




'''simple docstring'''







class     _a						:
       def __init__(			self		:			Dict     ,       lowercase		:			Any     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	name
           UpperCAmelCase     =	val
       def __str__(			self		:			List[str]       ):



           '''simple docstring'''

           return f"{self.__class__.__name__}({self.name}, {self.val})"







       def __lt__(			self		:			Dict     ,       lowercase		:			Any       ):



           '''simple docstring'''

           return self.val < other.val






class     _a						:
       def __init__(			self		:			List[Any]     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	{}
           UpperCAmelCase     =	{}
           UpperCAmelCase     =	self.build_heap(lowercase       )
       def __getitem__(			self		:			Tuple     ,       lowercase		:			int       ):



           '''simple docstring'''

           return self.get_value(lowercase       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           return (idx - 1) // 2
       def 					A   (			self		:			int     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           return idx * 2 + 1
       def 					A   (			self		:			Tuple     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           return idx * 2 + 2
       def 					A   (			self		:			List[str]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           return self.heap_dict[key]
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	len(lowercase       ) - 1
           UpperCAmelCase     =	self.get_parent_idx(lowercase       )

           for idx, i in enumerate(lowercase       ):
               UpperCAmelCase     =	idx
               UpperCAmelCase     =	i.val

           for i in range(lowercase     ,       -1     ,       -1       ):
               self.sift_down(lowercase     ,       lowercase       )
           return array
       def 					A   (			self		:			int     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           while True:
               UpperCAmelCase     =	self.get_left_child_idx(lowercase       )  # noqa: E741
               UpperCAmelCase     =	self.get_right_child_idx(lowercase       )

               UpperCAmelCase     =	idx
               if l < len(lowercase       ) and array[l] < array[idx]:
                   UpperCAmelCase     =	l
               if r < len(lowercase       ) 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 					A   (			self		:			int     ,       lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_parent_idx(lowercase       )
           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(lowercase       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           return self.heap[0]
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           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 					A   (			self		:			Optional[int]     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           self.heap.append(lowercase       )
           UpperCAmelCase     =	len(self.heap       ) - 1
           UpperCAmelCase     =	node.val
           self.sift_up(len(self.heap       ) - 1       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           return len(self.heap       ) == 0







       def 					A   (			self		:			Any     ,       lowercase		:			Tuple     ,       lowercase		:			str       ):



           '''simple docstring'''

           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									=Node('R', -1)
A									=Node('B', 6)
A									=Node('A', 3)
A									=Node('X', 1)
A									=Node('E', 4)

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

# Generating Min-Heap from array
A									=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 transformers import is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
       OpenAIGPTConfig,
       OpenAIGPTDoubleHeadsModel,
       OpenAIGPTForSequenceClassification,
       OpenAIGPTLMHeadModel,
       OpenAIGPTModel,
   )


class     _a						:
       def __init__(			self		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			str=13     ,       lowercase		:			int=7     ,       lowercase		:			Optional[int]=True     ,       lowercase		:			Dict=True     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			Union[str, Any]=99     ,       lowercase		:			Optional[Any]=32     ,       lowercase		:			List[Any]=5     ,       lowercase		:			List[Any]=4     ,       lowercase		:			int=37     ,       lowercase		:			int="gelu"     ,       lowercase		:			Tuple=0.1     ,       lowercase		:			Dict=0.1     ,       lowercase		:			Tuple=512     ,       lowercase		:			List[str]=16     ,       lowercase		:			Optional[int]=2     ,       lowercase		:			int=0.02     ,       lowercase		:			str=3     ,       lowercase		:			List[Any]=4     ,       lowercase		:			List[str]=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	num_choices
           UpperCAmelCase     =	scope
           UpperCAmelCase     =	self.vocab_size - 1
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

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

           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           if self.use_labels:
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.type_sequence_label_size       )
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels       )
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.num_choices       )

           UpperCAmelCase     =	OpenAIGPTConfig(
               vocab_size=self.vocab_size     ,       n_embd=self.hidden_size     ,       n_layer=self.num_hidden_layers     ,       n_head=self.num_attention_heads     ,       n_positions=self.max_position_embeddings     ,       pad_token_id=self.pad_token_id     ,       )

           UpperCAmelCase     =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,       2       )

           return (
               config,
               input_ids,
               head_mask,
               token_type_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )
       def 					A   (			self		:			Any     ,       lowercase		:			Tuple     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[str]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTModel(config=lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       head_mask=lowercase       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase       )

           self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			Optional[int]     ,       *lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTLMHeadModel(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.loss.shape     ,       ()       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTDoubleHeadsModel(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.loss.shape     ,       ()       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Dict     ,       lowercase		:			Any     ,       lowercase		:			Any     ,       lowercase		:			Optional[int]     ,       *lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	OpenAIGPTForSequenceClassification(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.type_sequence_label_size       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )







       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  
           )     =	config_and_inputs
           UpperCAmelCase     =	{
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict






@require_torch
class     _a						(			__a							,							__a							,							__a							,							unittest.TestCase				):
       __a       :  List[str]            =      (
           (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
           if is_torch_available()
           else ()
       )
       __a       :  List[Any]            =      (
           (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       __a       :  Dict            =      (
           {
               """feature-extraction""": OpenAIGPTModel,
               """text-classification""": OpenAIGPTForSequenceClassification,
               """text-generation""": OpenAIGPTLMHeadModel,
               """zero-shot""": OpenAIGPTForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Tuple     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
               # tiny config could not be created.
               return True

           return False
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	super()._prepare_for_class(lowercase     ,       lowercase     ,       return_labels=lowercase       )

           if return_labels:
               if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=lowercase     ,       )
                   UpperCAmelCase     =	inputs_dict['''labels''']
                   UpperCAmelCase     =	inputs_dict['''labels''']
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices)     ,       dtype=torch.long     ,       device=lowercase     ,       )
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=lowercase       )
           return inputs_dict
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       n_embd=37       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_double_lm_head_model(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase       )







       @slow
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	OpenAIGPTModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )






@require_torch
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''       )
           model.to(lowercase       )
           UpperCAmelCase     =	torch.tensor([[481, 4_735, 544]]     ,       dtype=torch.long     ,       device=lowercase       )  # the president is
           UpperCAmelCase     =	[
               481,
               4_735,
               544,
               246,
               963,
               870,
               762,
               239,
               244,
               40_477,
               244,
               249,
               719,
               881,
               487,
               544,
               240,
               244,
               603,
               481,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           UpperCAmelCase     =	model.generate(lowercase     ,       do_sample=lowercase       )
           self.assertListEqual(output_ids[0].tolist()     ,       lowercase       )




