


import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


snake_case__  				=			logging.get_logger(__name__)


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset

			from transformers import Trainer



			class      snake_case__	(   lowerCamelCase__	):
							def __init__(	self  ,   _UpperCAmelCase = 101   ):




													"""simple docstring"""




													__magic_name__					:				Dict				=   length
							def __len__(	self   ):




													"""simple docstring"""




													return self.length





							def __getitem__(	self  ,   _UpperCAmelCase   ):




													"""simple docstring"""




													return i






			class      snake_case__	:
							def __call__(	self  ,   _UpperCAmelCase   ):




													"""simple docstring"""




													return {"input_ids": torch.tensor(_UpperCAmelCase   ), "labels": torch.tensor(_UpperCAmelCase   )}






			class      snake_case__	(   nn.Module	):
							def __init__(	self   ):




													"""simple docstring"""




													super().__init__()
													# Add some (unused) params otherwise DDP will complain.
													__magic_name__					:				Union[str, Any]				=   nn.Linear(120  ,   80   )





							def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=None   ):




													"""simple docstring"""




													if labels is not None:
																			return torch.tensor(0.0  ,   device=input_ids.device   ), input_ids
													else:
																			return input_ids
def 			__SCREAMING_SNAKE_CASE							(					)							->	str:
						__magic_name__					:				List[str]				=   HfArgumentParser((TrainingArguments,)					)
						sys.argv += ["--output_dir", "./examples"]
						__magic_name__					:				int				=   parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
						    f"tpu_num_cores: {training_args.tpu_num_cores}"   ,    )

						# Essentially, what we want to verify in the distributed case is
						# that we get all samples back, in the right order.
						# (this is crucial for prediction for instance)
						for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
												__magic_name__					:				Optional[int]				=   DummyDataset(lowercase__					)

												def compute_metrics(lowercase__	:EvalPrediction					) -> Dict:
																		__magic_name__					:				List[Any]				=   list(range(len(lowercase__					)					)					)
																		__magic_name__					:				Optional[int]				=   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																		return {"success": success}

												__magic_name__					:				Union[str, Any]				=   Trainer(
												    model=DummyModel()   ,    args=lowercase__   ,    data_collator=DummyDataCollator()   ,    eval_dataset=lowercase__   ,    compute_metrics=lowercase__   ,    )
												__magic_name__					:				Optional[int]				=   trainer.evaluate()
												logger.info(lowercase__					)
												if metrics["eval_success"] is not True:
																		logger.error(lowercase__					)
																		exit(1					)

												__magic_name__					:				Dict				=   trainer.predict(lowercase__					)
												logger.info(p.metrics					)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics					)
																		exit(1					)

												__magic_name__					:				List[str]				=   2

												__magic_name__					:				str				=   trainer.evaluate()
												logger.info(lowercase__					)
												if metrics["eval_success"] is not True:
																		logger.error(lowercase__					)
																		exit(1					)

												__magic_name__					:				Union[str, Any]				=   trainer.predict(lowercase__					)
												logger.info(p.metrics					)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics					)
																		exit(1					)

												__magic_name__					:				Dict				=   None

						logger.info('🔥 All distributed tests successful'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Tuple					)							->	Any:
						main()


if __name__ == "__main__":
			main()





"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
