






import pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase   :     Any 		= ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				List[Any]	,    snake_case_   :				Tuple  ):


	for item in items:
		if any(marker in item.keywords for marker in ["integration", "unit"]  ):
			continue
		item.add_marker(pytest.mark.unit  )







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				str  ):
	config.addinivalue_line("markers"	,    "torchaudio_latest: mark test to run with torchaudio>=0.12"  )







@pytest.fixture(autouse=_lowercase  )
def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[Any]	,    snake_case_   :				Optional[Any]  ):
	snake_case__					: int  =    tmp_path_factory.getbasetemp() / "cache"
	snake_case__					: Optional[Any]  =    test_hf_cache_home / "datasets"
	snake_case__					: Any  =    test_hf_cache_home / "metrics"
	snake_case__					: List[str]  =    test_hf_cache_home / "modules"
	monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"	,    str(_lowercase  )  )
	monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"	,    str(_lowercase  )  )
	monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"	,    str(_lowercase  )  )
	snake_case__					: Optional[Any]  =    test_hf_datasets_cache / "downloads"
	monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"	,    str(_lowercase  )  )
	snake_case__					: Union[str, Any]  =    test_hf_datasets_cache / "downloads" / "extracted"
	monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"	,    str(_lowercase  )  )







@pytest.fixture(autouse=_lowercase	,    scope="session"  )
def 	SCREAMING_SNAKE_CASE							(					):
	datasets.disable_progress_bar()







@pytest.fixture(autouse=_lowercase  )
def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Tuple  ):
	monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"	,    _lowercase  )







@pytest.fixture
def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Any  ):
	monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"	,    _lowercase  )







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()


