import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class 						snake_case_ (	tf.keras.layers.Layer ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

       lowercase__				:     Optional[int]					      =       vocab_list
       lowercase__				:     List[Any]					      =       do_lower_case
       lowercase__				:     Dict					      =       cls_token_id or vocab_list.index("[CLS]"						)
       lowercase__				:     Any					      =       sep_token_id or vocab_list.index("[SEP]"						)
       lowercase__				:     Union[str, Any]					      =       pad_token_id or vocab_list.index("[PAD]"						)
       lowercase__				:     List[str]					      =       ShrinkLongestTrimmer(max_length - 3			,		axis=1						)  # Allow room for special tokens
       lowercase__				:     Dict					      =       max_length
       lowercase__				:     List[str]					      =       padding
       lowercase__				:     Tuple					      =       truncation
       lowercase__				:     Tuple					      =       pad_to_multiple_of
       lowercase__				:     Optional[Any]					      =       return_token_type_ids
       lowercase__				:     Dict					      =       return_attention_mask






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       lowercase__				:     Union[str, Any]					      =       tokenizer.get_vocab()
       lowercase__				:     Tuple					      =       sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,		key=lambda lowercase_						: x[1]						)
       lowercase__				:     Tuple					      =       [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowercase_			,		do_lower_case=lowercase_			,		cls_token_id=lowercase_			,		sep_token_id=lowercase_			,		pad_token_id=lowercase_			,		**lowercase_			,		)






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     str					      =       self.padding
       if padding not in ("longest", "max_length"):
           raise ValueError("Padding must be either 'longest' or 'max_length'!"						)
       if max_length is not None and text_pair is not None:
           # Because we have to instantiate a Trimmer to do it properly
           raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"						)
       if max_length is None:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None:
           if text.shape.rank > 1:
               raise ValueError("text argument should not be multidimensional when a text pair is supplied!"						)
           if text_pair.shape.rank > 1:
               raise ValueError("text_pair should not be multidimensional!"						)
       if text.shape.rank == 2:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       return {
           "vocab_list": self.vocab_list,
           "do_lower_case": self.do_lower_case,
           "cls_token_id": self.cls_token_id,
           "sep_token_id": self.sep_token_id,
           "pad_token_id": self.pad_token_id,
       }




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_ (	__A ):
   __A			:	Optional[Any]        =       RetriBertConfig
   __A			:	str        =       None
   __A			:	Any        =       "retribert"






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[Any]						)      ->		List[str]:
       if isinstance(lowercase_			,		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(lowercase_			,		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(lowercase_			,		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."						,__A						,)
class 						snake_case_ (	__A ):






   def __init__(   self       :  Dict			,		lowercase_       :  RetriBertConfig						)      ->		None:
       super().__init__(lowercase_						)
       lowercase__				:     str					      =       config.projection_dim

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

       lowercase__				:     Optional[Any]					      =       nn.CrossEntropyLoss(reduction="mean"						)

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






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

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

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






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  Any			,		lowercase_       :  Any=None			,		lowercase_       :  Union[str, Any]=-1			,		)      ->		List[str]:
       lowercase__				:     Optional[Any]					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query			,		lowercase_			,		)
       return self.project_query(lowercase_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Dict=-1			,		)      ->		Optional[int]:
       lowercase__				:     str					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query if self.bert_doc is None else self.bert_doc			,		lowercase_			,		)
       return self.project_doc(lowercase_						)







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



