






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				Optional[Any]							 =			['image_processor', 'tokenizer']
      a						:				Union[str, Any]							 =			'AutoImageProcessor'
      a						:				Union[str, Any]							 =			'AutoTokenizer'
      def __init__(   self      :    Optional[int]	,   __lowercase      :    Any=None	,   __lowercase      :    Any=None	,   **__lowercase      :    List[str]      )							->   Dict:
        __UpperCAmelCase							:			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."""	,   __lowercase	,   )
          __UpperCAmelCase							:			List[str]     =						kwargs.pop("""feature_extractor"""      )

        __UpperCAmelCase							:			Union[str, 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__(__lowercase	,   __lowercase      )
        __UpperCAmelCase							:			str     =						self.image_processor
      def __call__(   self      :    int	,   __lowercase      :    List[str]=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Optional[Any]=None	,   **__lowercase      :    Tuple      )							->   List[str]:

        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:
          __UpperCAmelCase							:			List[Any]     =						self.tokenizer(__lowercase	,   return_tensors=__lowercase	,   **__lowercase      )

        if images is not None:
          __UpperCAmelCase							:			Tuple     =						self.image_processor(__lowercase	,   return_tensors=__lowercase	,   **__lowercase      )

        if text is not None and images is not None:
          __UpperCAmelCase							:			Tuple     =						image_features.pixel_values
          return encoding
        elif text is not None:
          return encoding
        else:
          return BatchEncoding(data=dict(**__lowercase      )	,   tensor_type=__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[str]	,   *__lowercase      :    Optional[Any]	,   **__lowercase      :    str      )							->   Any:
        return self.tokenizer.batch_decode(*__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Dict	,   *__lowercase      :    List[str]	,   **__lowercase      :    str      )							->   Union[str, Any]:
        return self.tokenizer.decode(*__lowercase	,   **__lowercase      )
      @property
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   str:
        __UpperCAmelCase							:			Dict     =						self.tokenizer.model_input_names
        __UpperCAmelCase							:			Tuple     =						self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )
      @property
      def 			UpperCAmelCase   (   self      :    int      )							->   Optional[int]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,   __lowercase	,   )
        return self.image_processor_class




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










from typing import TYPE_CHECKING

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


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

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


