'''simple docstring'''




from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


_A								=   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       UpperCAmelCase__  (       _snake_case			):






    """simple docstring"""
    def __init__(self       ,				_a = 101				)    ->     Union[str, Any]:
         lowercase_					:       Union[str, Any]     				=					length
    def __len__(self				)    ->     List[str]:
         return self.length





    def __getitem__(self       ,				_a				)    ->     int:
         return i



 class       UpperCAmelCase__  :






    """simple docstring"""
    def __call__(self       ,				_a				)    ->     Dict:
         return {"input_ids": torch.tensor(_a				), "labels": torch.tensor(_a				)}



 class       UpperCAmelCase__  (       nn.Module			):






    """simple docstring"""
    def __init__(self				)    ->     Union[str, Any]:
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase_					:       Tuple     				=					nn.Linear(120       ,				80				)





    def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     int:
         if labels is not None:
              return torch.tensor(0.0       ,				device=input_ids.device				), input_ids
         else:
              return input_ids


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   @require_torch_neuroncore
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Tuple     				=					f'''--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
        lowercase_					:       Any     				=					self.get_auto_remove_tmp_dir()
        lowercase_					:       Any     				=					f'''--output_dir {output_dir}'''.split()
        lowercase_					:       Dict     				=					['torchrun'] + distributed_args + args
        execute_subprocess_async(_a       ,				env=self.get_env()				)
        # successful return here == success - any errors would have caused an error in the sub-call



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   @require_torch_multi_gpu
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Optional[Any]     				=					f'''--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
        lowercase_					:       Tuple     				=					self.get_auto_remove_tmp_dir()
        lowercase_					:       Union[str, Any]     				=					f'''--output_dir {output_dir}'''.split()
        lowercase_					:       Union[str, Any]     				=					['torchrun'] + distributed_args + args
        execute_subprocess_async(_a       ,				env=self.get_env()				)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 _A								=   HfArgumentParser((TrainingArguments,))
 _A								=   parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
     F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
 )

 # 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_1, 4_0, 7]:
  _A								=   DummyDataset(dataset_length)

  def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
       lowercase_					:       Optional[Any]     				=					list(range(len(SCREAMING_SNAKE_CASE_							)							)							)
       lowercase_					:       Union[str, Any]     				=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
       if not success and training_args.local_rank == 0:
            logger.warning(
                'Predictions and/or labels do not match expected results:\n  - predictions: '
                f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''							)
       return {"success": success}

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

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

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







  _A								=   None





'''simple docstring'''




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


_A								=   logging.get_logger(__name__)

_A								=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Any     =    '''mra'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=1       ,				_a=0.02       ,				_a=1e-5       ,				_a="absolute"       ,				_a=4       ,				_a="full"       ,				_a=0       ,				_a=0       ,				_a=1       ,				_a=0       ,				_a=2       ,				**_a       ,				)    ->     int:
        super().__init__(pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				**_a				)

        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       Optional[int]     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Optional[int]     				=					intermediate_size
        lowercase_					:       Dict     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					initializer_range
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       Any     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					position_embedding_type
        lowercase_					:       Any     				=					block_per_row
        lowercase_					:       Optional[int]     				=					approx_mode
        lowercase_					:       int     				=					initial_prior_first_n_blocks
        lowercase_					:       str     				=					initial_prior_diagonal_n_blocks




