




import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Tuple:  # picklable for multiprocessing
      return i + 1

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def    UpperCAmelCase_				( )   ->					str:
      with parallel_backend('spark'				):
            assert ParallelBackendConfig.backend_name == "spark"

      SCREAMING_SNAKE_CASE_      		=     [1, 2, 3]
      with pytest.raises(__UpperCAmelCase				):
            with parallel_backend('unsupported backend'				):
                  map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=2				)

      with pytest.raises(__UpperCAmelCase				):
            with parallel_backend('unsupported backend'				):
                  map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=-1				)





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'     ,     [2, -1]				)
def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple				)   ->					List[Any]:
      SCREAMING_SNAKE_CASE_      		=     [1, 2]
      SCREAMING_SNAKE_CASE_      		=     {'a': 1, 'b': 2}
      SCREAMING_SNAKE_CASE_      		=     {'a': [1, 2], 'b': [3, 4]}
      SCREAMING_SNAKE_CASE_      		=     {'a': {'1': 1}, 'b': 2}
      SCREAMING_SNAKE_CASE_      		=     {'a': 1, 'b': 2, 'c': 3, 'd': 4}
      SCREAMING_SNAKE_CASE_      		=     [2, 3]
      SCREAMING_SNAKE_CASE_      		=     {'a': 2, 'b': 3}
      SCREAMING_SNAKE_CASE_      		=     {'a': [2, 3], 'b': [4, 5]}
      SCREAMING_SNAKE_CASE_      		=     {'a': {'1': 2}, 'b': 3}
      SCREAMING_SNAKE_CASE_      		=     {'a': 2, 'b': 3, 'c': 4, 'd': 5}

      with parallel_backend('spark'				):
            assert map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=__UpperCAmelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=__UpperCAmelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=__UpperCAmelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=__UpperCAmelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCAmelCase     ,     __UpperCAmelCase     ,     num_proc=__UpperCAmelCase				) == expected_map_nested_sa



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			(_a						):







       @slow
       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny'					)
            __UpperCamelCase =			BertTokenizer.from_pretrained('bert-base-uncased'					)

            __UpperCamelCase =			bertabert.config.encoder.vocab_size
            __UpperCamelCase =			tokenizer.sep_token_id
            __UpperCamelCase =			tokenizer.cls_token_id
            __UpperCamelCase =			128

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

            __UpperCamelCase =			train_dataset.select(range(32					)					)
            __UpperCamelCase =			val_dataset.select(range(16					)					)

            __UpperCamelCase =			4

            def _map_to_encoder_decoder_inputs(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128					)
                 __UpperCamelCase =			inputs.input_ids
                 __UpperCamelCase =			inputs.attention_mask

                 __UpperCamelCase =			outputs.input_ids
                 __UpperCamelCase =			outputs.input_ids.copy()
                 __UpperCamelCase =			[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 __UpperCamelCase =			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(A_: str					):
                 __UpperCamelCase =			pred.label_ids
                 __UpperCamelCase =			pred.predictions

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

                 __UpperCamelCase =			sum([int(pred_str[i] == label_str[i]					) for i in range(len(A_					)					)]					) / len(A_					)

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			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 =			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 =			self.get_auto_remove_tmp_dir()

            __UpperCamelCase =			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 =			SeqaSeqTrainer(
                model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)

            # start training
            trainer.train()


