




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_						=			{
    '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 (  _a				):
  _UpperCamelCase       :      Optional[int]			=      '''xmod'''






  def __init__(  self		:     Tuple				,						_A		:     int=30_522				,						_A		:     Optional[int]=768				,						_A		:     Optional[Any]=12				,						_A		:     Any=12				,						_A		:     Union[str, Any]=3_072				,						_A		:     List[Any]="gelu"				,						_A		:     List[Any]=0.1				,						_A		:     Optional[Any]=0.1				,						_A		:     Dict=512				,						_A		:     List[Any]=2				,						_A		:     Optional[int]=0.02				,						_A		:     Any=1E-12				,						_A		:     int=1				,						_A		:     Union[str, Any]=0				,						_A		:     Any=2				,						_A		:     List[str]="absolute"				,						_A		:     str=True				,						_A		:     int=None				,						_A		:     Optional[Any]=False				,						_A		:     Union[str, Any]=2				,						_A		:     Union[str, Any]=False				,						_A		:     Tuple=True				,						_A		:     Optional[Any]=True				,						_A		:     str=("en_XX",)				,						_A		:     List[str]=None				,						**_A		:     Dict				,						)		->   int:






       """simple docstring"""







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

       lowercase :       Union[str, Any]       =			vocab_size
       lowercase :       Any       =			hidden_size
       lowercase :       Union[str, Any]       =			num_hidden_layers
       lowercase :       Any       =			num_attention_heads
       lowercase :       Tuple       =			hidden_act
       lowercase :       str       =			intermediate_size
       lowercase :       Tuple       =			hidden_dropout_prob
       lowercase :       Optional[Any]       =			attention_probs_dropout_prob
       lowercase :       Dict       =			max_position_embeddings
       lowercase :       Dict       =			type_vocab_size
       lowercase :       Optional[int]       =			initializer_range
       lowercase :       Dict       =			layer_norm_eps
       lowercase :       Optional[int]       =			position_embedding_type
       lowercase :       int       =			use_cache
       lowercase :       Optional[Any]       =			classifier_dropout
       lowercase :       List[Any]       =			pre_norm
       lowercase :       List[str]       =			adapter_reduction_factor
       lowercase :       int       =			adapter_layer_norm
       lowercase :       List[Any]       =			adapter_reuse_layer_norm
       lowercase :       Tuple       =			ln_before_adapter
       lowercase :       Union[str, Any]       =			list(_a				)
       lowercase :       Any       =			default_language


class 						_A (  _a				):






  @property
  def  __a      (  self		:     str				)		->   int:






       """simple docstring"""







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





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


if __name__ == "__main__":
 fire.Fire(convert)