




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


lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {
    "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Optional[int]     =  "mobilevitv2"




     def __init__(     self      :			Dict    ,_UpperCamelCase      :			Tuple=3    ,_UpperCamelCase      :			Optional[int]=2_5_6    ,_UpperCamelCase      :			Optional[int]=2    ,_UpperCamelCase      :			Optional[Any]=2.0    ,_UpperCamelCase      :			Tuple="swish"    ,_UpperCamelCase      :			Optional[Any]=3    ,_UpperCamelCase      :			Tuple=3_2    ,_UpperCamelCase      :			Dict=0.1    ,_UpperCamelCase      :			Tuple=0.02    ,_UpperCamelCase      :			Optional[Any]=1e-5    ,_UpperCamelCase      :			Union[str, Any]=5_1_2    ,_UpperCamelCase      :			Dict=[6, 1_2, 1_8]    ,_UpperCamelCase      :			List[Any]=0.1    ,_UpperCamelCase      :			Union[str, Any]=2_5_5    ,_UpperCamelCase      :			Optional[int]=[2, 4, 3]    ,_UpperCamelCase      :			int=[1_2_8, 1_9_2, 2_5_6]    ,_UpperCamelCase      :			int=1.0    ,_UpperCamelCase      :			Optional[int]=2    ,_UpperCamelCase      :			str=0.0    ,_UpperCamelCase      :			List[str]=0.0    ,**_UpperCamelCase      :			List[str]    ,)		->       Any:


         '''simple docstring'''





         super().__init__(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =num_channels
         SCREAMING_SNAKE_CASE__       =image_size
         SCREAMING_SNAKE_CASE__       =patch_size
         SCREAMING_SNAKE_CASE__       =expand_ratio
         SCREAMING_SNAKE_CASE__       =hidden_act
         SCREAMING_SNAKE_CASE__       =conv_kernel_size
         SCREAMING_SNAKE_CASE__       =output_stride
         SCREAMING_SNAKE_CASE__       =initializer_range
         SCREAMING_SNAKE_CASE__       =layer_norm_eps
         SCREAMING_SNAKE_CASE__       =n_attn_blocks
         SCREAMING_SNAKE_CASE__       =base_attn_unit_dims
         SCREAMING_SNAKE_CASE__       =width_multiplier
         SCREAMING_SNAKE_CASE__       =ffn_multiplier
         SCREAMING_SNAKE_CASE__       =ffn_dropout
         SCREAMING_SNAKE_CASE__       =attn_dropout
         SCREAMING_SNAKE_CASE__       =classifier_dropout_prob

         # decode head attributes for semantic segmentation
         SCREAMING_SNAKE_CASE__       =aspp_out_channels
         SCREAMING_SNAKE_CASE__       =atrous_rates
         SCREAMING_SNAKE_CASE__       =aspp_dropout_prob
         SCREAMING_SNAKE_CASE__       =semantic_loss_ignore_index






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Dict     =  version.parse("1.11"					)




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


         '''simple docstring'''





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




     @property
     def   __A    (     self      :			List[Any]						)		->       Mapping[str, Mapping[int, str]]:


         '''simple docstring'''





         if self.task == "image-classification":
             return OrderedDict([("""logits""", {0: """batch"""})]						)
         else:
             return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]						)




     @property
     def   __A    (     self      :			str						)		->       float:


         '''simple docstring'''





         return 1e-4












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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
    from .tokenization_deberta_va import DebertaVaTokenizer
else:
    lowerCamelCase_  						=       None

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_  						=       {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   str     =  VOCAB_FILES_NAMES
     _A    :   Union[str, Any]     =  PRETRAINED_VOCAB_FILES_MAP
     _A    :   Optional[int]     =  PRETRAINED_INIT_CONFIGURATION
     _A    :   Any     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A    :   Optional[int]     =  DebertaVaTokenizer




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


         '''simple docstring'''





         super().__init__(
             _UpperCamelCase    ,tokenizer_file=_UpperCamelCase    ,do_lower_case=_UpperCamelCase    ,bos_token=_UpperCamelCase    ,eos_token=_UpperCamelCase    ,unk_token=_UpperCamelCase    ,sep_token=_UpperCamelCase    ,pad_token=_UpperCamelCase    ,cls_token=_UpperCamelCase    ,mask_token=_UpperCamelCase    ,split_by_punct=_UpperCamelCase    ,**_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =do_lower_case
         SCREAMING_SNAKE_CASE__       =split_by_punct
         SCREAMING_SNAKE_CASE__       =vocab_file
         SCREAMING_SNAKE_CASE__       =False if not self.vocab_file else True




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			str=None						)		->       Dict:


         '''simple docstring'''





         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[self.cls_token_id]
         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			Union[str, Any]=False						)		->       str:


         '''simple docstring'''





         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCamelCase    ,token_ids_a=_UpperCamelCase    ,already_has_special_tokens=_UpperCamelCase						)

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




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			str=None						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[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   __A    (     self      :			int    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = None						)		->       Tuple[str]:


         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""						)

         if not os.path.isdir(_UpperCamelCase						):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
             return
         SCREAMING_SNAKE_CASE__       =os.path.join(
             _UpperCamelCase    ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

         return (out_vocab_file,)






