





'''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










'''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



