


import warnings

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







class      _lowercase					(				__UpperCamelCase):


       """simple docstring"""




       A__   		=     ["image_processor", "tokenizer"]
       A__   		=     "AutoImageProcessor"
       A__   		=     "AutoTokenizer"





       def __init__(  self				:     Optional[int]   , __lowerCamelCase				:     Dict=None   , __lowerCamelCase				:     str=None   , **__lowerCamelCase				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."   , _lowerCAmelCase   , )
                   lowerCamelCase__						:							str       =    kwargs.pop("feature_extractor"			)

             lowerCamelCase__						:							Optional[Any]       =    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__(_lowerCAmelCase   , _lowerCAmelCase			)
             lowerCamelCase__						:							Optional[Any]       =    self.image_processor





       def __call__(  self				:     List[str]   , __lowerCamelCase				:     List[str]=None   , __lowerCamelCase				:     List[str]=None   , __lowerCamelCase				:     List[Any]=None   , **__lowerCamelCase				:     Optional[Any]			):


             '''simple docstring'''



             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:
                   lowerCamelCase__						:							List[str]       =    self.tokenizer(_lowerCAmelCase   , return_tensors=_lowerCAmelCase   , **_lowerCAmelCase			)

             if images is not None:
                   lowerCamelCase__						:							str       =    self.image_processor(_lowerCAmelCase   , return_tensors=_lowerCAmelCase   , **_lowerCAmelCase			)

             if text is not None and images is not None:
                   lowerCamelCase__						:							Optional[Any]       =    image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**_lowerCAmelCase			)   , tensor_type=_lowerCAmelCase			)





       def 		lowerCAmelCase	(  self				:     Any   , *__lowerCamelCase				:     Any   , **__lowerCamelCase				:     Dict			):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*_lowerCAmelCase   , **_lowerCAmelCase			)





       def 		lowerCAmelCase	(  self				:     str   , *__lowerCamelCase				:     Optional[int]   , **__lowerCamelCase				:     Any			):


             '''simple docstring'''



             return self.tokenizer.decode(*_lowerCAmelCase   , **_lowerCAmelCase			)





       @property
       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    self.tokenizer.model_input_names
             lowerCamelCase__						:							Union[str, Any]       =    self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)





       @property
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   , _lowerCAmelCase   , )
             return self.image_processor_class





       @property
       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   , _lowerCAmelCase   , )
             return self.image_processor



import warnings

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







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

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

           __snake_case       :				int 			=    image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""			)
           if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""			)

           super().__init__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

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

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



