


'''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


lowercase__					  =		logging.get_logger(__name__)

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





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					RetriBertConfig
     UpperCAmelCase			       =					None
     UpperCAmelCase			       =					'''retribert'''






     def 			a_       (      self  ,				__UpperCamelCase		)							->     Optional[int]:
          if isinstance(__UpperCamelCase  ,				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(__UpperCamelCase  ,				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(__UpperCamelCase  ,				nn.LayerNorm		):
               module.bias.data.zero_()
               module.weight.data.fill_(1.0		)







lowercase__					  =		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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):






     def __init__(      self  ,				__UpperCamelCase		)							->     None:
          super().__init__(__UpperCamelCase		)
          _a            =		config.projection_dim

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

          _a            =		nn.CrossEntropyLoss(reduction="mean"		)

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






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase=-1  ,				)							->     List[Any]:
          # reproduces BERT forward pass with checkpointing
          if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
               return sent_encoder(__UpperCamelCase  ,				attention_mask=__UpperCamelCase		)[1]
          else:
               # prepare implicit variables
               _a            =		input_ids.device
               _a            =		input_ids.size()
               _a            =		torch.zeros(__UpperCamelCase  ,				dtype=torch.long  ,				device=__UpperCamelCase		)
               _a            =		[None] * sent_encoder.config.num_hidden_layers
               _a            =		sent_encoder.get_extended_attention_mask(
                   __UpperCamelCase  ,				__UpperCamelCase		)

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

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






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=None  ,				__UpperCamelCase=-1  ,				)							->     Optional[Any]:
          _a            =		self.embed_sentences_checkpointed(
              __UpperCamelCase  ,				__UpperCamelCase  ,				self.bert_query  ,				__UpperCamelCase  ,				)
          return self.project_query(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=None  ,				__UpperCamelCase=-1  ,				)							->     Optional[Any]:
          _a            =		self.embed_sentences_checkpointed(
              __UpperCamelCase  ,				__UpperCamelCase  ,				self.bert_query if self.bert_doc is None else self.bert_doc  ,				__UpperCamelCase  ,				)
          return self.project_doc(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = -1  ,				)							->     torch.FloatTensor:
          _a            =		input_ids_query.device
          _a            =		self.embed_questions(__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          _a            =		self.embed_answers(__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          _a            =		torch.mm(__UpperCamelCase  ,				a_reps.t()		)
          _a            =		self.ce_loss(__UpperCamelCase  ,				torch.arange(compare_scores.shape[1]		).to(__UpperCamelCase		)		)
          _a            =		self.ce_loss(compare_scores.t()  ,				torch.arange(compare_scores.shape[0]		).to(__UpperCamelCase		)		)
          _a            =		(loss_qa + loss_aq) / 2
          return loss





'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			float   ,	__lowerCamelCase		:			float   ,	__lowerCamelCase		:			float					)   ->  float:







     '''simple docstring'''






     if mass < 0:
          raise ValueError("The mass of the body cannot be negative"					)
     if radius <= 0:
          raise ValueError("The radius is always a positive non zero integer"					)
     return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

