


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 		_snake_case  (				_snake_case							):




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

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

       a						:str										=	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						:Union[str, Any]										=	train_dataset.select(range(32   )   )
       a						:Any										=	val_dataset.select(range(16   )   )

       a						:Tuple										=	4

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

             a						:Dict										=	outputs.input_ids
             a						:List[Any]										=	outputs.input_ids.copy()
             a						:Union[str, Any]										=	[
                 [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
             ]
             a						:Tuple										=	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(_lowerCamelCase   ):
             a						:Optional[int]										=	pred.label_ids
             a						:Union[str, Any]										=	pred.predictions

             # all unnecessary tokens are removed
             a						:int										=	tokenizer.batch_decode(_lowerCamelCase ,  skip_special_tokens=_lowerCamelCase   )
             a						:Any										=	tokenizer.batch_decode(_lowerCamelCase ,  skip_special_tokens=_lowerCamelCase   )

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

             return {"accuracy": accuracy}

       # map train dataset
       a						:Optional[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						:Tuple										=	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						:Tuple										=	self.get_auto_remove_tmp_dir()

       a						:int										=	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						:Optional[Any]										=	SeqaSeqTrainer(
           model=_lowerCamelCase ,  args=_lowerCamelCase ,  compute_metrics=_compute_metrics ,  train_dataset=_lowerCamelCase ,  eval_dataset=_lowerCamelCase ,  tokenizer=_lowerCamelCase ,  )

       # start training
       trainer.train()









from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .multicontrolnet import MultiControlNetModel
     from .pipeline_controlnet import StableDiffusionControlNetPipeline
     from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
     from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
     from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline





