





'''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   _lowercase						(							__lowerCAmelCase				):


   @slow
   @require_torch
   def 					snake_case				( self					):
       A       :       str							=						EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''    ,   '''prajjwal1/bert-tiny'''					)
       A       :       Dict							=						BertTokenizer.from_pretrained('''bert-base-uncased'''					)

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

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

       A       :       Tuple							=						train_dataset.select(range(32					)					)
       A       :       Dict							=						val_dataset.select(range(16					)					)

       A       :       List[str]							=						4

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

           A       :       Dict							=						outputs.input_ids
           A       :       Dict							=						outputs.input_ids.copy()
           A       :       Any							=						[
               [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
           ]
           A       :       Optional[int]							=						outputs.attention_mask

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

           return batch

       def _compute_metrics(_UpperCAmelCase					):
           A       :       Any							=						pred.label_ids
           A       :       Tuple							=						pred.predictions

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

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

           return {"accuracy": accuracy}

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

       A       :       List[str]							=						self.get_auto_remove_tmp_dir()

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

       # instantiate trainer
       A       :       Tuple							=						SeqaSeqTrainer(
           model=lowerCAmelCase_    ,   args=lowerCAmelCase_    ,   compute_metrics=_compute_metrics    ,   train_dataset=lowerCAmelCase_    ,   eval_dataset=lowerCAmelCase_    ,   tokenizer=lowerCAmelCase_    ,   )

       # start training
       trainer.train()







'''simple docstring'''






from math import pi
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int		)		->  float:
    return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))
