
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				Optional[int] 	=						["image_processor", "tokenizer"]
     UpperCAmelCase__							:				Tuple 	=						"AutoImageProcessor"
     UpperCAmelCase__							:				Tuple 	=						"AutoTokenizer"





     def __init__(self :       List[Any]							,       _A :       List[Any]=None							,       _A :       int=None							,       **_A :       Tuple  )		->		Optional[Any]:
         snake_case						=   None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                 " instead."							,       _A							,       )
             snake_case						=   kwargs.pop("feature_extractor"  )

         snake_case						=   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError("You have to specify an image_processor."  )
         if tokenizer is None:
             raise ValueError("You have to specify a tokenizer."  )

         super().__init__(_A							,       _A  )
         snake_case						=   self.image_processor





     def __call__(self :       Any							,       _A :       Any=None							,       _A :       Tuple=None							,       _A :       Optional[Any]=None							,       **_A :       Union[str, Any]  )		->		Optional[int]:







         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:
             snake_case						=   self.tokenizer(_A							,       return_tensors=_A							,       **_A  )

         if images is not None:
             snake_case						=   self.image_processor(_A							,       return_tensors=_A							,       **_A  )

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





     def 						UpperCAmelCase(self :       int							,       *_A :       List[str]							,       **_A :       Tuple  )		->		str:
         return self.tokenizer.batch_decode(*_A							,       **_A  )





     def 						UpperCAmelCase(self :       List[Any]							,       *_A :       str							,       **_A :       Tuple  )		->		Optional[int]:
         return self.tokenizer.decode(*_A							,       **_A  )





     @property
     def 						UpperCAmelCase(self :       List[Any]  )		->		int:
         snake_case						=   self.tokenizer.model_input_names
         snake_case						=   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





     @property
     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Any:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,       _A							,       )
         return self.image_processor_class





     @property
     def 						UpperCAmelCase(self :       Dict  )		->		Tuple:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,       _A							,       )
         return self.image_processor









'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
