

'''simple docstring'''





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

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


@dataclass(frozen=UpperCAmelCase__    )
class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					field(default='text-classification'  , metadata={'include_in_asdict_even_if_is_default': True}    )
		SCREAMING_SNAKE_CASE_           =					Features({'text': Value('string'    )}    )
		SCREAMING_SNAKE_CASE_           =					Features({'labels': ClassLabel}    )
		SCREAMING_SNAKE_CASE_           =					"text"
		SCREAMING_SNAKE_CASE_           =					"labels"
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     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]   ,			SCREAMING_SNAKE_CASE_		):
												raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
							lowerCamelCase_  				=      copy.deepcopy(self		)
							lowerCamelCase_  				=      self.label_schema.copy()
							lowerCamelCase_  				=      features[self.label_column]
							lowerCamelCase_  				=      label_schema
							return task_template






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






							'''simple docstring'''



							return {
							    self.text_column: "text",
							    self.label_column: "labels",
							}










'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d