code: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  