



from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      """mega"""





     def __init__(							self			:				Dict  ,	UpperCamelCase			:				str=3_0_5_2_2  ,	UpperCamelCase			:				Tuple=1_2_8  ,	UpperCamelCase			:				List[Any]=4  ,	UpperCamelCase			:				Optional[int]=2_5_6  ,	UpperCamelCase			:				List[Any]=1_6  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Tuple=6_4  ,	UpperCamelCase			:				Dict=False  ,	UpperCamelCase			:				List[Any]=-1  ,	UpperCamelCase			:				Any=None  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Optional[Any]="scalenorm"  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				List[str]="silu"  ,	UpperCamelCase			:				int="softmax"  ,	UpperCamelCase			:				Dict=0.1  ,	UpperCamelCase			:				Tuple=0.1  ,	UpperCamelCase			:				List[str]=0.1  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				Tuple=True  ,	UpperCamelCase			:				Optional[int]=2_5_6  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Optional[int]=0.1  ,	UpperCamelCase			:				Optional[Any]=2_0_4_8  ,	UpperCamelCase			:				Optional[Any]=False  ,	UpperCamelCase			:				Optional[Any]=2  ,	UpperCamelCase			:				Optional[Any]=0.0_2  ,	UpperCamelCase			:				List[str]=0.2  ,	UpperCamelCase			:				int=0.0_2  ,	UpperCamelCase			:				Tuple=1.0  ,	UpperCamelCase			:				List[str]=1  ,	UpperCamelCase			:				Optional[Any]=0  ,	UpperCamelCase			:				Optional[Any]=2  ,	UpperCamelCase			:				Union[str, Any]="rotary"  ,	UpperCamelCase			:				Optional[int]=None  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				Union[str, Any]=True  ,	**UpperCamelCase			:				Tuple  ,	)->Dict:
      super().__init__(pad_token_id=UpperCamelCase  ,	bos_token_id=UpperCamelCase  ,	eos_token_id=UpperCamelCase  ,	**UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						vocab_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						hidden_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						num_hidden_layers
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						activation
      __SCREAMING_SNAKE_CASE							:     Tuple							=						attention_activation
      __SCREAMING_SNAKE_CASE							:     Any							=						intermediate_size
      __SCREAMING_SNAKE_CASE							:     int							=						ema_projection_size
      __SCREAMING_SNAKE_CASE							:     int							=						bidirectional
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						shared_representation_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						use_chunking
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						chunk_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						truncation
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						normalize_before_mega
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						normalization_type
      __SCREAMING_SNAKE_CASE							:     Tuple							=						norm_affine
      __SCREAMING_SNAKE_CASE							:     str							=						dropout_prob
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[str]							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						use_feature_dropout
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						use_normalized_ffn
      __SCREAMING_SNAKE_CASE							:     str							=						nffn_hidden_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						normalize_before_ffn
      __SCREAMING_SNAKE_CASE							:     Any							=						nffn_activation_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[str]							=						max_positions
      __SCREAMING_SNAKE_CASE							:     List[str]							=						add_token_type_embeddings
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						type_vocab_size
      __SCREAMING_SNAKE_CASE							:     Dict							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Tuple							=						ema_delta_alpha_range
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						ema_beta_range
      __SCREAMING_SNAKE_CASE							:     Tuple							=						ema_gamma_omega_range
      __SCREAMING_SNAKE_CASE							:     Any							=						relative_positional_bias
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						use_cache
      __SCREAMING_SNAKE_CASE							:     int							=						classifier_dropout
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						add_lm_hidden_dense_layer
      __SCREAMING_SNAKE_CASE							:     Tuple							=						1  # not used but required by Hugging Face






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





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











'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



