



import sys
from typing import Dict

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


__lowerCamelCase      =    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 ):





     """simple docstring"""



     def __init__(  self		,			a = 1_0_1    )		->							Optional[int]:




       """simple docstring"""







       _A							=			length
     def __len__(  self    )		->							str:




       """simple docstring"""







       return self.length



     def __getitem__(  self		,			a    )		->							List[Any]:




       """simple docstring"""







       return i







    class 						_snake_case					:





     """simple docstring"""



     def __call__(  self		,			a    )		->							int:




       """simple docstring"""







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







    class 						_snake_case					(	nn.Module ):





     """simple docstring"""



     def __init__(  self    )		->							Any:




       """simple docstring"""







       super().__init__()
       # Add some (unused) params otherwise DDP will complain.
       _A							=			nn.Linear(1_2_0		,			8_0    )



     def lowercase_						(  self		,			a		,			a=None    )		->							Any:




       """simple docstring"""







       if labels is not None:
         return torch.tensor(0.0		,			device=input_ids.device    ), input_ids
       else:
         return input_ids




def 			UpperCAmelCase__       (							)      ->       Optional[Any]:
  _A							=			HfArgumentParser((TrainingArguments,)							)
  sys.argv += ["--output_dir", "./examples"]
  _A							=			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_001, 256, 15]:
    _A							=			DummyDataset(__UpperCAmelCase							)

    def compute_metrics(__snake_case							) -> Dict:
      _A							=			list(range(len(__UpperCAmelCase							)							)							)
      _A							=			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    _A							=			Trainer(
        model=DummyModel()  , args=__UpperCAmelCase  , data_collator=DummyDataCollator()  , eval_dataset=__UpperCAmelCase  , compute_metrics=__UpperCAmelCase  , )
    _A							=			trainer.evaluate()
    logger.info(__UpperCAmelCase							)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCAmelCase							)
      exit(1							)

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

    _A							=			2

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

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

    _A							=			None

  logger.info('''🔥 All distributed tests successful'''							)




def 			UpperCAmelCase__       (							__snake_case							)      ->       int:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()




"""simple docstring"""







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


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)

UpperCAmelCase:   Optional[Any]       			=							{
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Dict		    =   "opt"
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   ["past_key_values"]






	def __init__(			self							,UpperCAmelCase_=5_02_72							,UpperCAmelCase_=7_68							,UpperCAmelCase_=12							,UpperCAmelCase_=30_72							,UpperCAmelCase_=20_48							,UpperCAmelCase_=True							,UpperCAmelCase_=False							,UpperCAmelCase_=None							,UpperCAmelCase_=0.1							,UpperCAmelCase_=0.0							,UpperCAmelCase_=12							,UpperCAmelCase_="relu"							,UpperCAmelCase_=0.0							,UpperCAmelCase_=0.02							,UpperCAmelCase_=True							,UpperCAmelCase_=1							,UpperCAmelCase_=2							,UpperCAmelCase_=2							,UpperCAmelCase_=True							,UpperCAmelCase_=True							,**UpperCAmelCase_							,):
			super().__init__(
			    pad_token_id=UpperCAmelCase_							,bos_token_id=UpperCAmelCase_							,eos_token_id=UpperCAmelCase_							,**UpperCAmelCase_							,)
			_lowercase							:      Optional[Any]			=					vocab_size
			_lowercase							:      Tuple			=					max_position_embeddings
			_lowercase							:      Union[str, Any]			=					num_attention_heads
			_lowercase							:      List[str]			=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
			_lowercase							:      List[str]			=					ffn_dim
			_lowercase							:      List[str]			=					hidden_size
			_lowercase							:      List[Any]			=					num_hidden_layers
			_lowercase							:      List[Any]			=					dropout
			_lowercase							:      str			=					attention_dropout
			_lowercase							:      List[Any]			=					activation_function
			_lowercase							:      int			=					init_std
			_lowercase							:      List[Any]			=					layerdrop
			_lowercase							:      Tuple			=					use_cache
			_lowercase							:      Any			=					do_layer_norm_before
			# We keep these variables at `True` for backward compatibility.
			_lowercase							:      int			=					enable_bias
			_lowercase							:      str			=					layer_norm_elementwise_affine

			# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
			# with checkpoints that have been fine-tuned before transformers v4.20.1
			# see https://github.com/facebookresearch/metaseq/pull/164
			_lowercase							:      Optional[Any]			=					_remove_final_layer_norm



