





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   __lowerCAmelCase			(					_UpperCamelCase  ):





   @slow
   @require_torch
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"					,			"prajjwal1/bert-tiny"      )
      a__	:						List[Any]      		=  BertTokenizer.from_pretrained("bert-base-uncased"      )

      a__	:						List[Any]      		=  bertabert.config.encoder.vocab_size
      a__	:						Optional[int]      		=  tokenizer.sep_token_id
      a__	:						Optional[Any]      		=  tokenizer.cls_token_id
      a__	:						Any      		=  128

      a__	:						Optional[int]      		=  datasets.load_dataset("cnn_dailymail"					,			"3.0.0"					,			split="train[:1%]"      )
      a__	:						Optional[int]      		=  datasets.load_dataset("cnn_dailymail"					,			"3.0.0"					,			split="validation[:1%]"      )

      a__	:						Tuple      		=  train_dataset.select(range(32      )      )
      a__	:						Optional[Any]      		=  val_dataset.select(range(16      )      )

      a__	:						int      		=  4

      def _map_to_encoder_decoder_inputs(snake_case      ):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         a__	:						List[Any]      		=  tokenizer(batch["article"]					,			padding="max_length"					,			truncation=snake_case					,			max_length=512      )
         a__	:						int      		=  tokenizer(batch["highlights"]					,			padding="max_length"					,			truncation=snake_case					,			max_length=128      )
         a__	:						str      		=  inputs.input_ids
         a__	:						Any      		=  inputs.attention_mask

         a__	:						Tuple      		=  outputs.input_ids
         a__	:						List[str]      		=  outputs.input_ids.copy()
         a__	:						Dict      		=  [
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
         ]
         a__	:						int      		=  outputs.attention_mask

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

         return batch

      def _compute_metrics(snake_case      ):
         a__	:						Any      		=  pred.label_ids
         a__	:						int      		=  pred.predictions

         # all unnecessary tokens are removed
         a__	:						Tuple      		=  tokenizer.batch_decode(snake_case					,			skip_special_tokens=snake_case      )
         a__	:						Union[str, Any]      		=  tokenizer.batch_decode(snake_case					,			skip_special_tokens=snake_case      )

         a__	:						Tuple      		=  sum([int(pred_str[i] == label_str[i]      ) for i in range(len(snake_case      )      )]      ) / len(snake_case      )

         return {"accuracy": accuracy}

      # map train dataset
      a__	:						Dict      		=  train_dataset.map(
          _map_to_encoder_decoder_inputs					,			batched=snake_case					,			batch_size=snake_case					,			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__	:						Tuple      		=  val_dataset.map(
          _map_to_encoder_decoder_inputs					,			batched=snake_case					,			batch_size=snake_case					,			remove_columns=["article", "highlights"]					,			)
      val_dataset.set_format(
          type="torch"					,			columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]					,			)

      a__	:						Dict      		=  self.get_auto_remove_tmp_dir()

      a__	:						Optional[int]      		=  SeqaSeqTrainingArguments(
          output_dir=snake_case					,			per_device_train_batch_size=snake_case					,			per_device_eval_batch_size=snake_case					,			predict_with_generate=snake_case					,			evaluation_strategy="steps"					,			do_train=snake_case					,			do_eval=snake_case					,			warmup_steps=0					,			eval_steps=2					,			logging_steps=2					,			)

      # instantiate trainer
      a__	:						List[str]      		=  SeqaSeqTrainer(
          model=snake_case					,			args=snake_case					,			compute_metrics=_compute_metrics					,			train_dataset=snake_case					,			eval_dataset=snake_case					,			tokenizer=snake_case					,			)

      # start training
      trainer.train()








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

