



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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """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 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: List[Any] =  "mctct"

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Dict=80_65			,						__lowerCAmelCase					:					Any=15_36			,						__lowerCAmelCase					:					Optional[int]=36			,						__lowerCAmelCase					:					Dict=61_44			,						__lowerCAmelCase					:					Union[str, Any]=4			,						__lowerCAmelCase					:					Union[str, Any]=3_84			,						__lowerCAmelCase					:					Dict=9_20			,						__lowerCAmelCase					:					Optional[Any]=1E-5			,						__lowerCAmelCase					:					Optional[int]=0.3			,						__lowerCAmelCase					:					Any="relu"			,						__lowerCAmelCase					:					str=0.02			,						__lowerCAmelCase					:					Optional[Any]=0.3			,						__lowerCAmelCase					:					int=0.3			,						__lowerCAmelCase					:					Optional[int]=1			,						__lowerCAmelCase					:					Optional[int]=0			,						__lowerCAmelCase					:					Any=2			,						__lowerCAmelCase					:					List[Any]=1			,						__lowerCAmelCase					:					Tuple=0.3			,						__lowerCAmelCase					:					Union[str, Any]=1			,						__lowerCAmelCase					:					List[str]=(7,)			,						__lowerCAmelCase					:					Any=(3,)			,						__lowerCAmelCase					:					List[Any]=80			,						__lowerCAmelCase					:					List[str]=1			,						__lowerCAmelCase					:					Optional[int]=None			,						__lowerCAmelCase					:					str="sum"			,						__lowerCAmelCase					:					Tuple=False			,						**__lowerCAmelCase					:					int			,						)   ->	Tuple:
    super().__init__(**__lowerCAmelCase			,						pad_token_id=__lowerCAmelCase			,						bos_token_id=__lowerCAmelCase			,						eos_token_id=__lowerCAmelCase      )
    _A      =       vocab_size
    _A      =       hidden_size
    _A      =       num_hidden_layers
    _A      =       intermediate_size
    _A      =       num_attention_heads
    _A      =       attention_head_dim
    _A      =       max_position_embeddings
    _A      =       layer_norm_eps
    _A      =       layerdrop
    _A      =       hidden_act
    _A      =       initializer_range
    _A      =       hidden_dropout_prob
    _A      =       attention_probs_dropout_prob
    _A      =       pad_token_id
    _A      =       bos_token_id
    _A      =       eos_token_id
    _A      =       conv_glu_dim
    _A      =       conv_dropout
    _A      =       num_conv_layers
    _A      =       input_feat_per_channel
    _A      =       input_channels
    _A      =       conv_channels
    _A      =       ctc_loss_reduction
    _A      =       ctc_zero_infinity

    # prevents config testing fail with exporting to json
    _A      =       list(__lowerCAmelCase      )
    _A      =       list(__lowerCAmelCase      )

    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_							(	_snake_case	:dict	,   _snake_case	:str				)  ->     set[str]:
   _A  ,    _A      =       set(_snake_case				), [start]

   while stack:
      _A      =       stack.pop()
      explored.add(_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(_snake_case				)
   return explored


UpperCAmelCase_		 =					{
    """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"""))



