from collections import OrderedDict
from typing import Mapping

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


__snake_case         =     logging.get_logger(__name__)

__snake_case         =     {
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Optional[Any] 	=   """rembert"""
 def __init__(	self		, snake_case__=25_0300		, snake_case__=1152		, snake_case__=32		, snake_case__=18		, snake_case__=256		, snake_case__=1664		, snake_case__=4608		, snake_case__="gelu"		, snake_case__=0.0		, snake_case__=0.0		, snake_case__=0.1		, snake_case__=512		, snake_case__=2		, snake_case__=0.02		, snake_case__=1e-12		, snake_case__=True		, snake_case__=0		, snake_case__=312		, snake_case__=313		, **snake_case__		, )						->				Dict:
       '''simple docstring'''





       super().__init__(pad_token_id=snake_case__		, bos_token_id=snake_case__		, eos_token_id=snake_case__		, **snake_case__    )

       UpperCAmelCase     :			Union[str, Any]     	=vocab_size
       UpperCAmelCase     :			Optional[Any]     	=input_embedding_size
       UpperCAmelCase     :			Optional[Any]     	=output_embedding_size
       UpperCAmelCase     :			List[str]     	=max_position_embeddings
       UpperCAmelCase     :			Optional[Any]     	=hidden_size
       UpperCAmelCase     :			str     	=num_hidden_layers
       UpperCAmelCase     :			int     	=num_attention_heads
       UpperCAmelCase     :			Tuple     	=intermediate_size
       UpperCAmelCase     :			List[str]     	=hidden_act
       UpperCAmelCase     :			Tuple     	=hidden_dropout_prob
       UpperCAmelCase     :			int     	=attention_probs_dropout_prob
       UpperCAmelCase     :			int     	=classifier_dropout_prob
       UpperCAmelCase     :			int     	=initializer_range
       UpperCAmelCase     :			Union[str, Any]     	=type_vocab_size
       UpperCAmelCase     :			int     	=layer_norm_eps
       UpperCAmelCase     :			Optional[Any]     	=use_cache
       UpperCAmelCase     :			List[str]     	=False







class       __snake_case (  lowerCamelCase__ ):
 @property
 def 						UpperCAmelCase__							(	self    )						->				Mapping[str, Mapping[int, str]]:
       '''simple docstring'''





       if self.task == "multiple-choice":
             UpperCAmelCase     :			Tuple     	={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             UpperCAmelCase     :			Optional[Any]     	={0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''token_type_ids''', dynamic_axis),
           ]    )






 @property
 def 						UpperCAmelCase__							(	self    )						->				float:
       '''simple docstring'''





       return 1e-4

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__snake_case         =     {
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileViTV2ForImageClassification''',
         '''MobileViTV2ForSemanticSegmentation''',
         '''MobileViTV2Model''',
         '''MobileViTV2PreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_mobilevitva import (
         MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileViTVaConfig,
         MobileViTVaOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevitva import (
              MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTVaForImageClassification,
              MobileViTVaForSemanticSegmentation,
              MobileViTVaModel,
              MobileViTVaPreTrainedModel,
          )

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
