






"""simple docstring"""






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
					import datasets





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	@slow
	@require_torch
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'  ,					'prajjwal1/bert-tiny'						)
					A__							 =       BertTokenizer.from_pretrained('bert-base-uncased'						)

					A__							 =       bertabert.config.encoder.vocab_size
					A__							 =       tokenizer.sep_token_id
					A__							 =       tokenizer.cls_token_id
					A__							 =       128

					A__							 =       datasets.load_dataset('cnn_dailymail'  ,					'3.0.0'  ,					split='train[:1%]'						)
					A__							 =       datasets.load_dataset('cnn_dailymail'  ,					'3.0.0'  ,					split='validation[:1%]'						)

					A__							 =       train_dataset.select(range(32						)						)
					A__							 =       val_dataset.select(range(16						)						)

					A__							 =       4

					def _map_to_encoder_decoder_inputs(__a						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									A__							 =       tokenizer(batch['article']  ,					padding='max_length'  ,					truncation=__a  ,					max_length=512						)
									A__							 =       tokenizer(batch['highlights']  ,					padding='max_length'  ,					truncation=__a  ,					max_length=128						)
									A__							 =       inputs.input_ids
									A__							 =       inputs.attention_mask

									A__							 =       outputs.input_ids
									A__							 =       outputs.input_ids.copy()
									A__							 =       [
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
									]
									A__							 =       outputs.attention_mask

									assert all(len(__a						) == 512 for x in inputs.input_ids						)
									assert all(len(__a						) == 128 for x in outputs.input_ids						)

									return batch

					def _compute_metrics(__a						):
									A__							 =       pred.label_ids
									A__							 =       pred.predictions

									# all unnecessary tokens are removed
									A__							 =       tokenizer.batch_decode(__a  ,					skip_special_tokens=__a						)
									A__							 =       tokenizer.batch_decode(__a  ,					skip_special_tokens=__a						)

									A__							 =       sum([int(pred_str[i] == label_str[i]						) for i in range(len(__a						)						)]						) / len(__a						)

									return {"accuracy": accuracy}

					# map train dataset
					A__							 =       train_dataset.map(
					    _map_to_encoder_decoder_inputs  ,					batched=__a  ,					batch_size=__a  ,					remove_columns=['article', 'highlights']  ,					)
					train_dataset.set_format(
					    type='torch'  ,					columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']  ,					)

					# same for validation dataset
					A__							 =       val_dataset.map(
					    _map_to_encoder_decoder_inputs  ,					batched=__a  ,					batch_size=__a  ,					remove_columns=['article', 'highlights']  ,					)
					val_dataset.set_format(
					    type='torch'  ,					columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']  ,					)

					A__							 =       self.get_auto_remove_tmp_dir()

					A__							 =       SeqaSeqTrainingArguments(
					    output_dir=__a  ,					per_device_train_batch_size=__a  ,					per_device_eval_batch_size=__a  ,					predict_with_generate=__a  ,					evaluation_strategy='steps'  ,					do_train=__a  ,					do_eval=__a  ,					warmup_steps=0  ,					eval_steps=2  ,					logging_steps=2  ,					)

					# instantiate trainer
					A__							 =       SeqaSeqTrainer(
					    model=__a  ,					args=__a  ,					compute_metrics=_compute_metrics  ,					train_dataset=__a  ,					eval_dataset=__a  ,					tokenizer=__a  ,					)

					# start training
					trainer.train()










"""simple docstring"""






class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       name
					A__							 =       val


	def __str__(      self						):


					"""simple docstring"""

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






	def __lt__(      self  ,					__a						):


					"""simple docstring"""

					return self.val < other.val





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       {}
					A__							 =       {}
					A__							 =       self.build_heap(__a						)


	def __getitem__(      self  ,					__a						):


					"""simple docstring"""

					return self.get_value(__a						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return (idx - 1) // 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 1


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.heap_dict[key]


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       len(__a						) - 1
					A__							 =       self.get_parent_idx(__a						)

					for idx, i in enumerate(__a						):
									A__							 =       idx
									A__							 =       i.val

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					while True:
									A__							 =       self.get_left_child_idx(__a						)  # noqa: E741
									A__							 =       self.get_right_child_idx(__a						)

									A__							 =       idx
									if l < len(__a						) and array[l] < array[idx]:
													A__							 =       l
									if r < len(__a						) and array[r] < array[smallest]:
													A__							 =       r

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       self.get_parent_idx(__a						)
					while p >= 0 and self.heap[p] > self.heap[idx]:
									A__ ,			A__							 =       self.heap[idx], self.heap[p]
									A__ ,			A__							 =       (
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									A__							 =       p
									A__							 =       self.get_parent_idx(__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.heap[0]


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__ ,			A__							 =       self.heap[-1], self.heap[0]
					A__ ,			A__							 =       (
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					self.heap.append(__a						)
					A__							 =       len(self.heap						) - 1
					A__							 =       node.val
					self.sift_up(len(self.heap						) - 1						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.heap						) == 0






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					A__							 =       new_value
					A__							 =       new_value
					self.sift_up(self.idx_of_element[node]						)





SCREAMING_SNAKE_CASE						:						List[str]					   =       Node('''R''', -1)
SCREAMING_SNAKE_CASE						:						Tuple					   =       Node('''B''', 6)
SCREAMING_SNAKE_CASE						:						List[Any]					   =       Node('''A''', 3)
SCREAMING_SNAKE_CASE						:						int					   =       Node('''X''', 1)
SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       Node('''E''', 4)

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

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE						:						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()


