"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					  =     logging.get_logger(__name__)

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



class      lowerCAmelCase_			(	__snake_case    ):



				"""simple docstring"""
				_lowerCAmelCase   :			int	     =  """switch_transformers"""
				_lowerCAmelCase   :			Optional[Any]	     =  ["""past_key_values"""]
				_lowerCAmelCase   :			Any	     =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

				def __init__( self						,			lowerCAmelCase=3_21_28						,			lowerCAmelCase=7_68						,			lowerCAmelCase=64						,			lowerCAmelCase=20_48						,			lowerCAmelCase=64						,			lowerCAmelCase=12						,			lowerCAmelCase=3						,			lowerCAmelCase=12						,			lowerCAmelCase=3						,			lowerCAmelCase=12						,			lowerCAmelCase=8						,			lowerCAmelCase=False						,			lowerCAmelCase=0.01						,			lowerCAmelCase="float32"						,			lowerCAmelCase=False						,			lowerCAmelCase=32						,			lowerCAmelCase=1_28						,			lowerCAmelCase=0.1						,			lowerCAmelCase=1E-6						,			lowerCAmelCase=0.0_01						,			lowerCAmelCase=0.0_01						,			lowerCAmelCase=1.0						,			lowerCAmelCase="relu"						,			lowerCAmelCase=True						,			lowerCAmelCase=False						,			lowerCAmelCase=True						,			lowerCAmelCase=0						,			lowerCAmelCase=1						,			**lowerCAmelCase						,			):






										"""simple docstring"""

										snake_case	    =	vocab_size
										snake_case	    =	d_model
										snake_case	    =	d_kv
										snake_case	    =	d_ff

										snake_case	    =	num_sparse_encoder_layers

										snake_case	    =	num_layers
										snake_case	    =	(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										snake_case	    =	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:
																snake_case	    =	self.num_layers // self.num_sparse_encoder_layers
										else:
																snake_case	    =	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:
																snake_case	    =	self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
																snake_case	    =	self.num_decoder_layers  # HACK: this will create 0 sparse layers

										snake_case	    =	num_heads
										snake_case	    =	num_experts
										snake_case	    =	expert_capacity
										snake_case	    =	router_bias
										snake_case	    =	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}"""			)
										snake_case	    =	router_dtype

										snake_case	    =	router_ignore_padding_tokens
										snake_case	    =	relative_attention_num_buckets
										snake_case	    =	relative_attention_max_distance

										snake_case	    =	dropout_rate
										snake_case	    =	layer_norm_epsilon
										snake_case	    =	initializer_factor
										snake_case	    =	feed_forward_proj
										snake_case	    =	use_cache
										snake_case	    =	add_router_probs

										snake_case	    =	router_z_loss_coef
										snake_case	    =	router_aux_loss_coef

										snake_case	    =	self.feed_forward_proj.split('-'			)
										snake_case	    =	act_info[-1]
										snake_case	    =	act_info[0] == 'gated'

										if len(lowerCAmelCase			) > 1 and act_info[0] != "gated" or len(lowerCAmelCase			) > 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":
																snake_case	    =	'gelu_new'

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





from __future__ import annotations

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

