





"""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  _SCREAMING_SNAKE_CASE(       A  ):




       @slow
       @require_torch
       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''		,'''prajjwal1/bert-tiny'''							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  BertTokenizer.from_pretrained('''bert-base-uncased'''							)

             __SCREAMING_SNAKE_CASE       :Any		    =  bertabert.config.encoder.vocab_size
             __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer.sep_token_id
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer.cls_token_id
             __SCREAMING_SNAKE_CASE       :Any		    =  1_28

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

             __SCREAMING_SNAKE_CASE       :str		    =  train_dataset.select(range(32							)							)
             __SCREAMING_SNAKE_CASE       :Any		    =  val_dataset.select(range(16							)							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  4

             def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE__							):
                   # Tokenizer will automatically set [BOS] <text> [EOS]
                   __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer(batch['''article''']		,padding='''max_length'''		,truncation=SCREAMING_SNAKE_CASE__		,max_length=5_12							)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer(batch['''highlights''']		,padding='''max_length'''		,truncation=SCREAMING_SNAKE_CASE__		,max_length=1_28							)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  inputs.input_ids
                   __SCREAMING_SNAKE_CASE       :str		    =  inputs.attention_mask

                   __SCREAMING_SNAKE_CASE       :Dict		    =  outputs.input_ids
                   __SCREAMING_SNAKE_CASE       :Any		    =  outputs.input_ids.copy()
                   __SCREAMING_SNAKE_CASE       :Dict		    =  [
                       [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
                   ]
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  outputs.attention_mask

                   assert all(len(SCREAMING_SNAKE_CASE__							) == 5_12 for x in inputs.input_ids							)
                   assert all(len(SCREAMING_SNAKE_CASE__							) == 1_28 for x in outputs.input_ids							)

                   return batch

             def _compute_metrics(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  pred.label_ids
                   __SCREAMING_SNAKE_CASE       :int		    =  pred.predictions

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

                   __SCREAMING_SNAKE_CASE       :int		    =  sum([int(pred_str[i] == label_str[i]							) for i in range(len(SCREAMING_SNAKE_CASE__							)							)]							) / len(SCREAMING_SNAKE_CASE__							)

                   return {"accuracy": accuracy}

             # map train dataset
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  train_dataset.map(
                 _map_to_encoder_decoder_inputs		,batched=SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_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
             __SCREAMING_SNAKE_CASE       :int		    =  val_dataset.map(
                 _map_to_encoder_decoder_inputs		,batched=SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,remove_columns=['''article''', '''highlights''']		,)
             val_dataset.set_format(
                 type='''torch'''		,columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']		,)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_auto_remove_tmp_dir()

             __SCREAMING_SNAKE_CASE       :Any		    =  SeqaSeqTrainingArguments(
                 output_dir=SCREAMING_SNAKE_CASE__		,per_device_train_batch_size=SCREAMING_SNAKE_CASE__		,per_device_eval_batch_size=SCREAMING_SNAKE_CASE__		,predict_with_generate=SCREAMING_SNAKE_CASE__		,evaluation_strategy='''steps'''		,do_train=SCREAMING_SNAKE_CASE__		,do_eval=SCREAMING_SNAKE_CASE__		,warmup_steps=0		,eval_steps=2		,logging_steps=2		,)

             # instantiate trainer
             __SCREAMING_SNAKE_CASE       :Dict		    =  SeqaSeqTrainer(
                 model=SCREAMING_SNAKE_CASE__		,args=SCREAMING_SNAKE_CASE__		,compute_metrics=_compute_metrics		,train_dataset=SCREAMING_SNAKE_CASE__		,eval_dataset=SCREAMING_SNAKE_CASE__		,tokenizer=SCREAMING_SNAKE_CASE__		,)

             # start training
             trainer.train()






"""simple docstring"""
from __future__ import annotations

import math



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int			,       a_					:		bool			,       a_					:		list[int]			,       a_					:		float							)      ->				int:

      if depth < 0:
            raise ValueError('''Depth cannot be less than 0'''							)

      if len(a_							) == 0:
            raise ValueError('''Scores cannot be empty'''							)

      if depth == height:
            return scores[node_index]

      if is_max:
            return max(
                minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )

      return min(
          minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )



def 					__lowerCamelCase       (							)      ->				None:
      __SCREAMING_SNAKE_CASE       :Dict		    =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  math.log(len(a_							)			,       2							)
      print('''Optimal value : '''			,       end=''''''							)
      print(minimax(0			,       0			,       a_			,       a_			,       a_							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()