

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__				=		logging.get_logger(__name__)

a__				=		{
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}

class 				UpperCAmelCase_		(			lowercase__   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    '''switch_transformers'''
							UpperCAmelCase__       :						Optional[int]	    =    ['''past_key_values''']
							UpperCAmelCase__       :						Optional[Any]	    =    {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


							def __init__(			self       ,							_a=3_2_1_2_8       ,							_a=7_6_8       ,							_a=6_4       ,							_a=2_0_4_8       ,							_a=6_4       ,							_a=1_2       ,							_a=3       ,							_a=1_2       ,							_a=3       ,							_a=1_2       ,							_a=8       ,							_a=False       ,							_a=0.01       ,							_a="float32"       ,							_a=False       ,							_a=3_2       ,							_a=1_2_8       ,							_a=0.1       ,							_a=1e-6       ,							_a=0.001       ,							_a=0.001       ,							_a=1.0       ,							_a="relu"       ,							_a=True       ,							_a=False       ,							_a=True       ,							_a=0       ,							_a=1       ,							**_a       ,							)    ->    Optional[Any]:
												_a	:    Optional[int]         =		vocab_size
												_a	:    List[str]         =		d_model
												_a	:    Tuple         =		d_kv
												_a	:    Optional[Any]         =		d_ff

												_a	:    List[Any]         =		num_sparse_encoder_layers

												_a	:    Any         =		num_layers
												_a	:    str         =		(
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												_a	:    List[Any]         =		num_sparse_decoder_layers

												# This tells us, each how many encoder layer we'll have to set a sparse layer.
												if self.num_sparse_encoder_layers > 0:
																	_a	:    Optional[int]         =		self.num_layers // self.num_sparse_encoder_layers
												else:
																	_a	:    List[Any]         =		self.num_layers  # HACK: this will create 0 sparse layers

												# This tells us, each how many encoder layer we'll have to set a sparse layer.
												if self.num_sparse_decoder_layers > 0:
																	_a	:    Union[str, Any]         =		self.num_decoder_layers // self.num_sparse_decoder_layers
												else:
																	_a	:    List[str]         =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

												_a	:    Dict         =		num_heads
												_a	:    str         =		num_experts
												_a	:    Any         =		expert_capacity
												_a	:    List[Any]         =		router_bias
												_a	:    str         =		router_jitter_noise
												if router_dtype not in ["float32", "float16", "bfloat16"]:
																	raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""       )
												_a	:    Optional[int]         =		router_dtype

												_a	:    int         =		router_ignore_padding_tokens
												_a	:    Any         =		relative_attention_num_buckets
												_a	:    List[str]         =		relative_attention_max_distance

												_a	:    Optional[Any]         =		dropout_rate
												_a	:    Tuple         =		layer_norm_epsilon
												_a	:    Dict         =		initializer_factor
												_a	:    Any         =		feed_forward_proj
												_a	:    Tuple         =		use_cache
												_a	:    str         =		add_router_probs

												_a	:    Optional[int]         =		router_z_loss_coef
												_a	:    List[str]         =		router_aux_loss_coef

												_a	:    int         =		self.feed_forward_proj.split('''-'''       )
												_a	:    int         =		act_info[-1]
												_a	:    Optional[int]         =		act_info[0] == 'gated'

												if len(SCREAMING_SNAKE_CASE__       ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__       ) > 2:
																	raise ValueError(
																	    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																	    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																	    '''\'gated-gelu\' or \'relu\''''       )

												# for backwards compatibility
												if feed_forward_proj == "gated-gelu":
																	_a	:    Any         =		'gelu_new'

												super().__init__(
												    pad_token_id=SCREAMING_SNAKE_CASE__       ,							eos_token_id=SCREAMING_SNAKE_CASE__       ,							is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,							**SCREAMING_SNAKE_CASE__       ,							)







def        SCREAMING_SNAKE_CASE_						(			__A :			list[int]  ,    __A :			str				)	->		list[int]:

       """simple docstring"""

       a_					:		Any							=  int(__A				)

       # Initialize Result
       a_					:		Tuple							=  []

       # Traverse through all denomination
       for denomination in reversed(__A				):
              # Find denominations
              while int(__A				) >= int(__A				):
                     total_value -= int(__A				)
                     answer.append(__A				)  # Append the "answers" array

       return answer


# Driver Code
if __name__ == "__main__":
   UpperCAmelCase_      : Union[str, Any]  					=       []
   UpperCAmelCase_      : Union[str, Any]  					=       '0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      UpperCAmelCase_      : List[Any]  					=       int(input('Enter the number of denominations you want to add: ').strip())

      for i in range(0, n):
         denominations.append(int(input(F'Denomination {i}: ').strip()))
      UpperCAmelCase_      : str  					=       input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase_      : List[Any]  					=       [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase_      : str  					=       input('Enter the change you want to make: ').strip()

   if int(value) == 0 or int(value) < 0:
      print('The total value cannot be zero or negative.')

   else:
      print(F'Following is minimal change for {value}: ')
      UpperCAmelCase_      : Optional[Any]  					=       find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')


