





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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase     :     Optional[int]							=	logging.getLogger(__name__)







@dataclass
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Optional[float]            =      field(
          default=0.0						,    metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""})
      _lowercase    : bool            =      field(default=UpperCamelCase__						,    metadata={"""help""": """Whether to SortishSamler or not."""})
      _lowercase    : bool            =      field(
          default=UpperCamelCase__						,    metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""})
      _lowercase    : bool            =      field(default=UpperCamelCase__						,    metadata={"""help""": """whether to use adafactor"""})
      _lowercase    : Optional[float]            =      field(
          default=UpperCamelCase__						,    metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""})
      _lowercase    : Optional[float]            =      field(
          default=UpperCamelCase__						,    metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""})
      _lowercase    : Optional[float]            =      field(default=UpperCamelCase__						,    metadata={"""help""": """Dropout probability. Goes into model.config."""})
      _lowercase    : Optional[float]            =      field(
          default=UpperCamelCase__						,    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())}"""}						,    )













import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase__)
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      field(default="""image-classification"""						,    metadata={"""include_in_asdict_even_if_is_default""": True})
      _lowercase    : ClassVar[Features]            =      Features({"""image""": Image()})
      _lowercase    : ClassVar[Features]            =      Features({"""labels""": ClassLabel})
      _lowercase    : str            =      "image"
      _lowercase    : str            =      "labels"







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        if self.label_column not in features:
          raise ValueError(F'''Column {self.label_column} is not present in features.'''							)
        if not isinstance(features[self.label_column]		,				lowerCAmelCase__							):
          raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''							)
        a__     : int  =copy.deepcopy(self							)
        a__     : Dict  =self.label_schema.copy()
        a__     : int  =features[self.label_column]
        a__     : str  =label_schema
        return task_template







      @property
      def        _lowercase  (   self							)       ->						Dict[str, str]:



        '''simple docstring'''




        return {
            self.image_column: "image",
            self.label_column: "labels",
        }






