






"""simple docstring"""





class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    name
       UpperCamelCase__         =    val

   def __str__(					self      )		->       Any:
       return F'{self.__class__.__name__}({self.name}, {self.val})'







   def __lt__(					self ,				snake_case_      )		->       List[Any]:
       return self.val < other.val







class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_      )		->       Optional[Any]:
       UpperCamelCase__         =    {}
       UpperCamelCase__         =    {}
       UpperCamelCase__         =    self.build_heap(snake_case_      )

   def __getitem__(					self ,				snake_case_      )		->       Optional[Any]:
       return self.get_value(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Union[str, Any]:
       return (idx - 1) // 2

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:
       return idx * 2 + 1

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Union[str, Any]:
       return idx * 2 + 2

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[Any]:
       return self.heap_dict[key]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:
       UpperCamelCase__         =    len(snake_case_      ) - 1
       UpperCamelCase__         =    self.get_parent_idx(snake_case_      )

       for idx, i in enumerate(snake_case_      ):
           UpperCamelCase__         =    idx
           UpperCamelCase__         =    i.val

       for i in range(snake_case_ ,				-1 ,				-1      ):
           self.sift_down(snake_case_ ,				snake_case_      )
       return array

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Any:
       while True:
           UpperCamelCase__         =    self.get_left_child_idx(snake_case_      )  # noqa: E741
           UpperCamelCase__         =    self.get_right_child_idx(snake_case_      )

           UpperCamelCase__         =    idx
           if l < len(snake_case_      ) and array[l] < array[idx]:
               UpperCamelCase__         =    l
           if r < len(snake_case_      ) and array[r] < array[smallest]:
               UpperCamelCase__         =    r

           if smallest != idx:
               UpperCamelCase__       ,						UpperCamelCase__         =    array[smallest], array[idx]
               (
                   (
                   UpperCamelCase__
               )       ,						(
                   UpperCamelCase__
               )       ,						
               )         =    (
                   self.idx_of_element[array[smallest]],
                   self.idx_of_element[array[idx]],
               )
               UpperCamelCase__         =    smallest
           else:
               break

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:
       UpperCamelCase__         =    self.get_parent_idx(snake_case_      )
       while p >= 0 and self.heap[p] > self.heap[idx]:
           UpperCamelCase__       ,						UpperCamelCase__         =    self.heap[idx], self.heap[p]
           UpperCamelCase__       ,						UpperCamelCase__         =    (
               self.idx_of_element[self.heap[idx]],
               self.idx_of_element[self.heap[p]],
           )
           UpperCamelCase__         =    p
           UpperCamelCase__         =    self.get_parent_idx(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       return self.heap[0]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.heap[-1], self.heap[0]
       UpperCamelCase__       ,						UpperCamelCase__         =    (
           self.idx_of_element[self.heap[-1]],
           self.idx_of_element[self.heap[0]],
       )

       UpperCamelCase__         =    self.heap.pop()
       del self.idx_of_element[x]
       self.sift_down(0 ,				self.heap      )
       return x

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       self.heap.append(snake_case_      )
       UpperCamelCase__         =    len(self.heap      ) - 1
       UpperCamelCase__         =    node.val
       self.sift_up(len(self.heap      ) - 1      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       return len(self.heap      ) == 0







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Tuple:
       assert (
           self.heap[self.idx_of_element[node]].val > new_value
       ), "newValue must be less that current value"
       UpperCamelCase__         =    new_value
       UpperCamelCase__         =    new_value
       self.sift_up(self.idx_of_element[node]      )







A__					: Any=	Node("""R""", -1)
A__					: Any=	Node("""B""", 6)
A__					: Optional[int]=	Node("""A""", 3)
A__					: List[str]=	Node("""X""", 1)
A__					: int=	Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
A__					: str=	MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
  print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
  print(i)

if __name__ == "__main__":
  import doctest

  doctest.testmod()












"""simple docstring"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


A__					: List[Any]=	["""bert-base-uncased""", """bert-base-cased"""]
A__					: Optional[int]=	"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():
  class    __lowerCamelCase    (  tf.keras.Model  ):

     def __init__(					self ,				snake_case_      )		->       Optional[int]:
         super().__init__()
         UpperCamelCase__         =    tokenizer
         UpperCamelCase__         =    AutoConfig.from_pretrained(snake_case_      )
         UpperCamelCase__         =    TFAutoModel.from_config(snake_case_      )







     def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
         UpperCamelCase__         =    self.tokenizer(snake_case_      )
         UpperCamelCase__         =    self.bert(**snake_case_      )
         return out["pooler_output"]
@require_tf
@require_tensorflow_text
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       super().setUp()

       UpperCamelCase__         =    [
           BertTokenizer.from_pretrained(snake_case_      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
       ]  # repeat for when fast_bert_tokenizer=false
       UpperCamelCase__         =    [TFBertTokenizer.from_pretrained(snake_case_      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
           TFBertTokenizer.from_pretrained(snake_case_ ,				use_fast_bert_tokenizer=snake_case_      )
           for checkpoint in TOKENIZER_CHECKPOINTS
       ]
       assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

       UpperCamelCase__         =    [
           'This is a straightforward English test sentence.',
           'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
           'Now we\'re going to add some Chinese: 一 二 三 一二三',
           'And some much more rare Chinese: 齉 堃 齉堃',
           'Je vais aussi écrire en français pour tester les accents',
           'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
       ]
       UpperCamelCase__         =    list(zip(self.test_sentences ,				self.test_sentences[::-1]      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       for tokenizer, tf_tokenizer in zip(self.tokenizers ,				self.tf_tokenizers      ):
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tokenizer(snake_case_ ,				return_tensors='tf' ,				padding='longest'      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in python_outputs.keys():
                   self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape      )      )
                   self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,				tf.intaa      ) == tf_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf_tokenizer(self.paired_sentences      )
           UpperCamelCase__         =    tf_tokenizer(
               text=[sentence[0] for sentence in self.paired_sentences] ,				text_pair=[sentence[1] for sentence in self.paired_sentences] ,				)
           for key in merged_outputs.keys():
               self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,				tf.intaa      ) == separated_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf.function(snake_case_      )
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tf.constant(snake_case_      )
               UpperCamelCase__         =    compiled_tokenizer(snake_case_      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in eager_outputs.keys():
                   self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    ModelToSave(tokenizer=snake_case_      )
           UpperCamelCase__         =    tf.convert_to_tensor(self.test_sentences      )
           UpperCamelCase__         =    model(snake_case_      )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
               UpperCamelCase__         =    Path(snake_case_      ) / 'saved.model'
               model.save(snake_case_      )
               UpperCamelCase__         =    tf.keras.models.load_model(snake_case_      )
           UpperCamelCase__         =    loaded_model(snake_case_      )
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output      )      ) ,				1E-5      )




