






from collections import OrderedDict
from typing import Mapping

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


__A					=	logging.get_logger(__name__)

__A					=	{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Any				= 'xmod'



    def __init__(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     List[Any]=30522 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=768 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=12 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=12 ,     __SCREAMING_SNAKE_CASE      :     Tuple=3072 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="gelu" ,     __SCREAMING_SNAKE_CASE      :     str=0.1 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Dict=512 ,     __SCREAMING_SNAKE_CASE      :     Any=2 ,     __SCREAMING_SNAKE_CASE      :     Tuple=0.02 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=1e-12 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=1 ,     __SCREAMING_SNAKE_CASE      :     Tuple=0 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=2 ,     __SCREAMING_SNAKE_CASE      :     Any="absolute" ,     __SCREAMING_SNAKE_CASE      :     List[Any]=True ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=False ,     __SCREAMING_SNAKE_CASE      :     int=2 ,     __SCREAMING_SNAKE_CASE      :     Dict=False ,     __SCREAMING_SNAKE_CASE      :     Tuple=True ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=True ,     __SCREAMING_SNAKE_CASE      :     Any=("en_XX",) ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     **__SCREAMING_SNAKE_CASE      :     Dict ,     )      ->							Optional[Any]:
           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,     bos_token_id=__SCREAMING_SNAKE_CASE ,     eos_token_id=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

           __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
           __UpperCAmelCase     				=pre_norm
           __UpperCAmelCase     				=adapter_reduction_factor
           __UpperCAmelCase     				=adapter_layer_norm
           __UpperCAmelCase     				=adapter_reuse_layer_norm
           __UpperCAmelCase     				=ln_before_adapter
           __UpperCAmelCase     				=list(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=default_language




class 				_A			(     UpperCamelCase				):
    """simple docstring"""

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







"""simple docstring"""


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		min(_a				)
      A				  =		max(_a				)
      # normalize data
      return [round((x - x_min) / (x_max - x_min)     ,   _a				) for x in data]





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		mean(_a				)
      A				  =		stdev(_a				)
      # standardize data
      return [round((x - mu) / (sigma)     ,   _a				) for x in data]




