import torch

from accelerate import Accelerator, DistributedType






class 							_SCREAMING_SNAKE_CASE:
   def __enter__(    self    )	->str:
        if self.enabled:
             a  :    int				=						self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->List[Any]:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = True    )	->int:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
        a  :    Union[str, Any]				=						enabled and accelerator.distributed_type != DistributedType.NO
        a  :    Optional[Any]				=						0
        if self.enabled:
             a  :    Union[str, Any]				=						accelerator
             a  :    int				=						model
             a  :    int				=						local_sgd_steps
   def _lowercase(    self    )	->List[Any]:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()


   def _lowercase(    self    )	->Optional[Any]:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  a  :    int				=						self.accelerator.reduce(param.data							,     reduction='''mean'''    )
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/