

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__		 =     {
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}

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







       __SCREAMING_SNAKE_CASE				    =							"xlm-roberta-xl"


       def __init__(		self      , _UpperCamelCase=25_08_80      , _UpperCamelCase=25_60      , _UpperCamelCase=36      , _UpperCamelCase=32      , _UpperCamelCase=1_02_40      , _UpperCamelCase="gelu"      , _UpperCamelCase=0.1      , _UpperCamelCase=0.1      , _UpperCamelCase=5_14      , _UpperCamelCase=1      , _UpperCamelCase=0.02      , _UpperCamelCase=1E-05      , _UpperCamelCase=1      , _UpperCamelCase=0      , _UpperCamelCase=2      , _UpperCamelCase="absolute"      , _UpperCamelCase=True      , _UpperCamelCase=None      , **_UpperCamelCase      , )      ->							Tuple:






              """simple docstring"""


              super().__init__(pad_token_id=_UpperCamelCase      , bos_token_id=_UpperCamelCase      , eos_token_id=_UpperCamelCase      , **_UpperCamelCase   )
              __snake_case       	=							vocab_size
              __snake_case       	=							hidden_size
              __snake_case       	=							num_hidden_layers
              __snake_case       	=							num_attention_heads
              __snake_case       	=							hidden_act
              __snake_case       	=							intermediate_size
              __snake_case       	=							hidden_dropout_prob
              __snake_case       	=							attention_probs_dropout_prob
              __snake_case       	=							max_position_embeddings
              __snake_case       	=							type_vocab_size
              __snake_case       	=							initializer_range
              __snake_case       	=							layer_norm_eps
              __snake_case       	=							position_embedding_type
              __snake_case       	=							use_cache
              __snake_case       	=							classifier_dropout






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







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






              """simple docstring"""


              if self.task == "multiple-choice":
                     __snake_case       	=							{0: """batch""", 1: """choice""", 2: """sequence"""}
              else:
                     __snake_case       	=							{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   )

