'''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


_UpperCamelCase					:		Union[str, Any]							      =		logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__					)
class 	_lowercase(  UpperCAmelCase__					):



   """simple docstring"""





   __lowerCamelCase     =	field(default=UpperCAmelCase__ ,metadata={'''help''': '''Whether to use SortishSampler or not.'''}					)
   __lowerCamelCase     =	field(
       default=UpperCAmelCase__ ,metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
   __lowerCamelCase     =	field(
       default=UpperCAmelCase__ ,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=UpperCAmelCase__ ,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=UpperCAmelCase__ ,metadata={
           '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
       } ,)





   def       snake_case						(				self:     int						):
    __UpperCAmelCase								=       super().to_dict()
    for k, v in d.items():
     if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						):
      __UpperCAmelCase								=       v.to_dict()
    return d







import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)

