





from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase				:       List[str]	   =				logging.get_logger(__name__)

_UpperCAmelCase				:       Union[str, Any]	   =				{
    """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    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""xlm-roberta-xl"""
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      Dict=250880			,     UpperCAmelCase  :      int=2560			,     UpperCAmelCase  :      Tuple=36			,     UpperCAmelCase  :      Dict=32			,     UpperCAmelCase  :      Dict=10240			,     UpperCAmelCase  :      List[Any]="gelu"			,     UpperCAmelCase  :      Optional[Any]=0.1			,     UpperCAmelCase  :      Optional[int]=0.1			,     UpperCAmelCase  :      int=514			,     UpperCAmelCase  :      Any=1			,     UpperCAmelCase  :      Dict=0.0_2			,     UpperCAmelCase  :      Union[str, Any]=1e-05			,     UpperCAmelCase  :      List[Any]=1			,     UpperCAmelCase  :      Any=0			,     UpperCAmelCase  :      List[str]=2			,     UpperCAmelCase  :      List[Any]="absolute"			,     UpperCAmelCase  :      Tuple=True			,     UpperCAmelCase  :      Optional[Any]=None			,     **UpperCAmelCase  :      Any			,     )			->	List[str]:
     super().__init__(pad_token_id=UpperCAmelCase			,     bos_token_id=UpperCAmelCase			,     eos_token_id=UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		vocab_size
     lowerCamelCase__    :      str					 =		hidden_size
     lowerCamelCase__    :      str					 =		num_hidden_layers
     lowerCamelCase__    :      List[Any]					 =		num_attention_heads
     lowerCamelCase__    :      List[str]					 =		hidden_act
     lowerCamelCase__    :      Dict					 =		intermediate_size
     lowerCamelCase__    :      Any					 =		hidden_dropout_prob
     lowerCamelCase__    :      List[str]					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      Tuple					 =		max_position_embeddings
     lowerCamelCase__    :      Any					 =		type_vocab_size
     lowerCamelCase__    :      Optional[Any]					 =		initializer_range
     lowerCamelCase__    :      List[str]					 =		layer_norm_eps
     lowerCamelCase__    :      Any					 =		position_embedding_type
     lowerCamelCase__    :      int					 =		use_cache
     lowerCamelCase__    :      int					 =		classifier_dropout


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












from itertools import count


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 50 )			->     int:
    lowerCamelCase__    :      Optional[Any]					 =		[1] * min_block_length

    for n in count(_UpperCAmelCase ):
        fill_count_functions.append(1 )

        for block_length in range(_UpperCAmelCase						,	n + 1 ):
            for block_start in range(n - block_length ):
                fill_count_functions[n] += fill_count_functions[
                    n - block_start - block_length - 1
                ]

            fill_count_functions[n] += 1

        if fill_count_functions[n] > 100_0000:
            break

    return n


if __name__ == "__main__":
    print(F"""{solution() = }""")





