




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class     a__ (      UpperCamelCase_   ):
     a	:    str	      =			["""image_processor""", """tokenizer"""]
     a	:    Tuple	      =			"""AutoImageProcessor"""
     a	:    List[str]	      =			"""AutoTokenizer"""







     def __init__(			self						,	A=None						,	A=None						,	**A				)						->					Optional[Any]:



          '''simple docstring'''



          a											=   None
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."						,	__UpperCamelCase						,	)
               a											=   kwargs.pop("feature_extractor"				)

          a											=   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__(__UpperCamelCase						,	__UpperCamelCase				)
          a											=   self.image_processor
          a											=   False







     def __call__(			self						,	*A						,	**A				)						->					Union[str, Any]:



          '''simple docstring'''



          if self._in_target_context_manager:
               return self.current_processor(*__UpperCamelCase						,	**__UpperCamelCase				)

          a											=   kwargs.pop("images"						,	__UpperCamelCase				)
          a											=   kwargs.pop("text"						,	__UpperCamelCase				)
          if len(__UpperCamelCase				) > 0:
               a											=   args[0]
               a											=   args[1:]

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

          if images is not None:
               a											=   self.image_processor(__UpperCamelCase						,	*__UpperCamelCase						,	**__UpperCamelCase				)
          if text is not None:
               a											=   self.tokenizer(__UpperCamelCase						,	**__UpperCamelCase				)

          if text is None:
               return inputs
          elif images is None:
               return encodings
          else:
               a											=   encodings['''input_ids''']
               return inputs







     def     lowerCAmelCase_       (			self						,	*A						,	**A				)						->					List[str]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	*A						,	**A				)						->					List[Any]:



          '''simple docstring'''



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







     @contextmanager
     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          warnings.warn(
              "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
              "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
              "your images inputs, or in a separate call."				)
          a											=   True
          a											=   self.tokenizer
          yield
          a											=   self.image_processor
          a											=   False







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



          '''simple docstring'''



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







     @property
     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   lowercase__   :    List[Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

