

from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase     			=     logging.get_logger(__name__)

_UpperCamelCase     			=     {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowercase		(			snake_case__   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							"""roberta-prelayernorm"""




 def __init__(self			,			__a=50265			,			__a=768			,			__a=12			,			__a=12			,			__a=3072			,			__a="gelu"			,			__a=0.1			,			__a=0.1			,			__a=512			,			__a=2			,			__a=0.02			,			__a=1E-1_2			,			__a=1			,			__a=0			,			__a=2			,			__a="absolute"			,			__a=True			,			__a=None			,			**__a			,			)					->   Tuple:






  """simple docstring"""





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

  UpperCAmelCase__     							=							vocab_size
  UpperCAmelCase__     							=							hidden_size
  UpperCAmelCase__     							=							num_hidden_layers
  UpperCAmelCase__     							=							num_attention_heads
  UpperCAmelCase__     							=							hidden_act
  UpperCAmelCase__     							=							intermediate_size
  UpperCAmelCase__     							=							hidden_dropout_prob
  UpperCAmelCase__     							=							attention_probs_dropout_prob
  UpperCAmelCase__     							=							max_position_embeddings
  UpperCAmelCase__     							=							type_vocab_size
  UpperCAmelCase__     							=							initializer_range
  UpperCAmelCase__     							=							layer_norm_eps
  UpperCAmelCase__     							=							position_embedding_type
  UpperCAmelCase__     							=							use_cache
  UpperCAmelCase__     							=							classifier_dropout







class        lowercase		(			snake_case__   ):



 '''simple docstring'''


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






  """simple docstring"""





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






from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )

