from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__		:					Any						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					str						=					{
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'xlm'
  lowercase_			=     {
      'hidden_size': 'emb_dim',
      'num_attention_heads': 'n_heads',
      'num_hidden_layers': 'n_layers',
      'n_words': 'vocab_size',  # For backward compatibility
  }




  def __init__(      self     :	List[Any]     , a_     :	Tuple=3_0145     , a_     :	Union[str, Any]=2048     , a_     :	int=12     , a_     :	List[Any]=16     , a_     :	str=0.1     , a_     :	Union[str, Any]=0.1     , a_     :	Optional[int]=True     , a_     :	Dict=False     , a_     :	Optional[Any]=False     , a_     :	Optional[Any]=False     , a_     :	List[Any]=1     , a_     :	int=True     , a_     :	Dict=512     , a_     :	List[str]=2048**-0.5     , a_     :	Tuple=1e-1_2     , a_     :	Optional[int]=0.02     , a_     :	Any=0     , a_     :	Optional[int]=1     , a_     :	Union[str, Any]=2     , a_     :	List[Any]=3     , a_     :	List[str]=5     , a_     :	Optional[int]=True     , a_     :	List[str]="first"     , a_     :	int=True     , a_     :	Union[str, Any]=None     , a_     :	Optional[int]=True     , a_     :	Tuple=0.1     , a_     :	Any=5     , a_     :	List[str]=5     , a_     :	List[str]=0     , a_     :	Any=0     , a_     :	Dict=2     , a_     :	List[Any]=0     , **a_     :	Optional[int]     , )->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		emb_dim
       SCREAMING_SNAKE_CASE__				:				Dict			=		n_layers
       SCREAMING_SNAKE_CASE__				:				Any			=		n_heads
       SCREAMING_SNAKE_CASE__				:				int			=		dropout
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		attention_dropout
       SCREAMING_SNAKE_CASE__				:				Any			=		gelu_activation
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		sinusoidal_embeddings
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		causal
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		asm
       SCREAMING_SNAKE_CASE__				:				int			=		n_langs
       SCREAMING_SNAKE_CASE__				:				Any			=		use_lang_emb
       SCREAMING_SNAKE_CASE__				:				int			=		layer_norm_eps
       SCREAMING_SNAKE_CASE__				:				str			=		bos_index
       SCREAMING_SNAKE_CASE__				:				Tuple			=		eos_index
       SCREAMING_SNAKE_CASE__				:				str			=		pad_index
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		unk_index
       SCREAMING_SNAKE_CASE__				:				str			=		mask_index
       SCREAMING_SNAKE_CASE__				:				Dict			=		is_encoder
       SCREAMING_SNAKE_CASE__				:				str			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				Dict			=		embed_init_std
       SCREAMING_SNAKE_CASE__				:				List[str]			=		init_std
       SCREAMING_SNAKE_CASE__				:				Dict			=		summary_type
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		summary_use_proj
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		summary_activation
       SCREAMING_SNAKE_CASE__				:				str			=		summary_proj_to_labels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		summary_first_dropout
       SCREAMING_SNAKE_CASE__				:				Tuple			=		start_n_top
       SCREAMING_SNAKE_CASE__				:				Tuple			=		end_n_top
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		mask_token_id
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		lang_id

       if "n_words" in kwargs:
            SCREAMING_SNAKE_CASE__				:				str			=		kwargs['n_words']

       super().__init__(pad_token_id=a_     , bos_token_id=a_     , **a_				)




class       snake_case					(      UpperCamelCase_						):




  @property
  def 	__lowercase(      self     :	List[Any]				)->       Mapping[str, Mapping[int, str]]:




       """simple docstring"""
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__				:				int			=		{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]				)




def     _a				(      lowercase__  :			int = 1_00_00_00 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[i - 1 for i in range(limit + 1 )]

     for i in range(2     ,  limit + 1 ):
          if phi[i] == i - 1:
               for j in range(2 * i     ,  limit + 1     ,  lowercase__ ):
                    phi[j] -= phi[j] // i

     return sum(phi[2 : limit + 1] )


if __name__ == "__main__":
      print(solution())



