





"""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_									=logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   field(default=_lowerCAmelCase     ,    metadata={'''help''': '''Whether to use SortishSampler or not.'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    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.'''
				        )
				    }     ,    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    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.'''
				        )
				    }     ,    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={
				        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
				    }     ,    )






				def     snake_case     (			self		:   Union[str, Any]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   super().to_dict()
										for k, v in d.items():
																if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																						_UpperCamelCase      : Optional[int]         =   v.to_dict()
										return d










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



