





'''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       a_   (				_lowerCAmelCase							):





     @slow
     @require_torch
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Tuple    			= EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"	,      "prajjwal1/bert-tiny"						)
        lowercase_			:Union[str, Any]    			= BertTokenizer.from_pretrained("bert-base-uncased"						)

        lowercase_			:str    			= bertabert.config.encoder.vocab_size
        lowercase_			:Dict    			= tokenizer.sep_token_id
        lowercase_			:Any    			= tokenizer.cls_token_id
        lowercase_			:Any    			= 128

        lowercase_			:List[Any]    			= datasets.load_dataset("cnn_dailymail"	,      "3.0.0"	,      split="train[:1%]"						)
        lowercase_			:Any    			= datasets.load_dataset("cnn_dailymail"	,      "3.0.0"	,      split="validation[:1%]"						)

        lowercase_			:Any    			= train_dataset.select(range(32						)						)
        lowercase_			:Optional[Any]    			= val_dataset.select(range(16						)						)

        lowercase_			:str    			= 4

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

           lowercase_			:List[str]    			= outputs.input_ids
           lowercase_			:Any    			= outputs.input_ids.copy()
           lowercase_			:Tuple    			= [
               [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
           ]
           lowercase_			:List[Any]    			= 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		:					Optional[int]						):
           lowercase_			:Optional[Any]    			= pred.label_ids
           lowercase_			:List[Any]    			= pred.predictions

           # all unnecessary tokens are removed
           lowercase_			:int    			= tokenizer.batch_decode(lowercase	,      skip_special_tokens=lowercase						)
           lowercase_			:Dict    			= tokenizer.batch_decode(lowercase	,      skip_special_tokens=lowercase						)

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

           return {"accuracy": accuracy}

        # map train dataset
        lowercase_			:Union[str, Any]    			= 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
        lowercase_			:int    			= 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"]	,      )

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

        lowercase_			:List[str]    			= 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
        lowercase_			:Dict    			= SeqaSeqTrainer(
            model=lowercase	,      args=lowercase	,      compute_metrics=_compute_metrics	,      train_dataset=lowercase	,      eval_dataset=lowercase	,      tokenizer=lowercase	,      )

        # start training
        trainer.train()









'''simple docstring'''


lowerCAmelCase					:     str												='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase					:     int												=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase					:     List[str]												={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


