import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__											=					{
    'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
    'tokenizer_file': {
        'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
    },
}

UpperCamelCase__											=					{'mobilebert-uncased': 5_1_2}


UpperCamelCase__											=					{}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Any      =			PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Dict      =			MobileBertTokenizer


   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						str=True       ,  __UpperCAmelCase     :						Optional[int]="[UNK]"       ,  __UpperCAmelCase     :						List[str]="[SEP]"       ,  __UpperCAmelCase     :						Tuple="[PAD]"       ,  __UpperCAmelCase     :						int="[CLS]"       ,  __UpperCAmelCase     :						Tuple="[MASK]"       ,  __UpperCAmelCase     :						List[Any]=True       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Tuple       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(
             __UpperCAmelCase       ,  tokenizer_file=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  unk_token=__UpperCAmelCase       ,  sep_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  cls_token=__UpperCAmelCase       ,  mask_token=__UpperCAmelCase       ,  tokenize_chinese_chars=__UpperCAmelCase       ,  strip_accents=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
         if (
             normalizer_state.get("lowercase"       ,  __UpperCAmelCase		) != do_lower_case
             or normalizer_state.get("strip_accents"       ,  __UpperCAmelCase		) != strip_accents
             or normalizer_state.get("handle_chinese_chars"       ,  __UpperCAmelCase		) != tokenize_chinese_chars
         ):
               UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  normalizer_state.pop("type"		)		)
               UpperCAmelCase__								=  do_lower_case
               UpperCAmelCase__								=  strip_accents
               UpperCAmelCase__								=  tokenize_chinese_chars
               UpperCAmelCase__								=  normalizer_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  do_lower_case


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[Any]=None		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  [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 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [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 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self._tokenizer.model.save(__UpperCAmelCase       ,  name=__UpperCAmelCase		)
         return tuple(__UpperCAmelCase		)




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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  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       ,  )



