


'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_rembert import RemBertTokenizer
else:
       a_						:       Dict      			=				None

a_						:       Optional[int]      			=				logging.get_logger(__name__)
a_						:       List[str]      			=				{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

a_						:       Any      			=				{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

a_						:       str      			=				{
    """google/rembert""": 256,
}

a_						:       int      			=				"""▁"""





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						RemBertTokenizer






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=False    ,						UpperCamelCase="[CLS]"    ,						UpperCamelCase="[SEP]"    ,						UpperCamelCase="<unk>"    ,						UpperCamelCase="[SEP]"    ,						UpperCamelCase="<pad>"    ,						UpperCamelCase="[CLS]"    ,						UpperCamelCase="[MASK]"    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						AddedToken(__a    ,						lstrip=__a    ,						rstrip=__a  ) if isinstance(__a    ,						__a  ) else mask_token

   super().__init__(
       __a    ,						tokenizer_file=__a    ,						do_lower_case=__a    ,						remove_space=__a    ,						keep_accents=__a    ,						bos_token=__a    ,						eos_token=__a    ,						unk_token=__a    ,						sep_token=__a    ,						pad_token=__a    ,						cls_token=__a    ,						mask_token=__a    ,						**__a    ,						)

   lowerCamelCase_  				=						do_lower_case
   lowerCamelCase_  				=						remove_space
   lowerCamelCase_  				=						keep_accents
   lowerCamelCase_  				=						vocab_file
   lowerCamelCase_  				=						False if not self.vocab_file else True






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						[self.sep_token_id]
   lowerCamelCase_  				=						[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = False  ):







   """simple docstring"""

   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         "You should not supply a second sequence if the provided sequence of "
         "ids is already formatted with special tokens for the model."  )
    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

   if token_ids_a is not None:
    return [1] + ([0] * len(__a  )) + [1] + ([0] * len(__a  )) + [1]
   return [1] + ([0] * len(__a  )) + [1]






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						[self.sep_token_id]
   lowerCamelCase_  				=						[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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   if not os.path.isdir(__a  ):
    logger.error("Vocabulary path ({}) should be a directory".format(__a  )  )
    return
   lowerCamelCase_  				=						os.path.join(
       __a    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

   if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ):
    copyfile(self.vocab_file    ,						__a  )

   return (out_vocab_file,)







'''simple docstring'''

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					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=2    ,						UpperCamelCase=512    ,						UpperCamelCase="cls"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						project_dim
   lowerCamelCase_  				=						pooler_fn
   lowerCamelCase_  				=						learn_encoder
   lowerCamelCase_  				=						use_attention_mask





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						[r"pooler", r"logit_scale"]
  _lowerCamelCase						=						[r"position_ids", r"predictions.decoder.bias"]
  _lowerCamelCase						=						"roberta"
  _lowerCamelCase						=						RobertaSeriesConfig






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )
   lowerCamelCase_  				=						XLMRobertaModel(UpperCamelCase  )
   lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
   lowerCamelCase_  				=						getattr(UpperCamelCase    ,						"has_pre_transformation"    ,						UpperCamelCase  )
   if self.has_pre_transformation:
    lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
    lowerCamelCase_  				=						nn.LayerNorm(config.hidden_size    ,						eps=config.layer_norm_eps  )
   self.post_init()







  def 						snake_case	(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.base_model(
       input_ids=UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						position_ids=UpperCamelCase    ,						head_mask=UpperCamelCase    ,						inputs_embeds=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						output_attentions=UpperCamelCase    ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,						return_dict=UpperCamelCase    ,						)

   if self.has_pre_transformation:
    lowerCamelCase_  				=						outputs["hidden_states"][-2]
    lowerCamelCase_  				=						self.pre_LN(UpperCamelCase  )
    lowerCamelCase_  				=						self.transformation_pre(UpperCamelCase  )

    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)
   else:
    lowerCamelCase_  				=						self.transformation(outputs.last_hidden_state  )
    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)



