





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__UpperCAmelCase       :   Any				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   int				       =							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase       :   str				       =							{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase       :   Union[str, Any]				       =							{
    "yjernite/retribert-base-uncased": 512,
}


__UpperCAmelCase       :   Any				       =							{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     VOCAB_FILES_NAMES
       lowerCAmelCase__      		=     PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__      		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__      		=     PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__      		=     RetriBertTokenizer
       lowerCAmelCase__      		=     ["""input_ids""", """attention_mask"""]




       def __init__(  self :    Optional[Any]							,				A :    Optional[int]=None							,				A :    str=None							,				A :    Optional[Any]=True							,				A :    Any="[UNK]"							,				A :    Optional[int]="[SEP]"							,				A :    Optional[int]="[PAD]"							,				A :    List[Any]="[CLS]"							,				A :    Optional[Any]="[MASK]"							,				A :    Tuple=True							,				A :    Union[str, Any]=None							,				**A :    str							,				):
         super().__init__(
             A							,				tokenizer_file=A							,				do_lower_case=A							,				unk_token=A							,				sep_token=A							,				pad_token=A							,				cls_token=A							,				mask_token=A							,				tokenize_chinese_chars=A							,				strip_accents=A							,				**A							,				)

         __snake_case:						Dict   =						json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
         if (
             normalizer_state.get("""lowercase"""							,				A		) != do_lower_case
             or normalizer_state.get("""strip_accents"""							,				A		) != strip_accents
             or normalizer_state.get("""handle_chinese_chars"""							,				A		) != tokenize_chinese_chars
         ):
           __snake_case:						Dict   =						getattr(A							,				normalizer_state.pop("""type"""		)		)
           __snake_case:						Dict   =						do_lower_case
           __snake_case:						str   =						strip_accents
           __snake_case:						Any   =						tokenize_chinese_chars
           __snake_case:						List[str]   =						normalizer_class(**A		)

         __snake_case:						Dict   =						do_lower_case




       def 							UpperCAmelCase__ (  self :    int							,				A :    Union[str, Any]							,				A :    Optional[int]=None		):
         __snake_case:						List[Any]   =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

         return output




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    List[int]							,				A :    Optional[List[int]] = None		):
         __snake_case:						Optional[int]   =						[self.sep_token_id]
         __snake_case:						str   =						[self.cls_token_id]
         if token_ids_a is None:
           return len(cls + token_ids_a + sep		) * [0]
         return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






       def 							UpperCAmelCase__ (  self :    str							,				A :    str							,				A :    Optional[str] = None		):
         __snake_case:						List[Any]   =						self._tokenizer.model.save(A							,				name=A		)
         return tuple(A		)













from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						self.base_model(
             input_ids=A							,				attention_mask=A							,				token_type_ids=A							,				position_ids=A							,				head_mask=A							,				inputs_embeds=A							,				encoder_hidden_states=A							,				encoder_attention_mask=A							,				output_attentions=A							,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,				return_dict=A							,				)

         if self.has_pre_transformation:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						self.transformation_pre(A		)

           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)
         else:
           __snake_case:						Optional[int]   =						self.transformation(outputs.last_hidden_state		)
           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)






