import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCamelCase													=		{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCamelCase													=		{'''Helsinki-NLP/opus-mt-en-de''': 512}
UpperCamelCase													=		{}



class 						snake_case_ (	__A ):
   __A			:	Dict        =       VOCAB_FILES_NAMES
   __A			:	Optional[int]        =       PRETRAINED_VOCAB_FILES_MAP
   __A			:	Optional[int]        =       PRETRAINED_INIT_CONFIGURATION
   __A			:	List[Any]        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:	Any        =       ['''input_ids''', '''attention_mask''']
   __A			:	Any        =       re.compile(">>.+<<" )  # type: re.Pattern






   def __init__(   self       :  Tuple			,		lowercase_       :  int			,		lowercase_       :  Any			,		lowercase_       :  Union[str, Any]			,		lowercase_       :  List[str]=None			,		lowercase_       :  List[Any]=None			,		lowercase_       :  List[str]=None			,		lowercase_       :  int="<unk>"			,		lowercase_       :  List[Any]="</s>"			,		lowercase_       :  Dict="<pad>"			,		lowercase_       :  str=5_12			,		lowercase_       :  str = None			,		lowercase_       :  Tuple=False			,		**lowercase_       :  int			,		)      ->		None:
       lowercase__				:     Dict					      =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
           source_lang=a_			,		target_lang=a_			,		unk_token=a_			,		eos_token=a_			,		pad_token=a_			,		model_max_length=a_			,		sp_model_kwargs=self.sp_model_kwargs			,		target_vocab_file=a_			,		separate_vocabs=a_			,		**a_			,		)
       assert Path(a_						).exists(), F'''cannot find spm source {source_spm}'''

       lowercase__				:     Optional[Any]					      =       separate_vocabs
       lowercase__				:     Union[str, Any]					      =       load_json(a_						)
       if self.unk_token not in self.encoder:
           raise KeyError("<unk> token must be in vocab"						)
       assert self.pad_token in self.encoder

       if separate_vocabs:
           lowercase__				:     Dict					      =       load_json(a_						)
           lowercase__				:     Optional[int]					      =       {v: k for k, v in self.target_encoder.items()}
           lowercase__				:     List[str]					      =       []
       else:
           lowercase__				:     Union[str, Any]					      =       {v: k for k, v in self.encoder.items()}
           lowercase__				:     str					      =       [k for k in self.encoder if k.startswith(">>"						) and k.endswith("<<"						)]

       lowercase__				:     int					      =       source_lang
       lowercase__				:     List[Any]					      =       target_lang
       lowercase__				:     Tuple					      =       [source_spm, target_spm]

       # load SentencePiece model for pre-processing
       lowercase__				:     int					      =       load_spm(a_			,		self.sp_model_kwargs						)
       lowercase__				:     Any					      =       load_spm(a_			,		self.sp_model_kwargs						)
       lowercase__				:     Tuple					      =       self.spm_source
       lowercase__				:     Tuple					      =       self.encoder

       # Multilingual target side: default to using first supported language code.

       self._setup_normalizer()






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Optional[Any]:
       try:
           from sacremoses import MosesPunctNormalizer

           lowercase__				:     List[str]					      =       MosesPunctNormalizer(self.source_lang						).normalize
       except (ImportError, FileNotFoundError):
           warnings.warn("Recommended: pip install sacremoses."						)
           lowercase__				:     Dict					      =       lambda lowercase_						: x






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  Tuple						)      ->		str:
       return self.punc_normalizer(a_						) if x else ""






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  Optional[Any]						)      ->		List[str]:
       return self.current_encoder.get(a_			,		self.current_encoder[self.unk_token]						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  List[str]						)      ->		Tuple:
       lowercase__				:     List[str]					      =       self.language_code_re.match(a_						)
       lowercase__				:     Optional[int]					      =       [match.group(0						)] if match else []
       return code, self.language_code_re.sub(""			,		a_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[int]						)      ->		List[str]:
       lowercase__      ,     lowercase__				:     Dict					      =       self.remove_language_code(a_						)
       lowercase__				:     Any					      =       self.current_spm.encode(a_			,		out_type=a_						)
       return code + pieces






   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  Any						)      ->		str:
       return self.decoder.get(a_			,		self.unk_token						)






   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  Optional[int]			,		**lowercase_       :  str						)      ->		Optional[int]:
       return super().batch_decode(a_			,		**a_						)






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  int			,		**lowercase_       :  List[str]						)      ->		Optional[Any]:
       return super().decode(a_			,		**a_						)






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  int						)      ->		str:
       lowercase__				:     Optional[Any]					      =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
       lowercase__				:     Optional[Any]					      =       []
       lowercase__				:     List[Any]					      =       ""
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               out_string += sp_model.decode_pieces(a_						) + token + " "
               lowercase__				:     Optional[Any]					      =       []
           else:
               current_sub_tokens.append(a_						)
       out_string += sp_model.decode_pieces(a_						)
       return out_string.strip()






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  Any			,		lowercase_       :  Optional[Any]=None						)      ->		List[int]:
       if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]






   def    __UpperCamelCase  (   self       :  Any						)      ->		Union[str, Any]:
       lowercase__				:     int					      =       self.spm_source
       lowercase__				:     Dict					      =       self.encoder






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Tuple:
       lowercase__				:     Any					      =       self.spm_target
       if self.separate_vocabs:
           lowercase__				:     List[str]					      =       self.target_encoder






   @property
   def    __UpperCamelCase  (   self       :  Any						)      ->		int:
       return len(self.encoder						)






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  List[Any] = None						)      ->		Tuple[str]:
       if not os.path.isdir(a_						):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
           return
       lowercase__				:     Union[str, Any]					      =       []

       if self.separate_vocabs:
           lowercase__				:     Optional[int]					      =       os.path.join(
               a_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]			,		)
           lowercase__				:     Optional[int]					      =       os.path.join(
               a_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]			,		)
           save_json(self.encoder			,		a_						)
           save_json(self.target_encoder			,		a_						)
           saved_files.append(a_						)
           saved_files.append(a_						)
       else:
           lowercase__				:     Optional[int]					      =       os.path.join(
               a_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]						)
           save_json(self.encoder			,		a_						)
           saved_files.append(a_						)

       for spm_save_filename, spm_orig_path, spm_model in zip(
           [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]			,		self.spm_files			,		[self.spm_source, self.spm_target]			,		):
           lowercase__				:     List[Any]					      =       os.path.join(
               a_			,		(filename_prefix + "-" if filename_prefix else "") + spm_save_filename						)
           if os.path.abspath(a_						) != os.path.abspath(a_						) and os.path.isfile(a_						):
               copyfile(a_			,		a_						)
               saved_files.append(a_						)
           elif not os.path.isfile(a_						):
               with open(a_			,		"wb"						) as fi:
                   lowercase__				:     Dict					      =       spm_model.serialized_model_proto()
                   fi.write(a_						)
               saved_files.append(a_						)

       return tuple(a_						)






   def    __UpperCamelCase  (   self       :  Any						)      ->		Dict:
       return self.get_src_vocab()






   def    __UpperCamelCase  (   self       :  int						)      ->		int:
       return dict(self.encoder			,		**self.added_tokens_encoder						)






   def    __UpperCamelCase  (   self       :  str						)      ->		List[Any]:
       return dict(self.target_encoder			,		**self.added_tokens_decoder						)






   def __getstate__(   self       :  List[str]						)      ->		Dict:
       lowercase__				:     Tuple					      =       self.__dict__.copy()
       state.update(
           {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}						)
       return state






   def __setstate__(   self       :  str			,		lowercase_       :  List[str]						)      ->		None:
       lowercase__				:     Any					      =       d

       # for backward compatibility
       if not hasattr(self			,		"sp_model_kwargs"						):
           lowercase__				:     List[str]					      =       {}

       lowercase__      ,     lowercase__				:     Optional[Any]					      =       (load_spm(a_			,		self.sp_model_kwargs						) for f in self.spm_files)
       lowercase__				:     str					      =       self.spm_source
       self._setup_normalizer()






   def    __UpperCamelCase  (   self       :  Optional[int]			,		*lowercase_       :  Any			,		**lowercase_       :  Optional[Any]						)      ->		str:
       return 1






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]						)      ->		Any:
       lowercase__				:     Union[str, Any]					      =       set(self.all_special_ids						)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
       return [1 if x in all_special_ids else 0 for x in seq]







   def    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  List[Any]			,		lowercase_       :  Optional[Any] = None			,		lowercase_       :  int = False						)      ->		List[int]:
       if already_has_special_tokens:
           return self._special_token_mask(a_						)
       elif token_ids_a is None:
           return self._special_token_mask(a_						) + [1]
       else:
           return self._special_token_mask(token_ids_a + token_ids_a						) + [1]



def     lowercase_       (    _lowerCamelCase							: int	,			_lowerCamelCase							: List[Any]):
    lowercase__				:     Tuple					      =       sentencepiece.SentencePieceProcessor(**UpperCamelCase__)
    spm.Load(UpperCamelCase__)
    return spm

def     lowercase_       (    _lowerCamelCase							: List[Any]	,			_lowerCamelCase							: Any):






    with open(UpperCamelCase__	,			"w") as f:
        json.dump(UpperCamelCase__	,			UpperCamelCase__	,			indent=2)

def     lowercase_       (    _lowerCamelCase							: Tuple):






    with open(UpperCamelCase__	,			"r") as f:
        return json.load(UpperCamelCase__)




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



