



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a=  logging.get_logger(__name__)

a=  {}






class 			__lowercase		(						_A						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''llama'''
     SCREAMING_SNAKE_CASE__		   =						['''past_key_values''']







     def __init__(		self     ,			_lowerCamelCase=3_2_0_0_0     ,			_lowerCamelCase=4_0_9_6     ,			_lowerCamelCase=1_1_0_0_8     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=None     ,			_lowerCamelCase="silu"     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-6     ,			_lowerCamelCase=True     ,			_lowerCamelCase=0     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			_lowerCamelCase=1     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	str		    =  vocab_size
         __UpperCamelCase	:	Union[str, Any]		    =  max_position_embeddings
         __UpperCamelCase	:	Tuple		    =  hidden_size
         __UpperCamelCase	:	List[str]		    =  intermediate_size
         __UpperCamelCase	:	Optional[Any]		    =  num_hidden_layers
         __UpperCamelCase	:	Optional[int]		    =  num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
             __UpperCamelCase	:	Tuple		    =  num_attention_heads

         __UpperCamelCase	:	Union[str, Any]		    =  num_key_value_heads
         __UpperCamelCase	:	Dict		    =  hidden_act
         __UpperCamelCase	:	Union[str, Any]		    =  initializer_range
         __UpperCamelCase	:	int		    =  rms_norm_eps
         __UpperCamelCase	:	Optional[Any]		    =  pretraining_tp
         __UpperCamelCase	:	List[str]		    =  use_cache
         __UpperCamelCase	:	str		    =  rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=UpperCamelCase__     ,			bos_token_id=UpperCamelCase__     ,			eos_token_id=UpperCamelCase__     ,			tie_word_embeddings=UpperCamelCase__     ,			**UpperCamelCase__     ,			)







     def lowerCAmelCase     (		self    ):






         if self.rope_scaling is None:
             return

         if not isinstance(self.rope_scaling     ,			UpperCamelCase__    ) or len(self.rope_scaling    ) != 2:
             raise ValueError(
                 '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                 f"""got {self.rope_scaling}"""    )
         __UpperCamelCase	:	Dict		    =  self.rope_scaling.get('type'     ,			UpperCamelCase__    )
         __UpperCamelCase	:	Dict		    =  self.rope_scaling.get('factor'     ,			UpperCamelCase__    )
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 f"""`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}"""    )
         if rope_scaling_factor is None or not isinstance(UpperCamelCase__     ,			UpperCamelCase__    ) or rope_scaling_factor <= 1.0:
             raise ValueError(f"""`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}"""    )




'''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @slow
     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,			'prajjwal1/bert-tiny'    )
         __UpperCamelCase	:	Optional[int]		    =  BertTokenizer.from_pretrained('bert-base-uncased'    )

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

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

         __UpperCamelCase	:	Dict		    =  train_dataset.select(range(3_2    )    )
         __UpperCamelCase	:	List[Any]		    =  val_dataset.select(range(1_6    )    )

         __UpperCamelCase	:	Optional[int]		    =  4

         def _map_to_encoder_decoder_inputs(_lowerCamelCase    ):
             # Tokenizer will automatically set [BOS] <text> [EOS]
             __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(batch['article']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=5_1_2    )
             __UpperCamelCase	:	Optional[int]		    =  tokenizer(batch['highlights']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=1_2_8    )
             __UpperCamelCase	:	Any		    =  inputs.input_ids
             __UpperCamelCase	:	Optional[Any]		    =  inputs.attention_mask

             __UpperCamelCase	:	Optional[Any]		    =  outputs.input_ids
             __UpperCamelCase	:	List[Any]		    =  outputs.input_ids.copy()
             __UpperCamelCase	:	Optional[Any]		    =  [
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
             ]
             __UpperCamelCase	:	Union[str, Any]		    =  outputs.attention_mask

             assert all(len(_lowerCamelCase    ) == 5_1_2 for x in inputs.input_ids    )
             assert all(len(_lowerCamelCase    ) == 1_2_8 for x in outputs.input_ids    )

             return batch

         def _compute_metrics(_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

             # all unnecessary tokens are removed
             __UpperCamelCase	:	Tuple		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

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

             return {"accuracy": accuracy}

         # map train dataset
         __UpperCamelCase	:	str		    =  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
         __UpperCamelCase	:	Any		    =  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']     ,			)

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

         __UpperCamelCase	:	List[Any]		    =  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
         __UpperCamelCase	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()



