

import warnings

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







class  UpperCamelCase_      (	__UpperCAmelCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   ["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__					:			int					    =   """VideoMAEImageProcessor"""
  SCREAMING_SNAKE_CASE__					:			str					    =   ("""CLIPTokenizer""", """CLIPTokenizerFast""")



  def __init__(						self     :					Union[str, Any]  ,	A_     :					Optional[Any]=None  ,	A_     :					Any=None  ,	**A_     :					Any						)       ->			Optional[int]:
      SCREAMING_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."  ,	UpperCAmelCase_  ,	)
          SCREAMING_SNAKE_CASE__         =    kwargs.pop("feature_extractor"						)

      SCREAMING_SNAKE_CASE__         =    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_						)
      SCREAMING_SNAKE_CASE__         =    self.image_processor



  def __call__(						self     :					Any  ,	A_     :					int=None  ,	A_     :					Dict=None  ,	A_     :					int=None  ,	**A_     :					Union[str, Any]						)       ->			Dict:

      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:
          SCREAMING_SNAKE_CASE__         =    self.tokenizer(UpperCAmelCase_  ,	return_tensors=UpperCAmelCase_  ,	**UpperCAmelCase_						)

      if videos is not None:
          SCREAMING_SNAKE_CASE__         =    self.image_processor(UpperCAmelCase_  ,	return_tensors=UpperCAmelCase_  ,	**UpperCAmelCase_						)

      if text is not None and videos is not None:
          SCREAMING_SNAKE_CASE__         =    image_features.pixel_values
          return encoding
      elif text is not None:
          return encoding
      else:
          return BatchEncoding(data=dict(**UpperCAmelCase_						)  ,	tensor_type=UpperCAmelCase_						)



  def   _UpperCamelCase     (						self     :					List[str]  ,	*A_     :					Union[str, Any]  ,	**A_     :					Union[str, Any]						)       ->			Any:
      return self.tokenizer.batch_decode(*UpperCAmelCase_  ,	**UpperCAmelCase_						)



  def   _UpperCamelCase     (						self     :					List[str]  ,	*A_     :					Dict  ,	**A_     :					str						)       ->			Union[str, Any]:
      return self.tokenizer.decode(*UpperCAmelCase_  ,	**UpperCAmelCase_						)



  @property
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Optional[int]:
      return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



  @property
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[str]:
      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   _UpperCamelCase     (						self     :					List[str]						)       ->			Any:
      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,
)


UpperCAmelCase_    ={
    """configuration_bark""": [
        """BARK_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BarkCoarseConfig""",
        """BarkConfig""",
        """BarkFineConfig""",
        """BarkSemanticConfig""",
    ],
    """processing_bark""": ["""BarkProcessor"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """BARK_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BarkFineModel""",
		    """BarkSemanticModel""",
		    """BarkCoarseModel""",
		    """BarkModel""",
		    """BarkPreTrainedModel""",
		    """BarkCausalModel""",
		]

if TYPE_CHECKING:
		from .configuration_bark import (
		    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BarkCoarseConfig,
		    BarkConfig,
		    BarkFineConfig,
		    BarkSemanticConfig,
		)
		from .processing_bark import BarkProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bark import (
				    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BarkCausalModel,
				    BarkCoarseModel,
				    BarkFineModel,
				    BarkModel,
				    BarkPreTrainedModel,
				    BarkSemanticModel,
				)

else:
		import sys

		UpperCAmelCase_    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



