




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


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

lowerCamelCase    :		List[Any]			   =							{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''mctct'''

 def __init__(    self :  List[Any]						,							A_ :  Dict=8065						,							A_ :  Optional[int]=1536						,							A_ :  List[Any]=36						,							A_ :  Optional[int]=6144						,							A_ :  Any=4						,							A_ :  Tuple=384						,							A_ :  str=920						,							A_ :  Optional[int]=1E-5						,							A_ :  List[str]=0.3						,							A_ :  Optional[Any]="relu"						,							A_ :  Union[str, Any]=0.02						,							A_ :  Any=0.3						,							A_ :  Optional[int]=0.3						,							A_ :  Dict=1						,							A_ :  Dict=0						,							A_ :  Optional[Any]=2						,							A_ :  int=1						,							A_ :  Optional[int]=0.3						,							A_ :  Optional[int]=1						,							A_ :  Any=(7,)						,							A_ :  Tuple=(3,)						,							A_ :  int=80						,							A_ :  Optional[int]=1						,							A_ :  Any=None						,							A_ :  str="sum"						,							A_ :  Dict=False						,							**A_ :  str						,							)       ->     Tuple:

       """simple docstring"""






       super().__init__(**A_						,							pad_token_id=A_						,							bos_token_id=A_						,							eos_token_id=A_  )
       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   attention_head_dim
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   layer_norm_eps
       lowerCamelCase_							=   layerdrop
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   initializer_range
       lowerCamelCase_							=   hidden_dropout_prob
       lowerCamelCase_							=   attention_probs_dropout_prob
       lowerCamelCase_							=   pad_token_id
       lowerCamelCase_							=   bos_token_id
       lowerCamelCase_							=   eos_token_id
       lowerCamelCase_							=   conv_glu_dim
       lowerCamelCase_							=   conv_dropout
       lowerCamelCase_							=   num_conv_layers
       lowerCamelCase_							=   input_feat_per_channel
       lowerCamelCase_							=   input_channels
       lowerCamelCase_							=   conv_channels
       lowerCamelCase_							=   ctc_loss_reduction
       lowerCamelCase_							=   ctc_zero_infinity

       # prevents config testing fail with exporting to json
       lowerCamelCase_							=   list(A_  )
       lowerCamelCase_							=   list(A_  )

       if len(self.conv_kernel  ) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
                 f"""but is `len(config.conv_kernel) = {len(self.conv_kernel  )}`, """
                 f"""`config.num_conv_layers = {self.num_conv_layers}`."""  )









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # 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(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "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"))



