



"""simple docstring"""







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		=						{
    '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       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """xlm"""
			__magic_name__     					=     {
			    """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]   ,	_lowercase							:				Union[str, Any]=30145   ,	_lowercase							:				Optional[int]=2048   ,	_lowercase							:				Optional[Any]=12   ,	_lowercase							:				Tuple=16   ,	_lowercase							:				Dict=0.1   ,	_lowercase							:				Tuple=0.1   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				Optional[Any]=False   ,	_lowercase							:				List[Any]=False   ,	_lowercase							:				Optional[int]=False   ,	_lowercase							:				List[Any]=1   ,	_lowercase							:				Any=True   ,	_lowercase							:				Union[str, Any]=512   ,	_lowercase							:				int=2048**-0.5   ,	_lowercase							:				Any=1E-12   ,	_lowercase							:				List[str]=0.02   ,	_lowercase							:				Any=0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				str=2   ,	_lowercase							:				int=3   ,	_lowercase							:				Union[str, Any]=5   ,	_lowercase							:				List[Any]=True   ,	_lowercase							:				List[str]="first"   ,	_lowercase							:				Tuple=True   ,	_lowercase							:				str=None   ,	_lowercase							:				List[Any]=True   ,	_lowercase							:				List[Any]=0.1   ,	_lowercase							:				str=5   ,	_lowercase							:				Optional[int]=5   ,	_lowercase							:				Any=0   ,	_lowercase							:				List[Any]=0   ,	_lowercase							:				List[Any]=2   ,	_lowercase							:				int=0   ,	**_lowercase							:				Dict   ,	)							->					List[str]:
									snake_case   :			Dict						    = vocab_size
									snake_case   :			str						    = emb_dim
									snake_case   :			Optional[Any]						    = n_layers
									snake_case   :			Dict						    = n_heads
									snake_case   :			Dict						    = dropout
									snake_case   :			Union[str, Any]						    = attention_dropout
									snake_case   :			List[Any]						    = gelu_activation
									snake_case   :			Tuple						    = sinusoidal_embeddings
									snake_case   :			Optional[Any]						    = causal
									snake_case   :			List[str]						    = asm
									snake_case   :			List[str]						    = n_langs
									snake_case   :			Optional[Any]						    = use_lang_emb
									snake_case   :			List[str]						    = layer_norm_eps
									snake_case   :			str						    = bos_index
									snake_case   :			Any						    = eos_index
									snake_case   :			Any						    = pad_index
									snake_case   :			Any						    = unk_index
									snake_case   :			Dict						    = mask_index
									snake_case   :			Optional[int]						    = is_encoder
									snake_case   :			Any						    = max_position_embeddings
									snake_case   :			Optional[int]						    = embed_init_std
									snake_case   :			Dict						    = init_std
									snake_case   :			List[Any]						    = summary_type
									snake_case   :			str						    = summary_use_proj
									snake_case   :			Dict						    = summary_activation
									snake_case   :			List[str]						    = summary_proj_to_labels
									snake_case   :			str						    = summary_first_dropout
									snake_case   :			str						    = start_n_top
									snake_case   :			Tuple						    = end_n_top
									snake_case   :			List[Any]						    = mask_token_id
									snake_case   :			Dict						    = lang_id

									if "n_words" in kwargs:
															snake_case   :			Union[str, Any]						    = kwargs["n_words"]

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



class       _a  ( SCREAMING_SNAKE_CASE__):



			@property
			def 				__lowercase					(			self							:				Dict		)							->					Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
															snake_case   :			int						    = {0: "batch", 1: "choice", 2: "sequence"}
									else:
															snake_case   :			Optional[Any]						    = {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]		)








"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



