



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_									=   logging.get_logger(__name__)

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







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : int       =						RetriBertConfig
   A__      : List[Any]       =						None
   A__      : Tuple       =						"retribert"


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     List[str]							)	->      Optional[Any]:
       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_									=   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." ,							__snake_case ,							)
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     Union[str, Any]  ,lowerCamelCase_:     RetriBertConfig							)	->      None:
       super().__init__(lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					config.projection_dim

       UpperCAmelCase_     :       Union[str, Any]  		=					BertModel(lowerCamelCase_							)
       UpperCAmelCase_     :       str  		=					None if config.share_encoders else BertModel(lowerCamelCase_							)
       UpperCAmelCase_     :       Dict  		=					nn.Dropout(config.hidden_dropout_prob							)
       UpperCAmelCase_     :       Optional[int]  		=					nn.Linear(config.hidden_size  ,config.projection_dim  ,bias=lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					nn.Linear(config.hidden_size  ,config.projection_dim  ,bias=lowerCamelCase_							)

       UpperCAmelCase_     :       Any  		=					nn.CrossEntropyLoss(reduction="""mean"""							)

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


   def       A__    (    self:     Any  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     int=-1  ,)	->      Optional[Any]:
       # reproduces BERT forward pass with checkpointing
       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_     :       str  		=					input_ids.device
           UpperCAmelCase_     :       int  		=					input_ids.size()
           UpperCAmelCase_     :       List[Any]  		=					torch.zeros(lowerCamelCase_  ,dtype=torch.long  ,device=lowerCamelCase_							)
           UpperCAmelCase_     :       int  		=					[None] * sent_encoder.config.num_hidden_layers
           UpperCAmelCase_     :       torch.Tensor  		=					sent_encoder.get_extended_attention_mask(
               lowerCamelCase_  ,lowerCamelCase_							)

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

           # run embedding layer on everything at once
           UpperCAmelCase_     :       Any  		=					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_     :       Optional[int]  		=					[]
           for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size							)							):
               UpperCAmelCase_     :       Union[str, Any]  		=					embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               UpperCAmelCase_     :       Optional[Any]  		=					extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               UpperCAmelCase_     :       Optional[Any]  		=					checkpoint.checkpoint(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)
               pooled_output_list.append(lowerCamelCase_							)
           return torch.cat(lowerCamelCase_  ,dim=0							)


   def       A__    (    self:     Any  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     List[str]=None  ,lowerCamelCase_:     Optional[int]=-1  ,)	->      str:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.embed_sentences_checkpointed(
           lowerCamelCase_  ,lowerCamelCase_  ,self.bert_query  ,lowerCamelCase_  ,)
       return self.project_query(lowerCamelCase_							)


   def       A__    (    self:     Dict  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Any=None  ,lowerCamelCase_:     List[Any]=-1  ,)	->      Tuple:
       UpperCAmelCase_     :       Optional[Any]  		=					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       A__    (    self:     Any  ,lowerCamelCase_:     torch.LongTensor  ,lowerCamelCase_:     Optional[torch.FloatTensor]  ,lowerCamelCase_:     torch.LongTensor  ,lowerCamelCase_:     Optional[torch.FloatTensor]  ,lowerCamelCase_:     int = -1  ,)	->      torch.FloatTensor:
       UpperCAmelCase_     :       Union[str, Any]  		=					input_ids_query.device
       UpperCAmelCase_     :       List[str]  		=					self.embed_questions(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)
       UpperCAmelCase_     :       str  		=					self.embed_answers(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					torch.mm(lowerCamelCase_  ,a_reps.t()							)
       UpperCAmelCase_     :       Any  		=					self.ce_loss(lowerCamelCase_  ,torch.arange(compare_scores.shape[1]							).to(lowerCamelCase_							)							)
       UpperCAmelCase_     :       Optional[int]  		=					self.ce_loss(compare_scores.t()  ,torch.arange(compare_scores.shape[0]							).to(lowerCamelCase_							)							)
       UpperCAmelCase_     :       Optional[int]  		=					(loss_qa + loss_aq) / 2
       return loss





from math import cos, sin







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					float(_a    )
    return [[scaling_factor * int(x == y    ) for x in range(2    )] for y in range(2    )]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cos(_a    ), sin(_a    )
    return [[c, -s], [s, c]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Dict  		=					c * s
    return [[c * c, cs], [cs, s * s]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					c * s
    return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"    {scaling(5) = }")
print(F"  {rotation(45) = }")
print(F"{projection(45) = }")






print(F"{reflection(45) = }")
