



'''simple docstring'''






import warnings

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

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""image_processor""", """tokenizer"""]
    lowercase__			=   """ViTImageProcessor"""
    lowercase__			=   ("""CLIPTokenizer""", """CLIPTokenizerFast""")







    def __init__(				self			:  List[Any]						,      __snake_case			:  List[Any]=None						,      __snake_case			:  Dict=None						,      **__snake_case			:  Optional[Any]     ):
      a						: Optional[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.'						,      __snake_case						,      )
        a						: List[str]					       =					kwargs.pop('feature_extractor'     )

      a						: Tuple					       =					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__(__snake_case						,      __snake_case     )







    def __call__(				self			:  int						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[Any]=None						,      __snake_case			:  List[str]=None						,      __snake_case			:  int=None						,      **__snake_case			:  str     ):
      if text is None and visual_prompt is None and images is None:
        raise ValueError('You have to specify either text, visual prompt or images.'     )

      if text is not None and visual_prompt is not None:
        raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'     )

      if text is not None:
        a						: Any					       =					self.tokenizer(__snake_case						,      return_tensors=__snake_case						,      **__snake_case     )

      if visual_prompt is not None:
        a						: int					       =					self.image_processor(__snake_case						,      return_tensors=__snake_case						,      **__snake_case     )

      if images is not None:
        a						: int					       =					self.image_processor(__snake_case						,      return_tensors=__snake_case						,      **__snake_case     )

      if visual_prompt is not None and images is not None:
        a						: Optional[Any]					       =					{
            'pixel_values': image_features.pixel_values,
            'conditional_pixel_values': prompt_features.pixel_values,
        }
        return encoding
      elif text is not None and images is not None:
        a						: List[Any]					       =					image_features.pixel_values
        return encoding
      elif text is not None:
        return encoding
      elif visual_prompt is not None:
        a						: Any					       =					{
            'conditional_pixel_values': prompt_features.pixel_values,
        }
        return encoding
      else:
        return BatchEncoding(data=dict(**__snake_case     )						,      tensor_type=__snake_case     )







    def       lowercase_	(				self			:  List[Any]						,      *__snake_case			:  List[Any]						,      **__snake_case			:  List[Any]     ):
      return self.tokenizer.batch_decode(*__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Any						,      *__snake_case			:  Any						,      **__snake_case			:  Optional[int]     ):
      return self.tokenizer.decode(*__snake_case						,      **__snake_case     )







    @property
    def       lowercase_	(				self			:  Tuple     ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,      __snake_case						,      )
      return self.image_processor_class







    @property
    def       lowercase_	(				self			:  Any     ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,      __snake_case						,      )
      return self.image_processor




'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase: List[Any]							=						{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: Optional[int]							=						['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: Dict							=						['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     lowerCAmelCase: Tuple							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)