

"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
 import torch







@require_torch
@require_optimum
@slow
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    'hf-internal-testing/tiny-random-t5'
      _lowerCAmelCase       :   List[Any]  =    AutoTokenizer.from_pretrained(_A      )
      _lowerCAmelCase       :   int  =    AutoModelForSeqaSeqLM.from_pretrained(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    tokenizer('This is me'    ,return_tensors='pt'      )

      _lowerCAmelCase       :   int  =    model.to_bettertransformer()

      self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

      _lowerCAmelCase       :   Union[str, Any]  =    model.generate(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    model.reverse_bettertransformer()

      self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(_A      )

          _lowerCAmelCase       :   List[str]  =    AutoModelForSeqaSeqLM.from_pretrained(_A      )

          self.assertFalse(
              any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

          _lowerCAmelCase       :   Optional[int]  =    model_reloaded.generate(**_A      )
          self.assertTrue(torch.allclose(_A    ,_A      )      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    'hf-internal-testing/tiny-random-t5'
      _lowerCAmelCase       :   Optional[int]  =    AutoModelForSeqaSeqLM.from_pretrained(_A      )

      _lowerCAmelCase       :   str  =    model.to_bettertransformer()

      with tempfile.TemporaryDirectory() as tmpdirname:
          with self.assertRaises(_A      ):
              model.save_pretrained(_A      )

          _lowerCAmelCase       :   str  =    model.reverse_bettertransformer()
          model.save_pretrained(_A      )








"""simple docstring"""






import logging
import os

from .state import PartialState







class 	__UpperCamelCase    (    logging.LoggerAdapter						):

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)







  def __lowerCamelCase		(		self    ,_A    ,_A    ,*_A    ,**_A      ):
      '''simple docstring'''

      if PartialState._shared_state == {}:
          raise RuntimeError(
              'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'      )
      _lowerCAmelCase       :   Tuple  =    kwargs.pop('main_process_only'    ,_A      )
      _lowerCAmelCase       :   Any  =    kwargs.pop('in_order'    ,_A      )

      if self.isEnabledFor(_A      ):
          if self._should_log(_A      ):
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
              self.logger.log(_A    ,_A    ,*_A    ,**_A      )

          elif in_order:
              _lowerCAmelCase       :   str  =    PartialState()
              for i in range(state.num_processes      ):
                  if i == state.process_index:
                      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
                      self.logger.log(_A    ,_A    ,*_A    ,**_A      )
                  state.wait_for_everyone()





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase = None   ):

    '''simple docstring'''



    if log_level is None:
        _lowerCAmelCase       :   Union[str, Any]  =    os.environ.get('ACCELERATE_LOG_LEVEL' ,   _lowerCamelCase   )
    _lowerCAmelCase       :   int  =    logging.getLogger(_lowerCamelCase   )
    if log_level is not None:
        logger.setLevel(log_level.upper()   )
        logger.root.setLevel(log_level.upper()   )
    return MultiProcessAdapter(_lowerCamelCase ,   {}   )





