





"""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 lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # 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    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[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(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:	int							      =logging.get_logger(__name__)

lowerCAmelCase:	int							      ={
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""qdqbert"""



   def __init__(       self      ,   snake_case=3_0_5_2_2      ,   snake_case=7_6_8      ,   snake_case=1_2      ,   snake_case=1_2      ,   snake_case=3_0_7_2      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=5_1_2      ,   snake_case=2      ,   snake_case=0.02      ,   snake_case=1E-12      ,   snake_case=True      ,   snake_case=1      ,   snake_case=0      ,   snake_case=2      ,   **snake_case      ,   ) ->    List[str]:







      """simple docstring"""
      super().__init__(pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   **snake_case	)

      lowercase    :			Optional[Any]  =       vocab_size
      lowercase    :			int  =       max_position_embeddings
      lowercase    :			Dict  =       hidden_size
      lowercase    :			Union[str, Any]  =       num_hidden_layers
      lowercase    :			str  =       num_attention_heads
      lowercase    :			List[str]  =       intermediate_size
      lowercase    :			List[str]  =       hidden_act
      lowercase    :			List[str]  =       hidden_dropout_prob
      lowercase    :			Optional[Any]  =       attention_probs_dropout_prob
      lowercase    :			Optional[Any]  =       initializer_range
      lowercase    :			str  =       type_vocab_size
      lowercase    :			Optional[int]  =       layer_norm_eps
      lowercase    :			Optional[int]  =       use_cache


