






import functools
import operator

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


lowerCamelCase     :							str    =logging.get_logger(__name__)

lowerCamelCase     :							Any    ={
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			'''sew'''







 def __init__(		self :       int							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       Tuple=7_68							,       SCREAMING_SNAKE_CASE :       str=12							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=12							,       SCREAMING_SNAKE_CASE :       Dict=30_72							,       SCREAMING_SNAKE_CASE :       Any=2							,       SCREAMING_SNAKE_CASE :       Dict="gelu"							,       SCREAMING_SNAKE_CASE :       int=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Any=0.1							,       SCREAMING_SNAKE_CASE :       Any=0.0							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       Optional[Any]=0.1							,       SCREAMING_SNAKE_CASE :       Tuple=0.0_2							,       SCREAMING_SNAKE_CASE :       int=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[int]="group"							,       SCREAMING_SNAKE_CASE :       Optional[int]="gelu"							,       SCREAMING_SNAKE_CASE :       List[str]=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)							,       SCREAMING_SNAKE_CASE :       List[str]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,       SCREAMING_SNAKE_CASE :       str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,       SCREAMING_SNAKE_CASE :       Optional[Any]=False							,       SCREAMING_SNAKE_CASE :       Optional[int]=1_28							,       SCREAMING_SNAKE_CASE :       Dict=16							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Dict=0.0_5							,       SCREAMING_SNAKE_CASE :       Optional[int]=10							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=2							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0							,       SCREAMING_SNAKE_CASE :       str=10							,       SCREAMING_SNAKE_CASE :       Dict=0							,       SCREAMING_SNAKE_CASE :       List[str]="mean"							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       SCREAMING_SNAKE_CASE :       Optional[int]=False							,       SCREAMING_SNAKE_CASE :       Optional[int]=2_56							,       SCREAMING_SNAKE_CASE :       int=0							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[Any]=2							,       **SCREAMING_SNAKE_CASE :       Optional[int]							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE							,       pad_token_id=SCREAMING_SNAKE_CASE							,       bos_token_id=SCREAMING_SNAKE_CASE							,       eos_token_id=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  hidden_size
      UpperCamelCase__      :       Union[str, Any]	    =  feat_extract_norm
      UpperCamelCase__      :       str	    =  feat_extract_activation
      UpperCamelCase__      :       Optional[Any]	    =  list(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       int	    =  list(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  list(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[Any]	    =  conv_bias
      UpperCamelCase__      :       List[str]	    =  num_conv_pos_embeddings
      UpperCamelCase__      :       List[str]	    =  num_conv_pos_embedding_groups
      UpperCamelCase__      :       int	    =  len(self.conv_dim						)
      UpperCamelCase__      :       List[str]	    =  num_hidden_layers
      UpperCamelCase__      :       Union[str, Any]	    =  intermediate_size
      UpperCamelCase__      :       Optional[int]	    =  squeeze_factor
      UpperCamelCase__      :       List[Any]	    =  hidden_act
      UpperCamelCase__      :       List[Any]	    =  num_attention_heads
      UpperCamelCase__      :       Optional[int]	    =  hidden_dropout
      UpperCamelCase__      :       Optional[int]	    =  attention_dropout
      UpperCamelCase__      :       str	    =  activation_dropout
      UpperCamelCase__      :       Dict	    =  feat_proj_dropout
      UpperCamelCase__      :       Union[str, Any]	    =  final_dropout
      UpperCamelCase__      :       List[str]	    =  layerdrop
      UpperCamelCase__      :       str	    =  layer_norm_eps
      UpperCamelCase__      :       Dict	    =  initializer_range
      UpperCamelCase__      :       Optional[int]	    =  vocab_size

      if (
          (len(self.conv_stride						) != self.num_feat_extract_layers)
          or (len(self.conv_kernel						) != self.num_feat_extract_layers)
          or (len(self.conv_dim						) != self.num_feat_extract_layers)
      ):
           raise ValueError(
               "Configuration for convolutional layers is incorrect."
               "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
               F'but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'
               F'= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'						)

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      UpperCamelCase__      :       int	    =  apply_spec_augment
      UpperCamelCase__      :       Optional[int]	    =  mask_time_prob
      UpperCamelCase__      :       Any	    =  mask_time_length
      UpperCamelCase__      :       List[Any]	    =  mask_time_min_masks
      UpperCamelCase__      :       Tuple	    =  mask_feature_prob
      UpperCamelCase__      :       Optional[int]	    =  mask_feature_length
      UpperCamelCase__      :       Optional[Any]	    =  mask_feature_min_masks

      # ctc loss
      UpperCamelCase__      :       Dict	    =  ctc_loss_reduction
      UpperCamelCase__      :       Optional[Any]	    =  ctc_zero_infinity

      # sequence classification
      UpperCamelCase__      :       str	    =  use_weighted_layer_sum
      UpperCamelCase__      :       Union[str, Any]	    =  classifier_proj_size







 @property
 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      return functools.reduce(operator.mul							,       self.conv_stride							,       1						)







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4