from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							:      int      							={
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase							:      str      							=[
        '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''InformerForPrediction''',
        '''InformerModel''',
        '''InformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    _lowercase							:      List[Any]      							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


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






