


'''simple docstring'''







import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


A      :     List[Any]										= logging.get_logger(__name__)

A      :     Optional[Any]										= [
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]


class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    RetriBertConfig
    a													=    None
    a													=    "retribert"


    def    A    (  self			:							Any      ,							SCREAMING_SNAKE_CASE			:							Dict):
           if isinstance(SCREAMING_SNAKE_CASE      ,							nn.Linear):
                  module.weight.data.normal_(mean=0.0      ,							std=self.config.initializer_range)
                  if module.bias is not None:
                         module.bias.data.zero_()
           elif isinstance(SCREAMING_SNAKE_CASE      ,							nn.Embedding):
                  module.weight.data.normal_(mean=0.0      ,							std=self.config.initializer_range)
                  if module.padding_idx is not None:
                         module.weight.data[module.padding_idx].zero_()
           elif isinstance(SCREAMING_SNAKE_CASE      ,							nn.LayerNorm):
                  module.bias.data.zero_()
                  module.weight.data.fill_(1.0)




A      :     int										= R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''


@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."				,					a_				,					)
class  __lowerCamelCase (       a_       ):
    """simple docstring"""


    def __init__(  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							RetriBertConfig):
           super().__init__(SCREAMING_SNAKE_CASE)
           _A     :	Union[str, Any]      =					config.projection_dim

           _A     :	Optional[int]      =					BertModel(SCREAMING_SNAKE_CASE)
           _A     :	List[str]      =					None if config.share_encoders else BertModel(SCREAMING_SNAKE_CASE)
           _A     :	str      =					nn.Dropout(config.hidden_dropout_prob)
           _A     :	Optional[int]      =					nn.Linear(config.hidden_size      ,							config.projection_dim      ,							bias=SCREAMING_SNAKE_CASE)
           _A     :	Any      =					nn.Linear(config.hidden_size      ,							config.projection_dim      ,							bias=SCREAMING_SNAKE_CASE)

           _A     :	Optional[Any]      =					nn.CrossEntropyLoss(reduction='mean')

           # Initialize weights and apply final processing
           self.post_init()


    def    A    (  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							str=-1      ,							):
           # reproduces BERT forward pass with checkpointing
           if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
                  return sent_encoder(SCREAMING_SNAKE_CASE      ,							attention_mask=SCREAMING_SNAKE_CASE)[1]
           else:
                  # prepare implicit variables
                  _A     :	List[str]      =					input_ids.device
                  _A     :	Dict      =					input_ids.size()
                  _A     :	Optional[int]      =					torch.zeros(SCREAMING_SNAKE_CASE      ,							dtype=torch.long      ,							device=SCREAMING_SNAKE_CASE)
                  _A     :	Optional[int]      =					[None] * sent_encoder.config.num_hidden_layers
                  _A     :	torch.Tensor      =					sent_encoder.get_extended_attention_mask(
                      SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

                  # define function for checkpointing
                  def partial_encode(*SCREAMING_SNAKE_CASE			:							Optional[Any]):
                         _A     :	Tuple      =					sent_encoder.encoder(
                             inputs[0]      ,							attention_mask=inputs[1]      ,							head_mask=SCREAMING_SNAKE_CASE      ,							)
                         _A     :	Dict      =					encoder_outputs[0]
                         _A     :	List[Any]      =					sent_encoder.pooler(SCREAMING_SNAKE_CASE)
                         return pooled_output

                  # run embedding layer on everything at once
                  _A     :	Union[str, Any]      =					sent_encoder.embeddings(
                      input_ids=SCREAMING_SNAKE_CASE      ,							position_ids=SCREAMING_SNAKE_CASE      ,							token_type_ids=SCREAMING_SNAKE_CASE      ,							inputs_embeds=SCREAMING_SNAKE_CASE)
                  # run encoding and pooling on one mini-batch at a time
                  _A     :	Optional[int]      =					[]
                  for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
                         _A     :	Optional[int]      =					embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                         _A     :	List[Any]      =					extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                         _A     :	Dict      =					checkpoint.checkpoint(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         pooled_output_list.append(SCREAMING_SNAKE_CASE)
                  return torch.cat(SCREAMING_SNAKE_CASE      ,							dim=0)


    def    A    (  self			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Any=None      ,							SCREAMING_SNAKE_CASE			:							int=-1      ,							):
           _A     :	Any      =					self.embed_sentences_checkpointed(
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							self.bert_query      ,							SCREAMING_SNAKE_CASE      ,							)
           return self.project_query(SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=None      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=-1      ,							):
           _A     :	List[Any]      =					self.embed_sentences_checkpointed(
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							self.bert_query if self.bert_doc is None else self.bert_doc      ,							SCREAMING_SNAKE_CASE      ,							)
           return self.project_doc(SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							torch.LongTensor      ,							SCREAMING_SNAKE_CASE			:							Optional[torch.FloatTensor]      ,							SCREAMING_SNAKE_CASE			:							torch.LongTensor      ,							SCREAMING_SNAKE_CASE			:							Optional[torch.FloatTensor]      ,							SCREAMING_SNAKE_CASE			:							int = -1      ,							):
           _A     :	List[str]      =					input_ids_query.device
           _A     :	Any      =					self.embed_questions(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Dict      =					self.embed_answers(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Optional[int]      =					torch.mm(SCREAMING_SNAKE_CASE      ,							a_reps.t())
           _A     :	List[Any]      =					self.ce_loss(SCREAMING_SNAKE_CASE      ,							torch.arange(compare_scores.shape[1]).to(SCREAMING_SNAKE_CASE))
           _A     :	Dict      =					self.ce_loss(compare_scores.t()      ,							torch.arange(compare_scores.shape[0]).to(SCREAMING_SNAKE_CASE))
           _A     :	List[Any]      =					(loss_qa + loss_aq) / 2
           return loss










'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






