



"""simple docstring"""







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy





class   UpperCamelCase	(     SCREAMING_SNAKE_CASE__       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  '''MarkupLMFeatureExtractor'''
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  True






	def __call__(     self				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_ = True				,UpperCAmelCase_ = False				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = 0				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = None				,UpperCAmelCase_ = False				,UpperCAmelCase_ = False				,UpperCAmelCase_ = False				,UpperCAmelCase_ = False				,UpperCAmelCase_ = True				,UpperCAmelCase_ = None				,**UpperCAmelCase_				,):
			# first, create nodes and xpaths
			if self.parse_html:
					if html_strings is None:
							raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""   )

					if nodes is not None or xpaths is not None or node_labels is not None:
							raise ValueError(
							    """Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""   )

					_lowercase				: Tuple     =   self.feature_extractor(lowerCAmelCase_   )
					_lowercase				: Union[str, Any]     =   features['''nodes''']
					_lowercase				: Union[str, Any]     =   features['''xpaths''']
			else:
					if html_strings is not None:
							raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""   )
					if nodes is None or xpaths is None:
							raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""   )

        # # second, apply the tokenizer
			if questions is not None and self.parse_html:
					if isinstance(lowerCAmelCase_				,lowerCAmelCase_   ):
							_lowercase				: Optional[Any]     =   [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

			_lowercase				: str     =   self.tokenizer(
			    text=questions if questions is not None else nodes				,text_pair=nodes if questions is not None else None				,xpaths=lowerCAmelCase_				,node_labels=lowerCAmelCase_				,add_special_tokens=lowerCAmelCase_				,padding=lowerCAmelCase_				,truncation=lowerCAmelCase_				,max_length=lowerCAmelCase_				,stride=lowerCAmelCase_				,pad_to_multiple_of=lowerCAmelCase_				,return_token_type_ids=lowerCAmelCase_				,return_attention_mask=lowerCAmelCase_				,return_overflowing_tokens=lowerCAmelCase_				,return_special_tokens_mask=lowerCAmelCase_				,return_offsets_mapping=lowerCAmelCase_				,return_length=lowerCAmelCase_				,verbose=lowerCAmelCase_				,return_tensors=lowerCAmelCase_				,**lowerCAmelCase_				,)

			return encoded_inputs






	def    lowerCamelCase__					(     self				,*UpperCAmelCase_				,**UpperCAmelCase_   ):
			return self.tokenizer.batch_decode(*lowerCAmelCase_				,**lowerCAmelCase_   )






	def    lowerCamelCase__					(     self				,*UpperCAmelCase_				,**UpperCAmelCase_   ):
			return self.tokenizer.decode(*lowerCAmelCase_				,**lowerCAmelCase_   )






	@property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   self.tokenizer.model_input_names
			return tokenizer_input_names





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__						:	Any	     = {
    'configuration_decision_transformer': [
        'DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DecisionTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:	Optional[Any]	     = [
       'DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DecisionTransformerGPT2Model',
       'DecisionTransformerGPT2PreTrainedModel',
       'DecisionTransformerModel',
       'DecisionTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:	Optional[Any]	     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



