





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase :    int  							= logging.get_logger(__name__)

_lowerCamelCase :    str  							= {
    '''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 						lowerCamelCase       (__lowerCamelCase      ):







       """simple docstring"""




       UpperCAmelCase_      						=   "xmod"






       def __init__( self     :					List[str],			_UpperCAmelCase     :					Dict=3_0_5_2_2,			_UpperCAmelCase     :					List[str]=7_6_8,			_UpperCAmelCase     :					Union[str, Any]=1_2,			_UpperCAmelCase     :					Tuple=1_2,			_UpperCAmelCase     :					List[Any]=3_0_7_2,			_UpperCAmelCase     :					List[str]="gelu",			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					Dict=0.1,			_UpperCAmelCase     :					Optional[Any]=5_1_2,			_UpperCAmelCase     :					List[Any]=2,			_UpperCAmelCase     :					Union[str, Any]=0.02,			_UpperCAmelCase     :					int=1E-12,			_UpperCAmelCase     :					str=1,			_UpperCAmelCase     :					Optional[int]=0,			_UpperCAmelCase     :					Optional[int]=2,			_UpperCAmelCase     :					Dict="absolute",			_UpperCAmelCase     :					int=True,			_UpperCAmelCase     :					Optional[int]=None,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Any=2,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					int=True,			_UpperCAmelCase     :					List[Any]=True,			_UpperCAmelCase     :					Union[str, Any]=("en_XX",),			_UpperCAmelCase     :					List[str]=None,			**_UpperCAmelCase     :					List[Any],			)				->			List[str]:





              """simple docstring"""
              super().__init__(pad_token_id=_UpperCAmelCase,			bos_token_id=_UpperCAmelCase,			eos_token_id=_UpperCAmelCase,			**_UpperCAmelCase						)

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


class 						lowerCamelCase       (__lowerCamelCase      ):







       """simple docstring"""




       @property
       def  A_     ( self     :					Tuple						)				->			Mapping[str, Mapping[int, str]]:





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













_lowerCamelCase :    dict[tuple[int, int, int], int]  							= {}




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int       )	->    int:

							'''simple docstring'''






							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							SCREAMING_SNAKE_CASE__       :      Tuple     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				late + 1       )

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							SCREAMING_SNAKE_CASE__       :      Dict     =						_calculate(days - 1			,				absent + 1			,				0       )

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							SCREAMING_SNAKE_CASE__       :      Any     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				0       )

							SCREAMING_SNAKE_CASE__       :      str     =						state_late + state_absent + state_ontime

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						prizestrings
							return prizestrings




def _a (  SCREAMING_SNAKE_CASE__      :  int = 30       )	->    int:

							'''simple docstring'''






							return _calculate(SCREAMING_SNAKE_CASE__			,				absent=0			,				late=0       )


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






