






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


SCREAMING_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 							A_    (						UpperCamelCase_			):






   '''simple docstring'''




   def __init__(	self							,  _A = 101) ->		Dict:

       """simple docstring"""



       _UpperCAmelCase				:      Union[str, Any]			  =				length


   def __len__(	self) ->		List[str]:

       """simple docstring"""



       return self.length


   def __getitem__(	self							,  _A) ->		int:

       """simple docstring"""



       return i



  class 							A_    :






   '''simple docstring'''




   def __call__(	self							,  _A) ->		List[str]:

       """simple docstring"""



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



  class 							A_    (						nn.Module			):






   '''simple docstring'''




   def __init__(	self) ->		Union[str, Any]:

       """simple docstring"""



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


   def  snake_case__				(	self							,  _A							,  _A=None) ->		str:

       """simple docstring"""



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






 '''simple docstring'''




 @require_torch_neuroncore
 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				f'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
     _UpperCAmelCase				:      List[Any]			  =				self.get_auto_remove_tmp_dir()
     _UpperCAmelCase				:      Any			  =				f'''--output_dir {output_dir}'''.split()
     _UpperCAmelCase				:      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



class 							A_    (						UpperCamelCase_			):






 '''simple docstring'''




 @require_torch_multi_gpu
 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				f'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
     _UpperCAmelCase				:      Dict			  =				self.get_auto_remove_tmp_dir()
     _UpperCAmelCase				:      Optional[Any]			  =				f'''--output_dir {output_dir}'''.split()
     _UpperCAmelCase				:      str			  =				['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

  SCREAMING_SNAKE_CASE 							=						HfArgumentParser((TrainingArguments,))
  SCREAMING_SNAKE_CASE 							=						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 [101, 40, 7]:
    SCREAMING_SNAKE_CASE 							=						DummyDataset(dataset_length)

    def      _lowerCamelCase	(						__A    :							EvalPrediction )      ->					Dict:

        '''simple docstring'''





        _UpperCAmelCase				:      str			  =				list(range(len(__A ) ) )
        _UpperCAmelCase				:      Dict			  =				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}

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

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

    SCREAMING_SNAKE_CASE 							=						2

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

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


    SCREAMING_SNAKE_CASE 							=						None












import argparse
import os
import re


SCREAMING_SNAKE_CASE 							=						'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE 							=						re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE 							=						re.compile(R'\s*\(\s*"(\S[^"]+)"')

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							bool = False )      ->					int:
    with open(__A		,		'''r'''		,		encoding='''utf-8''' ) as f:
        _UpperCAmelCase				:      Union[str, Any]			  =				f.read()

    _UpperCAmelCase				:      Any			  =				content.split('''\n''' )
    _UpperCAmelCase				:      Any			  =				[]
    _UpperCAmelCase				:      Tuple			  =				0
    while line_idx < len(__A ):
        if _re_intro_mapping.search(lines[line_idx] ) is not None:
            _UpperCAmelCase				:      Union[str, Any]			  =				len(re.search(r'''^(\s*)\S'''		,		lines[line_idx] ).groups()[0] ) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
                new_lines.append(lines[line_idx] )
                line_idx += 1

            _UpperCAmelCase				:      str			  =				[]
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    _UpperCAmelCase				:      List[str]			  =				line_idx
                    while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
                        line_idx += 1
                    blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
                else:
                    blocks.append(lines[line_idx] )
                line_idx += 1

            # Sort blocks by their identifiers
            _UpperCAmelCase				:      Tuple			  =				sorted(__A		,		key=lambda __A : _re_identifier.search(__A ).groups()[0] )
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx] )
            line_idx += 1

    if overwrite:
        with open(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
            f.write('''\n'''.join(__A ) )
    elif "\n".join(__A ) != content:
        return True

def      _lowerCamelCase	(						__A    :							bool = False )      ->					List[str]:
    _UpperCAmelCase				:      List[str]			  =				[os.path.join(__A		,		__A ) for f in os.listdir(__A ) if f.endswith('''.py''' )]
    _UpperCAmelCase				:      List[Any]			  =				[sort_auto_mapping(__A		,		overwrite=__A ) for fname in fnames]

    if not overwrite and any(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				[f for f, d in zip(__A		,		__A ) if d]
        raise ValueError(
            f'''The following files have auto mappings that need sorting: {', '.join(__A )}. Run `make style` to fix'''
            ''' this.''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  sort_all_auto_mappings(not args.check_only)




