




'''simple docstring'''


from argparse import ArgumentParser

from . import BaseTransformersCLICommand
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


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

class      A       (						_a       ):







							@staticmethod
							def   __lowerCAmelCase     (    lowerCAmelCase_			:    ArgumentParser   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							parser.add_parser('''download'''   )
														download_parser.add_argument(
														    '''--cache-dir'''				,     type=lowerCAmelCase_				,     default=lowerCAmelCase_				,     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=lowerCAmelCase_				,     help='''Name of the model to download'''   )
														download_parser.set_defaults(func=lowerCAmelCase_   )







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    bool				,     lowerCAmelCase_			:    bool   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							model
														_a      	=							cache
														_a      	=							force
														_a      	=							trust_remote_code







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[str]:


														"""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   )



from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}






class     UpperCAmelCase						(		A_					):
   A__   :						Optional[int]			=	"ibert"



   def __init__(self						:   List[Any]       ,		snake_case__						:   int=3_05_22       ,		snake_case__						:   int=7_68       ,		snake_case__						:   Any=12       ,		snake_case__						:   str=12       ,		snake_case__						:   Optional[int]=30_72       ,		snake_case__						:   Union[str, Any]="gelu"       ,		snake_case__						:   Any=0.1       ,		snake_case__						:   List[Any]=0.1       ,		snake_case__						:   str=5_12       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Any=0.02       ,		snake_case__						:   List[Any]=1e-12       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[Any]=0       ,		snake_case__						:   str=2       ,		snake_case__						:   Tuple="absolute"       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   Tuple="none"       ,		**snake_case__						:   Tuple       ,		)						->      Union[str, Any]:


          '''simple docstring'''


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

          snake_case			:  str     							= vocab_size
          snake_case			:  Tuple     							= hidden_size
          snake_case			:  Optional[Any]     							= num_hidden_layers
          snake_case			:  Optional[int]     							= num_attention_heads
          snake_case			:  List[Any]     							= hidden_act
          snake_case			:  Optional[Any]     							= intermediate_size
          snake_case			:  Dict     							= hidden_dropout_prob
          snake_case			:  List[str]     							= attention_probs_dropout_prob
          snake_case			:  List[str]     							= max_position_embeddings
          snake_case			:  Union[str, Any]     							= type_vocab_size
          snake_case			:  Optional[int]     							= initializer_range
          snake_case			:  Optional[int]     							= layer_norm_eps
          snake_case			:  int     							= position_embedding_type
          snake_case			:  Union[str, Any]     							= quant_mode
          snake_case			:  Dict     							= force_dequant






class     UpperCAmelCase						(		A_					):



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


          '''simple docstring'''


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





