from typing import Dict

from .base import GenericTensor, Pipeline


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
		"""simple docstring"""







		def    __A     (							self :       Optional[int]  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       Optional[Any]=None  ,     **__magic_name__ :       List[str]   )  ->	Dict:
								if tokenize_kwargs is None:
														SCREAMING_SNAKE_CASE_          =  {}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"   )
														SCREAMING_SNAKE_CASE_          =  truncation

								SCREAMING_SNAKE_CASE_          =  tokenize_kwargs

								SCREAMING_SNAKE_CASE_          =  {}
								if return_tensors is not None:
														SCREAMING_SNAKE_CASE_          =  return_tensors

								return preprocess_params, {}, postprocess_params







		def    __A     (							self :       Tuple  ,     __magic_name__ :       int  ,     **__magic_name__ :       Dict   )  ->	Dict[str, GenericTensor]:
								SCREAMING_SNAKE_CASE_          =  self.framework
								SCREAMING_SNAKE_CASE_          =  self.tokenizer(__magic_name__  ,     return_tensors=__magic_name__  ,     **__magic_name__   )
								return model_inputs







		def    __A     (							self :       List[Any]  ,     __magic_name__ :       Dict   )  ->	List[Any]:
								SCREAMING_SNAKE_CASE_          =  self.model(**__magic_name__   )
								return model_outputs







		def    __A     (							self :       List[Any]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Union[str, Any]=False   )  ->	List[str]:
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()







		def __call__(							self :       List[str]  ,     *__magic_name__ :       Union[str, Any]  ,     **__magic_name__ :       Tuple   )  ->	Tuple:
								return super().__call__(*__magic_name__  ,     **__magic_name__   )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A     :  Dict					     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Optional[Any]					     =       ["BartphoTokenizer"]

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

else:
      import sys

      A     :  Optional[int]					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


