




def 			__magic_name__				( A		:					str,  A		:					int   ):






			'''simple docstring'''







			a    =			[[] for _ in range(A   )]
			a    =			key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"   )
			if key == 1 or len(A   ) <= key:
						return input_string

			for position, character in enumerate(A   ):
						a    =			position % (lowest * 2)  # puts it in bounds
						a    =			min(A,  lowest * 2 - num   )  # creates zigzag pattern
						temp_grid[num].append(A   )
			a    =			["".join(A   ) for row in temp_grid]
			a    =			"".join(A   )

			return output_string




def 			__magic_name__				( A		:					str,  A		:					int   ):






			'''simple docstring'''







			a    =			[]
			a    =			key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"   )
			if key == 1:
						return input_string

			a    =			[[] for _ in range(A   )]  # generates template
			for position in range(len(A   )   ):
						a    =			position % (lowest * 2)  # puts it in bounds
						a    =			min(A,  lowest * 2 - num   )  # creates zigzag pattern
						temp_grid[num].append("*"   )

			a    =			0
			for row in temp_grid:  # fills in the characters
						a    =			input_string[counter : counter + len(A   )]
						grid.append(list(A   )   )
						counter += len(A   )

			a    =			""  # reads as zigzag
			for position in range(len(A   )   ):
						a    =			position % (lowest * 2)  # puts it in bounds
						a    =			min(A,  lowest * 2 - num   )  # creates zigzag pattern
						output_string += grid[num][0]
						grid[num].pop(0   )
			return output_string




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			{}
			for key_guess in range(1,  len(A   )   ):  # tries every key
						a    =			decrypt(A,  A   )
			return results


if __name__ == "__main__":
		import doctest

		doctest.testmod()











from collections import OrderedDict
from typing import Mapping

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


A__  :  Union[str, Any]								=					logging.get_logger(__name__)

A__  :  Tuple								=					{
    '''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						(     UpperCamelCase_						):
							_a			      = '''xlm-roberta-xl'''




							def __init__(       self     :       int				,				A_     :       List[str]=2_5_0_8_8_0				,				A_     :       List[str]=2_5_6_0				,				A_     :       Optional[int]=3_6				,				A_     :       List[Any]=3_2				,				A_     :       Optional[int]=1_0_2_4_0				,				A_     :       Dict="gelu"				,				A_     :       int=0.1				,				A_     :       Optional[Any]=0.1				,				A_     :       int=5_1_4				,				A_     :       Any=1				,				A_     :       Optional[Any]=0.02				,				A_     :       str=1e-05				,				A_     :       Dict=1				,				A_     :       Any=0				,				A_     :       Tuple=2				,				A_     :       str="absolute"				,				A_     :       str=True				,				A_     :       List[str]=None				,				**A_     :       Dict				,				):
									super().__init__(pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				**A_)
									lowerCAmelCase_			:     Tuple									= vocab_size
									lowerCAmelCase_			:     List[str]									= hidden_size
									lowerCAmelCase_			:     int									= num_hidden_layers
									lowerCAmelCase_			:     int									= num_attention_heads
									lowerCAmelCase_			:     Dict									= hidden_act
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= type_vocab_size
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     str									= layer_norm_eps
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= use_cache
									lowerCAmelCase_			:     List[str]									= classifier_dropout





class 				__snake_case						(     UpperCamelCase_						):




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





