
"""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


UpperCamelCase      :							str          =					logging.get_logger(__name__)

UpperCamelCase      :							str          =					[
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]

class __lowerCAmelCase						( snake_case_     ):
     lowercase		  =		RetriBertConfig
     lowercase		  =		None
     lowercase		  =		'retribert'





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      if isinstance(lowerCamelCase_      ,	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(lowerCamelCase_      ,	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(lowerCamelCase_      ,	nn.LayerNorm ):
       module.bias.data.zero_()
       module.weight.data.fill_(1.0 )


UpperCamelCase      :							Any          =					r"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."     ,    snake_case_     ,    )
class __lowerCAmelCase						( snake_case_     ):





     def __init__(      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      super().__init__(lowerCamelCase_ )
      __UpperCamelCase							    =			config.projection_dim

      __UpperCamelCase							    =			BertModel(lowerCamelCase_ )
      __UpperCamelCase							    =			None if config.share_encoders else BertModel(lowerCamelCase_ )
      __UpperCamelCase							    =			nn.Dropout(config.hidden_dropout_prob )
      __UpperCamelCase							    =			nn.Linear(config.hidden_size      ,	config.projection_dim      ,	bias=lowerCamelCase_ )
      __UpperCamelCase							    =			nn.Linear(config.hidden_size      ,	config.projection_dim      ,	bias=lowerCamelCase_ )

      __UpperCamelCase							    =			nn.CrossEntropyLoss(reduction='mean' )

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





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=-1      ,	):

      '''simple docstring'''




      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
       return sent_encoder(lowerCamelCase_      ,	attention_mask=lowerCamelCase_ )[1]
      else:
       # prepare implicit variables
       __UpperCamelCase							    =			input_ids.device
       __UpperCamelCase							    =			input_ids.size()
       __UpperCamelCase							    =			torch.zeros(lowerCamelCase_      ,	dtype=torch.long      ,	device=lowerCamelCase_ )
       __UpperCamelCase							    =			[None] * sent_encoder.config.num_hidden_layers
       __UpperCamelCase							    =			sent_encoder.get_extended_attention_mask(
           lowerCamelCase_      ,	lowerCamelCase_ )

       # define function for checkpointing
       def partial_encode(*__UpperCAmelCase ):
        __UpperCamelCase							    =			sent_encoder.encoder(
            inputs[0]      ,	attention_mask=inputs[1]      ,	head_mask=lowerCamelCase_      ,	)
        __UpperCamelCase							    =			encoder_outputs[0]
        __UpperCamelCase							    =			sent_encoder.pooler(lowerCamelCase_ )
        return pooled_output

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





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=-1      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			self.embed_sentences_checkpointed(
          lowerCamelCase_      ,	lowerCamelCase_      ,	self.bert_query      ,	lowerCamelCase_      ,	)
      return self.project_query(lowerCamelCase_ )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=-1      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			self.embed_sentences_checkpointed(
          lowerCamelCase_      ,	lowerCamelCase_      ,	self.bert_query if self.bert_doc is None else self.bert_doc      ,	lowerCamelCase_      ,	)
      return self.project_doc(lowerCamelCase_ )







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase = -1      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			input_ids_query.device
      __UpperCamelCase							    =			self.embed_questions(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_ )
      __UpperCamelCase							    =			self.embed_answers(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_ )
      __UpperCamelCase							    =			torch.mm(lowerCamelCase_      ,	a_reps.t() )
      __UpperCamelCase							    =			self.ce_loss(lowerCamelCase_      ,	torch.arange(compare_scores.shape[1] ).to(lowerCamelCase_ ) )
      __UpperCamelCase							    =			self.ce_loss(compare_scores.t()      ,	torch.arange(compare_scores.shape[0] ).to(lowerCamelCase_ ) )
      __UpperCamelCase							    =			(loss_qa + loss_aq) / 2
      return loss











"""simple docstring"""
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		HfArgumentParser(_lowerCamelCase							)
 try:
  lowerCamelCase__	:     Dict								=		parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  lowerCamelCase__	:     Optional[int]								=		'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  lowerCamelCase__	:     Optional[Any]								=		' '.join(str(_lowerCamelCase							).split(' '							)[:-1]							)
  lowerCamelCase__	:     int								=		''
  lowerCamelCase__	:     Optional[int]								=		eval(str(_lowerCamelCase							).split(' '							)[-1]							)
  lowerCamelCase__	:     Tuple								=		[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]							)
   else:
    wrong_args.append(_lowerCamelCase							)
  if len(_lowerCamelCase							) > 0:
   lowerCamelCase__	:     Optional[int]								=		full_error_msg + begin_error_msg + str(_lowerCamelCase							)
  raise ValueError(_lowerCamelCase							)

 lowerCamelCase__	:     Any								=		PyTorchBenchmark(args=_lowerCamelCase							)
 benchmark.run()


if __name__ == "__main__":
      main()

