


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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



