


from ..utils import DummyObject, requires_backends



class A__     ( metaclass=A__  ):



  """simple docstring"""



  _lowercase									=		['transformers', 'torch', 'note_seq']

  def __init__( self			:  Optional[int]				,				*lowerCamelCase__			:  int				,				**lowerCamelCase__			:  int       ):
        requires_backends(self				,				["transformers", "torch", "note_seq"]       )

  @classmethod
  def    _UpperCamelCase( cls			:  str				,				*lowerCamelCase__			:  Union[str, Any]				,				**lowerCamelCase__			:  Optional[int]       ):
        requires_backends(cls				,				["transformers", "torch", "note_seq"]       )

  @classmethod
  def    _UpperCamelCase( cls			:  str				,				*lowerCamelCase__			:  Optional[int]				,				**lowerCamelCase__			:  Any       ):
        requires_backends(cls				,				["transformers", "torch", "note_seq"]       )





'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)