
"""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


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)

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






class      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Optional[Any]   			=      RetriBertConfig
       _lowerCamelCase				:				Any   			=      None
       _lowerCamelCase				:				Optional[Any]   			=      'retribert'







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''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		)



SCREAMING_SNAKE_CASE_  					= 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.'					,						lowerCAmelCase_					,						)
class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,lowerCamelCase_		):



          '''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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_=-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(*lowerCamelCase_		):
                _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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None				,lowerCamelCase_=-1				,):



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None				,lowerCamelCase_=-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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_ = -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"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

