





"""simple docstring"""




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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCamelCase_									=logging.getLogger(__name__)




@dataclass
class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   field(
				    default=0.0     ,    metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}    )
				UpperCamelCase								=   field(default=_lowerCAmelCase     ,    metadata={'''help''': '''Whether to SortishSamler or not.'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}    )
				UpperCamelCase								=   field(default=_lowerCAmelCase     ,    metadata={'''help''': '''whether to use adafactor'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}    )
				UpperCamelCase								=   field(default=_lowerCAmelCase     ,    metadata={'''help''': '''Dropout probability. Goes into model.config.'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}    )
				UpperCamelCase								=   field(
				    default='''linear'''     ,    metadata={'''help''': f"""Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()    )}"""}     ,    )










"""simple docstring"""




from ...processing_utils import ProcessorMixin




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''feature_extractor''']
				UpperCamelCase								=   '''TvltImageProcessor'''
				UpperCamelCase								=   '''TvltFeatureExtractor'''






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__(image_processor=lowerCAmelCase__,			feature_extractor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   image_processor
										_UpperCamelCase      : int         =   feature_extractor






				def __call__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]=False,			lowerCAmelCase__		:   str=False,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Optional[int],			)     ->				Dict:




										'''simple docstring'''



										if images is None and audio is None:
																raise ValueError('''You need to specify either an `images` or `audio` input to process.'''							)

										_UpperCamelCase      : Optional[int]         =   None
										if images is not None:
																_UpperCamelCase      : Optional[int]         =   self.image_processor(lowerCAmelCase__,			mask_pixel=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if images_mixed is not None:
																_UpperCamelCase      : str         =   self.image_processor(lowerCAmelCase__,			is_mixed=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if audio is not None:
																_UpperCamelCase      : Union[str, Any]         =   self.feature_extractor(
																    lowerCAmelCase__,			*lowerCAmelCase__,			sampling_rate=lowerCAmelCase__,			mask_audio=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : str         =   {}
										if audio is not None:
																output_dict.update(lowerCAmelCase__							)
										if images is not None:
																output_dict.update(lowerCAmelCase__							)
										if images_mixed_dict is not None:
																output_dict.update(lowerCAmelCase__							)
										return output_dict






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.image_processor.model_input_names
										_UpperCamelCase      : List[Any]         =   self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)



