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      (   A__      ):
 @slow
 @require_torch
 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       int			     =EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''						,      '''prajjwal1/bert-tiny'''  )
       UpperCAmelCase			:       Dict			     =BertTokenizer.from_pretrained('''bert-base-uncased'''  )

       UpperCAmelCase			:       Union[str, Any]			     =bertabert.config.encoder.vocab_size
       UpperCAmelCase			:       Optional[Any]			     =tokenizer.sep_token_id
       UpperCAmelCase			:       Dict			     =tokenizer.cls_token_id
       UpperCAmelCase			:       Optional[Any]			     =128

       UpperCAmelCase			:       Optional[int]			     =datasets.load_dataset('''cnn_dailymail'''						,      '''3.0.0'''						,      split='''train[:1%]'''  )
       UpperCAmelCase			:       Any			     =datasets.load_dataset('''cnn_dailymail'''						,      '''3.0.0'''						,      split='''validation[:1%]'''  )

       UpperCAmelCase			:       Tuple			     =train_dataset.select(range(32  )  )
       UpperCAmelCase			:       int			     =val_dataset.select(range(16  )  )

       UpperCAmelCase			:       List[str]			     =4

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

             UpperCAmelCase			:       Dict			     =outputs.input_ids
             UpperCAmelCase			:       Optional[int]			     =outputs.input_ids.copy()
             UpperCAmelCase			:       Union[str, Any]			     =[
                 [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
             ]
             UpperCAmelCase			:       List[str]			     =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(snake_case__  ):
             UpperCAmelCase			:       Union[str, Any]			     =pred.label_ids
             UpperCAmelCase			:       str			     =pred.predictions

             # all unnecessary tokens are removed
             UpperCAmelCase			:       List[str]			     =tokenizer.batch_decode(_A						,      skip_special_tokens=_A  )
             UpperCAmelCase			:       List[Any]			     =tokenizer.batch_decode(_A						,      skip_special_tokens=_A  )

             UpperCAmelCase			:       Any			     =sum([int(pred_str[i] == label_str[i]  ) for i in range(len(_A  )  )]  ) / len(_A  )

             return {"accuracy": accuracy}

       # map train dataset
       UpperCAmelCase			:       int			     =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
       UpperCAmelCase			:       Optional[int]			     =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''']						,      )

       UpperCAmelCase			:       Tuple			     =self.get_auto_remove_tmp_dir()

       UpperCAmelCase			:       List[Any]			     =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
       UpperCAmelCase			:       Union[str, Any]			     =SeqaSeqTrainer(
           model=_A						,      args=_A						,      compute_metrics=_compute_metrics						,      train_dataset=_A						,      eval_dataset=_A						,      tokenizer=_A						,      )

       # start training
       trainer.train()

from collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							if node.start == i and node.end == j:
									return node.val

							if i <= node.mid:
									if j <= node.mid:
											# range in left child tree
											return self._query_range(node.left,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    queue.get()
											yield node

											if node.left is not None:
													queue.put(node.left    )

											if node.right is not None:
													queue.put(node.right    )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				__lowerCamelCase					:	int       =  SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()
