


'''simple docstring'''






import warnings

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




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""image_processor""", """tokenizer"""]
 _lowerCAmelCase 					=	"""CLIPImageProcessor"""
 _lowerCAmelCase 					=	("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				**__magic_name__						)					->							Optional[Any]:
      _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.'      ,				__magic_name__      ,				)
           _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__(__magic_name__      ,				__magic_name__						)
 def __call__( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=None      ,				**__magic_name__						)					->							List[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(__magic_name__      ,				return_tensors=__magic_name__      ,				**__magic_name__						)

      if images is not None:
           _a			      =							self.image_processor(__magic_name__      ,				return_tensors=__magic_name__      ,				**__magic_name__						)

      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(**__magic_name__						)      ,				tensor_type=__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      return self.tokenizer.batch_decode(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							str:
      return self.tokenizer.decode(*__magic_name__      ,				**__magic_name__						)
 @property
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							self.tokenizer.model_input_names
      _a			      =							self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,				__magic_name__      ,				)
      return self.image_processor_class




 @property
 def 							__UpperCAmelCase		( self						)					->							str:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,				__magic_name__      ,				)
      return self.image_processor





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_					:    int          =    {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    ["ViTFeatureExtractor"]
 a_					:    Optional[int]          =    ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Any          =    [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

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

