


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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[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
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

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




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






import math

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


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Optional[int]        = 'data2vec-audio'
 def __init__(   self				, lowerCAmelCase__=32				, lowerCAmelCase__=768				, lowerCAmelCase__=12				, lowerCAmelCase__=12				, lowerCAmelCase__=3_072				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0_2				, lowerCAmelCase__=1E-5				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512)				, lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)				, lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)				, lowerCAmelCase__=False				, lowerCAmelCase__=16				, lowerCAmelCase__=19				, lowerCAmelCase__=5				, lowerCAmelCase__=0.0_5				, lowerCAmelCase__=10				, lowerCAmelCase__=2				, lowerCAmelCase__=0.0				, lowerCAmelCase__=10				, lowerCAmelCase__=0				, lowerCAmelCase__="sum"				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=256				, lowerCAmelCase__=(512, 512, 512, 512, 1_500)				, lowerCAmelCase__=(5, 3, 3, 1, 1)				, lowerCAmelCase__=(1, 2, 3, 1, 1)				, lowerCAmelCase__=512				, lowerCAmelCase__=0				, lowerCAmelCase__=1				, lowerCAmelCase__=2				, lowerCAmelCase__=False				, lowerCAmelCase__=3				, lowerCAmelCase__=2				, lowerCAmelCase__=3				, lowerCAmelCase__=None				, **lowerCAmelCase__				, )					->				Optional[int]:




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

        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)`, but is `len(config.conv_dim) ='
                   F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
                   F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

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

        # ctc loss
        lowercase__:   Union[str, Any]  			=   ctc_loss_reduction
        lowercase__:   str  			=   ctc_zero_infinity

        # adapter
        lowercase__:   str  			=   add_adapter
        lowercase__:   List[Any]  			=   adapter_kernel_size
        lowercase__:   Tuple  			=   adapter_stride
        lowercase__:   Dict  			=   num_adapter_layers
        lowercase__:   Optional[Any]  			=   output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase__:   List[Any]  			=   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Dict  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   str  			=   xvector_output_dim
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return math.prod(self.conv_stride )


