"""simple docstring"""




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			UpperCamelCase__(	a_       ):
						lowerCAmelCase__     :  Optional[int]  							=						['image_processor', 'tokenizer']
						lowerCAmelCase__     :  int  							=						'AutoImageProcessor'
						lowerCAmelCase__     :  Any  							=						'AutoTokenizer'





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[Any]:
													super().__init__(lowercase_						,lowercase_   )
													A__		   =							self.image_processor





						def __call__(				self						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Any:

													if text is None and images is None:
																				raise ValueError('You have to specify either text or images. Both cannot be none.'   )

													if text is not None:
																				A__		   =							self.tokenizer(lowercase_						,return_tensors=lowercase_						,**lowercase_   )

													if images is not None:
																				A__		   =							self.image_processor(lowercase_						,return_tensors=lowercase_						,**lowercase_   )

													if text is not None and images is not None:
																				A__		   =							image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowercase_   )						,tensor_type=lowercase_   )





						def  snake_case__		(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Union[str, Any]:
													return self.tokenizer.batch_decode(*lowercase_						,**lowercase_   )





						def  snake_case__		(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Any:
													return self.tokenizer.decode(*lowercase_						,**lowercase_   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													return ["input_ids", "attention_mask", "pixel_values"]








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




