



"""simple docstring"""




from typing import Dict

from .base import GenericTensor, Pipeline






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      Dict	,      A	:      Dict=None	,      A	:      int=None	,      A	:      Union[str, Any]=None	,      **A	:      Optional[int]		):
           if tokenize_kwargs is None:
                _UpperCAmelCase     :      Optional[Any]														=       {}

           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)"		)
                _UpperCAmelCase     :      List[Any]														=       truncation

           _UpperCAmelCase     :      Optional[Any]														=       tokenize_kwargs

           _UpperCAmelCase     :      Optional[int]														=       {}
           if return_tensors is not None:
                _UpperCAmelCase     :      Union[str, Any]														=       return_tensors

           return preprocess_params, {}, postprocess_params



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      List[str]	,      **A	:      Optional[int]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.framework
           _UpperCAmelCase     :      Optional[int]														=       self.tokenizer(A	,      return_tensors=A	,      **A		)
           return model_inputs



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      Tuple		):
           _UpperCAmelCase     :      List[Any]														=       self.model(**A		)
           return model_outputs



      def 		snake_case_   (	self	:      Any	,      A	:      List[str]	,      A	:      Optional[Any]=False		):
           # [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	:      str	,      *A	:      Optional[Any]	,      **A	:      List[Any]		):
           return super().__call__(*A	,      **A		)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase							:   List[Any]				=				{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:   int				=				[
     "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LiltForQuestionAnswering",
     "LiltForSequenceClassification",
     "LiltForTokenClassification",
     "LiltModel",
     "LiltPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lilt import (
      LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
      LiltPreTrainedModel,
  )

else:
 import sys

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