





'''simple docstring'''






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}






class     lowercase_			(snake_case__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    '''conditional_detr'''
  SCREAMING_SNAKE_CASE					:						int           =    ['''past_key_values''']
  SCREAMING_SNAKE_CASE					:						Tuple           =    {
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }






  def __init__(   self     :  Optional[Any]      ,lowercase__     :  str=True      ,lowercase__     :  Optional[int]=None      ,lowercase__     :  int=3      ,lowercase__     :  Any=3_0_0      ,lowercase__     :  List[str]=6      ,lowercase__     :  str=2_0_4_8      ,lowercase__     :  Optional[int]=8      ,lowercase__     :  str=6      ,lowercase__     :  str=2_0_4_8      ,lowercase__     :  str=8      ,lowercase__     :  str=0.0      ,lowercase__     :  str=0.0      ,lowercase__     :  int=True      ,lowercase__     :  str="relu"      ,lowercase__     :  Dict=2_5_6      ,lowercase__     :  str=0.1      ,lowercase__     :  int=0.0      ,lowercase__     :  Tuple=0.0      ,lowercase__     :  Any=0.0_2      ,lowercase__     :  Tuple=1.0      ,lowercase__     :  Tuple=False      ,lowercase__     :  Union[str, Any]="sine"      ,lowercase__     :  Union[str, Any]="resnet50"      ,lowercase__     :  int=True      ,lowercase__     :  str=False      ,lowercase__     :  List[str]=2      ,lowercase__     :  List[Any]=5      ,lowercase__     :  Dict=2      ,lowercase__     :  Tuple=1      ,lowercase__     :  List[str]=1      ,lowercase__     :  Optional[Any]=2      ,lowercase__     :  Optional[int]=5      ,lowercase__     :  Tuple=2      ,lowercase__     :  Optional[int]=0.2_5      ,**lowercase__     :  int      ,):
   if backbone_config is not None and use_timm_backbone:
    raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''    )

   if not use_timm_backbone:
    if backbone_config is None:
     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
     __lowercase													=				CONFIG_MAPPING['resnet'](out_features=['''stage4''']    )
    elif isinstance(_A      ,_A    ):
     __lowercase													=				backbone_config.get('''model_type'''    )
     __lowercase													=				CONFIG_MAPPING[backbone_model_type]
     __lowercase													=				config_class.from_dict(_A    )

   __lowercase													=				use_timm_backbone
   __lowercase													=				backbone_config
   __lowercase													=				num_channels
   __lowercase													=				num_queries
   __lowercase													=				d_model
   __lowercase													=				encoder_ffn_dim
   __lowercase													=				encoder_layers
   __lowercase													=				encoder_attention_heads
   __lowercase													=				decoder_ffn_dim
   __lowercase													=				decoder_layers
   __lowercase													=				decoder_attention_heads
   __lowercase													=				dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				activation_function
   __lowercase													=				init_std
   __lowercase													=				init_xavier_std
   __lowercase													=				encoder_layerdrop
   __lowercase													=				decoder_layerdrop
   __lowercase													=				encoder_layers
   __lowercase													=				auxiliary_loss
   __lowercase													=				position_embedding_type
   __lowercase													=				backbone
   __lowercase													=				use_pretrained_backbone
   __lowercase													=				dilation
   # Hungarian matcher
   __lowercase													=				class_cost
   __lowercase													=				bbox_cost
   __lowercase													=				giou_cost
   # Loss coefficients
   __lowercase													=				mask_loss_coefficient
   __lowercase													=				dice_loss_coefficient
   __lowercase													=				cls_loss_coefficient
   __lowercase													=				bbox_loss_coefficient
   __lowercase													=				giou_loss_coefficient
   __lowercase													=				focal_alpha
   super().__init__(is_encoder_decoder=_A      ,**_A    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return self.encoder_attention_heads






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   return self.d_model






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				copy.deepcopy(self.__dict__    )
   if self.backbone_config is not None:
    __lowercase													=				self.backbone_config.to_dict()
   __lowercase													=				self.__class__.model_type
   return output






class     lowercase_			(snake_case__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[str]           =    version.parse('1.11'   )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   return OrderedDict(
       [
           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ('''pixel_mask''', {0: '''batch'''}),
       ]    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return 1e-5






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return 1_2




'''simple docstring'''






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

import sentencepiece as spm

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


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{'''vocab_file''': '''sentencepiece.model'''}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

lowerCAmelCase__      		=			{
    '''google/rembert''': 256,
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(   self     :  str      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]=False      ,lowercase__     :  Dict=True      ,lowercase__     :  List[str]=True      ,lowercase__     :  Dict="[CLS]"      ,lowercase__     :  Union[str, Any]="[SEP]"      ,lowercase__     :  List[str]="[UNK]"      ,lowercase__     :  int="[SEP]"      ,lowercase__     :  List[str]="[PAD]"      ,lowercase__     :  Optional[int]="[CLS]"      ,lowercase__     :  List[Any]="[MASK]"      ,**lowercase__     :  int      ,):
   super().__init__(
       do_lower_case=lowercase__      ,remove_space=lowercase__      ,keep_accents=lowercase__      ,bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,sep_token=lowercase__      ,pad_token=lowercase__      ,cls_token=lowercase__      ,mask_token=lowercase__      ,**lowercase__      ,)

   __lowercase													=				do_lower_case
   __lowercase													=				remove_space
   __lowercase													=				keep_accents
   __lowercase													=				vocab_file

   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(lowercase__    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return len(self.sp_model    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				{self.convert_ids_to_tokens(lowercase__    ): i for i in range(self.vocab_size    )}
   vocab.update(self.added_tokens_encoder    )
   return vocab






  def __getstate__(   self     :  List[Any]    ):
   __lowercase													=				self.__dict__.copy()
   __lowercase													=				None
   return state






  def __setstate__(   self     :  str      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				d
   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(self.vocab_file    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]=False    ):
   __lowercase													=				self.sp_model.EncodeAsPieces(lowercase__    )
   return pieces






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]    ):
   return self.sp_model.PieceToId(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  str    ):
   return self.sp_model.IdToPiece(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Tuple    ):
   __lowercase													=				self.sp_model.decode_pieces(lowercase__    )
   return out_string






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None      ,lowercase__     :  bool = False    ):

   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(lowercase__    )) + [1] + ([0] * len(lowercase__    )) + [1]
   return [1] + ([0] * len(lowercase__    )) + [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   if not os.path.isdir(lowercase__    ):
    logger.error('''Vocabulary path ({}) should be a directory'''.format(lowercase__    )    )
    return
   __lowercase													=				os.path.join(
       lowercase__      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

   return (out_vocab_file,)

