


'''simple docstring'''




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


__snake_case    						=logging.get_logger(__name__)

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

__snake_case    						={
    """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"""
    },
}

__snake_case    						={"""mobilebert-uncased""": 512}


__snake_case    						={}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[int]		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	int		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Tuple		=     PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase				:	Dict		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Union[str, Any]		=     MobileBertTokenizer





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      Optional[int]=None    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      Optional[int]="[UNK]"    ,      UpperCAmelCase__       :      Optional[Any]="[SEP]"    ,      UpperCAmelCase__       :      List[Any]="[PAD]"    ,      UpperCAmelCase__       :      int="[CLS]"    ,      UpperCAmelCase__       :      str="[MASK]"    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      Any=None    ,      **UpperCAmelCase__       :      List[str]    ,      )				->  str:
        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__    ,      )

        lowerCAmelCase     =			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
        ):
           lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      normalizer_state.pop('type'    )    )
           lowerCAmelCase     =			do_lower_case
           lowerCAmelCase     =			strip_accents
           lowerCAmelCase     =			tokenize_chinese_chars
           lowerCAmelCase     =			normalizer_class(**UpperCAmelCase__    )

        lowerCAmelCase     =			do_lower_case





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]=None    )				->  Optional[int]:
        lowerCAmelCase     =			[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       :      List[Any]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        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 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[str] = None    )				->  Tuple[str]:
        lowerCAmelCase     =			self._tokenizer.model.save(UpperCAmelCase__    ,      name=UpperCAmelCase__    )
        return tuple(UpperCAmelCase__    )










'''simple docstring'''





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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



