


import warnings

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



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""image_processor""", """tokenizer"""]
       _lowercase											=     """CLIPImageProcessor"""
       _lowercase											=     ("""CLIPTokenizer""", """CLIPTokenizerFast""")







       def __init__(			self: Tuple,A_: Dict=None,A_: Dict=None,**A_: str					):






            '''simple docstring'''




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

            __UpperCamelCase =			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__(A_,A_					)







       def __call__(			self: Optional[int],A_: Union[str, Any]=None,A_: int=None,A_: List[Any]=None,**A_: Optional[Any]					):






            '''simple docstring'''




            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 =			self.tokenizer(A_,return_tensors=A_,**A_					)

            if images is not None:
                 __UpperCamelCase =			self.image_processor(A_,return_tensors=A_,**A_					)

            if text is not None and images is not None:
                 __UpperCamelCase =			image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**A_					),tensor_type=A_					)







       def 		snake_case_	(			self: Optional[int],*A_: Tuple,**A_: Union[str, Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict,*A_: int,**A_: List[Any]					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.tokenizer.model_input_names
            __UpperCamelCase =			self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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




