




from typing import Dict

from .base import GenericTensor, Pipeline




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def     _lowercase     (    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Optional[int]:
           if tokenize_kwargs is None:
                  lowerCamelCase   :      List[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)"     )
                  lowerCamelCase   :      Union[str, Any]			 =					truncation

           lowerCamelCase   :      Optional[Any]			 =					tokenize_kwargs

           lowerCamelCase   :      List[str]			 =					{}
           if return_tensors is not None:
                  lowerCamelCase   :      Tuple			 =					return_tensors

           return preprocess_params, {}, postprocess_params







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Dict[str, GenericTensor]:
           lowerCamelCase   :      int			 =					self.framework
           lowerCamelCase   :      Optional[int]			 =					self.tokenizer(UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     )
           return model_inputs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[Any]:
           lowerCamelCase   :      Dict			 =					self.model(**UpperCamelCase__     )
           return model_outputs







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__=False     )     ->      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     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Optional[Any]:
           return super().__call__(*UpperCamelCase__     ,  **UpperCamelCase__     )












from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :      List[Any]  =   {'processing_layoutxlm': ['LayoutXLMProcessor']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   ['LayoutXLMTokenizer']

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

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      str  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






