


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
lowerCamelCase		   =			Lock()




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : List[Any]	):
       '''simple docstring'''





       global process_lock

       # we perform n swaps since after n swaps we know we are sorted
       # we *could* stop early if we are sorted already, but it takes as long to
       # find out we are sorted as it does to sort the list with this algorithm
       for i in range(0						,     10	):
              if (i + position) % 2 == 0 and r_send is not None:
                     # send your value to your right neighbor
                     process_lock.acquire()
                     r_send[1].send(SCREAMING_SNAKE_CASE__	)
                     process_lock.release()

                     # receive your right neighbor's value
                     process_lock.acquire()
                     _lowerCamelCase			:						int            =rr_cv[0].recv()
                     process_lock.release()

                     # take the lower value since you are on the left
                     _lowerCamelCase			:						Optional[Any]            =min(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
              elif (i + position) % 2 != 0 and l_send is not None:
                     # send your value to your left neighbor
                     process_lock.acquire()
                     l_send[1].send(SCREAMING_SNAKE_CASE__	)
                     process_lock.release()

                     # receive your left neighbor's value
                     process_lock.acquire()
                     _lowerCamelCase			:						List[str]            =lr_cv[0].recv()
                     process_lock.release()

                     # take the higher value since you are on the right
                     _lowerCamelCase			:						Optional[Any]            =max(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
    # after all swaps are performed, send the values back to main
       result_pipe[1].send(SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : Any	):
       '''simple docstring'''





       _lowerCamelCase			:						Tuple            =[]
       _lowerCamelCase			:						Optional[Any]            =[]
       # initialize the list of pipes where the values will be retrieved
       for _ in arr:
              result_pipe.append(Pipe()	)
       # creates the processes
       # the first and last process only have one neighbor so they are made outside
       # of the loop
       _lowerCamelCase			:						Dict            =Pipe()
       _lowerCamelCase			:						Optional[int]            =Pipe()
       process_array_.append(
           Process(
               target=SCREAMING_SNAKE_CASE__						,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])						,     )	)
       _lowerCamelCase			:						Optional[int]            =temp_rs
       _lowerCamelCase			:						Union[str, Any]            =temp_rr

       for i in range(1						,     len(SCREAMING_SNAKE_CASE__	) - 1	):
              _lowerCamelCase			:						Tuple            =Pipe()
              _lowerCamelCase			:						Dict            =Pipe()
              process_array_.append(
                  Process(
                      target=SCREAMING_SNAKE_CASE__						,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])						,     )	)
              _lowerCamelCase			:						Tuple            =temp_rs
              _lowerCamelCase			:						Optional[int]            =temp_rr

       process_array_.append(
           Process(
               target=SCREAMING_SNAKE_CASE__						,     args=(
                   len(SCREAMING_SNAKE_CASE__	) - 1,
                   arr[len(SCREAMING_SNAKE_CASE__	) - 1],
                   temp_ls,
                   None,
                   temp_lr,
                   None,
                   result_pipe[len(SCREAMING_SNAKE_CASE__	) - 1],
               )						,     )	)

       # start the processes
       for p in process_array_:
              p.start()

       # wait for the processes to end and write their values to the list
       for p in range(0						,     len(SCREAMING_SNAKE_CASE__	)	):
              _lowerCamelCase			:						Optional[Any]            =result_pipe[p][0].recv()
              process_array_[p].join()
       return arr




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Tuple            =list(range(10						,     0						,     -1	)	)
       print('Initial List'	)
       print(*SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Tuple            =odd_even_transposition(SCREAMING_SNAKE_CASE__	)
       print('Sorted List\n'	)
       print(*SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 main()






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 A    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




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

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

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

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

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

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

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

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

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

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


