': ' grouped',
              },
              {
                  'sequence': 'The largest city in France is accuser',
                  'score': 2.1e-05,
                  'token': 2_55_06,
                  'token_str': ' accuser',
              },
          ] ,			)

      _a      =							unmasker('My name is <mask>' ,			targets=[' Patrick', ' Clara', ' Teven'] ,			top_k=3  )
      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 1_36_06, 'token_str': ' Clara'},
              {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 34_99, 'token_str': ' Patrick'},
              {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 29_41, 'token_str': ' Te'},
          ] ,			)
 @require_torch
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							pipeline(task='fill-mask' ,			model='sshleifer/tiny-distilroberta-base' ,			top_k=2 ,			framework='pt'  )

      _a      =							unmasker('My name is <mask>'  )
      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              {'sequence': 'My name is Maul', 'score': 2.2e-05, 'token': 3_56_76, 'token_str': ' Maul'},
              {'sequence': 'My name isELS', 'score': 2.2e-05, 'token': 1_64_16, 'token_str': 'ELS'},
          ] ,			)

      _a      =							unmasker('The largest city in France is <mask>'  )
      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              {
                  'sequence': 'The largest city in France is Maul',
                  'score': 2.2e-05,
                  'token': 3_56_76,
                  'token_str': ' Maul',
              },
              {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 1_64_16, 'token_str': 'ELS'},
          ] ,			)

      _a      =							unmasker('My name is <mask>' ,			targets=[' Patrick', ' Clara', ' Teven'] ,			top_k=3  )
      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              {'sequence': 'My name is Patrick', 'score': 2.1e-05, 'token': 34_99, 'token_str': ' Patrick'},
              {'sequence': 'My name is Te', 'score': 2e-05, 'token': 29_41, 'token_str': ' Te'},
              {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 1_36_06, 'token_str': ' Clara'},
          ] ,			)

      _a      =							unmasker('My name is <mask> <mask>' ,			top_k=2  )

      self.assertEqual(
          nested_simplify(__magic_name__ ,			decimals=6  ) ,			[
              [
                  {
                      'score': 2.2e-05,
                      'token': 3_56_76,
                      'token_str': ' Maul',
                      'sequence': '<s>My name is Maul<mask></s>',
                  },
                  {'score': 2.2e-05, 'token': 1_64_16, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
              ],
              [
                  {
                      'score': 2.2e-05,
                      'token': 3_56_76,
                      'token_str': ' Maul',
                      'sequence': '<s>My name is<mask> Maul</s>',
                  },
                  {'score': 2.2e-05, 'token': 1_64_16, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
              ],
          ] ,			)
 @require_torch_gpu
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							pipeline('fill-mask' ,			model='hf-internal-testing/tiny-random-distilbert' ,			device=0 ,			framework='pt'  )

      # convert model to fp16
      pipe.model.half()

      _a      =							pipe('Paris is the [MASK] of France.'  )
      # We actually don't care about the result, we just want to make sure
      # it works, meaning the float16 tensor got casted back to float32
      # for postprocessing.
      self.assertIsInstance(__magic_name__ ,			__magic_name__  )
 @slow
 @require_torch
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							pipeline(task='fill-mask' ,			model='distilroberta-base' ,			top_k=2 ,			framework='pt'  )
      self.run_large_test(__magic_name__  )
 @slow
 @require_tf
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							pipeline(task='fill-mask' ,			model='distilroberta-base' ,			top_k=2 ,			framework='tf'  )
      self.run_large_test(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[int]:
      _a      =							unmasker('My name is <mask>'  )
      self.assertEqual(
          nested_simplify(__magic_name__  ) ,			[
              {'sequence': 'My name is John', 'score': 0.0_0_8, 'token': 6_10, 'token_str': ' John'},
              {'sequence': 'My name is Chris', 'score': 0.0_0_7, 'token': 15_73, 'token_str': ' Chris'},
          ] ,			)
      _a      =							unmasker('The largest city in France is <mask>'  )
      self.assertEqual(
          nested_simplify(__magic_name__  ) ,			[
              {
                  'sequence': 'The largest city in France is Paris',
                  'score': 0.2_5_1,
                  'token': 22_01,
                  'token_str': ' Paris',
              },
              {
                  'sequence': 'The largest city in France is Lyon',
                  'score': 0.2_1_4,
                  'token': 1_27_90,
                  'token_str': ' Lyon',
              },
          ] ,			)

      _a      =							unmasker('My name is <mask>' ,			targets=[' Patrick', ' Clara', ' Teven'] ,			top_k=3  )
      self.assertEqual(
          nested_simplify(__magic_name__  ) ,			[
              {'sequence': 'My name is Patrick', 'score': 0.0_0_5, 'token': 34_99, 'token_str': ' Patrick'},
              {'sequence': 'My name is Clara', 'score': 0.0_0_0, 'token': 1_36_06, 'token_str': ' Clara'},
              {'sequence': 'My name is Te', 'score': 0.0_0_0, 'token': 29_41, 'token_str': ' Te'},
          ] ,			)
 @require_torch
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							pipeline(task='fill-mask' ,			model='sshleifer/tiny-distilroberta-base' ,			framework='pt'  )
      _a      =							None
      _a      =							None
      self.run_pipeline_test(__magic_name__ ,			[]  )
 @require_tf
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							pipeline(task='fill-mask' ,			model='sshleifer/tiny-distilroberta-base' ,			framework='tf'  )
      _a      =							None
      _a      =							None
      self.run_pipeline_test(__magic_name__ ,			[]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							str:
      if tokenizer is None or tokenizer.mask_token_id is None:
           self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'  )

      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__  )
      _a      =							[
          f'This is another {tokenizer.mask_token} test',
      ]
      return fill_masker, examples
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							str:
      _a      =							fill_masker.tokenizer
      _a      =							fill_masker.model

      _a      =							fill_masker(
          f'This is a {tokenizer.mask_token}' ,			)
      self.assertEqual(
          __magic_name__ ,			[
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
          ] ,			)

      _a      =							fill_masker([f'This is a {tokenizer.mask_token}']  )
      self.assertEqual(
          __magic_name__ ,			[
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
          ] ,			)

      _a      =							fill_masker([f'This is a {tokenizer.mask_token}', f'Another {tokenizer.mask_token} great test.']  )
      self.assertEqual(
          __magic_name__ ,			[
              [
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              ],
              [
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              ],
          ] ,			)

      with self.assertRaises(__magic_name__  ):
           fill_masker([None]  )
      # No mask_token is not supported
      with self.assertRaises(__magic_name__  ):
           fill_masker('This is'  )

      self.run_test_top_k(__magic_name__ ,			__magic_name__  )
      self.run_test_targets(__magic_name__ ,			__magic_name__  )
      self.run_test_top_k_targets(__magic_name__ ,			__magic_name__  )
      self.fill_mask_with_duplicate_targets_and_top_k(__magic_name__ ,			__magic_name__  )
      self.fill_mask_with_multiple_masks(__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Tuple:
      _a      =							tokenizer.get_vocab()
      _a      =							sorted(vocab.keys()  )[:2]
      # Pipeline argument
      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__ ,			targets=__magic_name__  )
      _a      =							fill_masker(f'This is a {tokenizer.mask_token}'  )
      self.assertEqual(
          __magic_name__ ,			[
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
          ] ,			)
      _a      =							{vocab[el] for el in targets}
      self.assertEqual({el['token'] for el in outputs} ,			__magic_name__  )
      _a      =							[tokenizer.decode([x]  ) for x in target_ids]
      self.assertEqual({el['token_str'] for el in outputs} ,			set(__magic_name__  )  )

      # Call argument
      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__  )
      _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			targets=__magic_name__  )
      self.assertEqual(
          __magic_name__ ,			[
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
          ] ,			)
      _a      =							{vocab[el] for el in targets}
      self.assertEqual({el['token'] for el in outputs} ,			__magic_name__  )
      _a      =							[tokenizer.decode([x]  ) for x in target_ids]
      self.assertEqual({el['token_str'] for el in outputs} ,			set(__magic_name__  )  )

      # Score equivalence
      _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			targets=__magic_name__  )
      _a      =							[top_mask['token_str'] for top_mask in outputs]
      _a      =							[top_mask['score'] for top_mask in outputs]

      # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
      if set(__magic_name__  ) == set(__magic_name__  ):
           _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			targets=__magic_name__  )
           _a      =							[top_mask['score'] for top_mask in unmasked_targets]
           self.assertEqual(nested_simplify(__magic_name__  ) ,			nested_simplify(__magic_name__  )  )

      # Raises with invalid
      with self.assertRaises(__magic_name__  ):
           _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			targets=[]  )
      # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
      if "" not in tokenizer.get_vocab():
           with self.assertRaises(__magic_name__  ):
                _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			targets=['']  )
           with self.assertRaises(__magic_name__  ):
                _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			targets=''  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Union[str, Any]:
      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__ ,			top_k=2  )
      _a      =							fill_masker(f'This is a {tokenizer.mask_token}'  )
      self.assertEqual(
          __magic_name__ ,			[
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
          ] ,			)

      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__  )
      _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			top_k=2  )
      self.assertEqual(
          __magic_name__ ,			[
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
          ] ,			)
      self.assertEqual(nested_simplify(__magic_name__  ) ,			nested_simplify(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							int:
      _a      =							tokenizer.get_vocab()
      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__  )

      # top_k=2, ntargets=3
      _a      =							sorted(vocab.keys()  )[:3]
      _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			top_k=2 ,			targets=__magic_name__  )

      # If we use the most probably targets, and filter differently, we should still
      # have the same results
      _a      =							[el['token_str'] for el in sorted(__magic_name__ ,			key=lambda __magic_name__  : x["score"] ,			reverse=__magic_name__  )]
      # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
      if set(__magic_name__  ).issubset(__magic_name__  ):
           _a      =							fill_masker(f'This is a {tokenizer.mask_token}' ,			top_k=3 ,			targets=__magic_name__  )
           # They should yield exactly the same result
           self.assertEqual(nested_simplify(__magic_name__  ) ,			nested_simplify(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__  )
      _a      =							tokenizer.get_vocab()
      # String duplicates + id duplicates
      _a      =							sorted(vocab.keys()  )[:3]
      _a      =							[targets[0], targets[1], targets[0], targets[2], targets[1]]
      _a      =							fill_masker(f'My name is {tokenizer.mask_token}' ,			targets=__magic_name__ ,			top_k=10  )

      # The target list contains duplicates, so we can't output more
      # than them
      self.assertEqual(len(__magic_name__  ) ,			3  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      _a      =							FillMaskPipeline(model=__magic_name__ ,			tokenizer=__magic_name__  )

      _a      =							fill_masker(
          f'This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}' ,			top_k=2  )
      self.assertEqual(
          __magic_name__ ,			[
              [
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              ],
              [
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              ],
              [
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
                  {'sequence': ANY(__magic_name__  ), 'score': ANY(__magic_name__  ), 'token': ANY(__magic_name__  ), 'token_str': ANY(__magic_name__  )},
              ],
          ] ,			)





'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_   :      Optional[Any]				    =      TypeVar("T")






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (position - 1) // 2






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 1






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 2




class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							[]
      _a      =							{}
      _a      =							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 ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)  )
      _a      =							self.elements
      self.elements += 1
      self._bubble_up(__magic_name__  )
 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  )
      _a ,				_a      =							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
           _a ,				_a      =							self.heap[0]
           self._bubble_down(__magic_name__  )
      return elem
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Update the weight of the given key
      _a      =							self.position_map[elem]
      _a      =							(elem, weight)
      if position > 0:
           _a      =							get_parent_position(__magic_name__  )
           _a ,				_a      =							self.heap[parent_position]
           if parent_weight > weight:
                self._bubble_up(__magic_name__  )
           else:
                self._bubble_down(__magic_name__  )
      else:
           self._bubble_down(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      _a      =							self.position_map[elem]
      if curr_pos == 0:
           return None
      _a      =							get_parent_position(__magic_name__  )
      _a ,				_a      =							self.heap[curr_pos]
      _a ,				_a      =							self.heap[parent_position]
      if parent_weight > weight:
           self._swap_nodes(__magic_name__ ,			__magic_name__  )
           return self._bubble_up(__magic_name__  )
      return None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      _a      =							self.position_map[elem]
      _a ,				_a      =							self.heap[curr_pos]
      _a      =							get_child_left_position(__magic_name__  )
      _a      =							get_child_right_position(__magic_name__  )
      if child_left_position < self.elements and child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      if child_left_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           if child_left_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      else:
           return None
      if child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      return None




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





class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							{}
      _a      =							0
 def __repr__(	self  )   ->							str:
      return str(self.connections  )
 def __len__(	self  )   ->							int:
      return self.nodes
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
           _a      =							{}
           self.nodes += 1




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






def 		_A			(lowerCAmelCase__					:GraphUndirectedWeighted[T]			,  )				->					tuple[dict[T, int], dict[T, T | None]]:




     '''simple docstring'''



     _a      =							{node: maxsize for node in graph.connections}
     _a      =							{node: None for node in graph.connections}

     _a      =							MinPriorityQueue()
     for node, weight in dist.items():
          priority_queue.push(lowerCAmelCase__			,  lowerCAmelCase__						)

     if priority_queue.is_empty():
          return dist, parent

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

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

