code: 
  
  
  
  
  
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __lowerCAmelCase							:			int				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Dict				={
      "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
      # See all RemBERT models at https://huggingface.co/models?filter=rembert
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """rembert"""
   def __init__(       self   :Union[str, Any]		, lowercase_   :Tuple=25_03_00		, lowercase_   :str=11_52		, lowercase_   :Dict=32		, lowercase_   :List[str]=18		, lowercase_   :List[Any]=2_56		, lowercase_   :Optional[int]=16_64		, lowercase_   :Tuple=46_08		, lowercase_   :Tuple="gelu"		, lowercase_   :Optional[int]=0.0		, lowercase_   :Tuple=0.0		, lowercase_   :Dict=0.1		, lowercase_   :Tuple=5_12		, lowercase_   :Tuple=2		, lowercase_   :Tuple=0.0_2		, lowercase_   :Dict=1E-12		, lowercase_   :Any=True		, lowercase_   :str=0		, lowercase_   :str=3_12		, lowercase_   :Optional[Any]=3_13		, **lowercase_   :List[str]		, )->							Any:
       super().__init__(pad_token_id=lowercase_		, bos_token_id=lowercase_		, eos_token_id=lowercase_		, **lowercase_							)
  
       A__       =    vocab_size
       A__       =    input_embedding_size
       A__       =    output_embedding_size
       A__       =    max_position_embeddings
       A__       =    hidden_size
       A__       =    num_hidden_layers
       A__       =    num_attention_heads
       A__       =    intermediate_size
       A__       =    hidden_act
       A__       =    hidden_dropout_prob
       A__       =    attention_probs_dropout_prob
       A__       =    classifier_dropout_prob
       A__       =    initializer_range
       A__       =    type_vocab_size
       A__       =    layer_norm_eps
       A__       =    use_cache
       A__       =    False
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   @property
   def   UpperCAmelCase_							(       self   :str							)->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           A__       =    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           A__       =    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]							)
  
  
  
   @property
   def   UpperCAmelCase_							(       self   :Tuple							)->							float:
       return 1E-4
  
  
  
  
style_context: 
  
  
  
  
  
  '''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
  
  
  
  