




import functools
import operator

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


lowerCamelCase__ :   Dict					=							logging.get_logger(__name__)

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





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    "sew-d"



 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		int=32				,				_lowerCAmelCase  :		Optional[int]=768				,				_lowerCAmelCase  :		Any=12				,				_lowerCAmelCase  :		str=12				,				_lowerCAmelCase  :		List[Any]=3_072				,				_lowerCAmelCase  :		Tuple=2				,				_lowerCAmelCase  :		Any=512				,				_lowerCAmelCase  :		str=256				,				_lowerCAmelCase  :		int=True				,				_lowerCAmelCase  :		str=True				,				_lowerCAmelCase  :		Optional[int]=("p2c", "c2p")				,				_lowerCAmelCase  :		Optional[int]="layer_norm"				,				_lowerCAmelCase  :		Union[str, Any]="gelu_python"				,				_lowerCAmelCase  :		Optional[int]=0.1				,				_lowerCAmelCase  :		Any=0.1				,				_lowerCAmelCase  :		Union[str, Any]=0.1				,				_lowerCAmelCase  :		Optional[int]=0.0				,				_lowerCAmelCase  :		Optional[Any]=0.1				,				_lowerCAmelCase  :		Optional[int]=0.02				,				_lowerCAmelCase  :		List[str]=1E-7				,				_lowerCAmelCase  :		str=1E-5				,				_lowerCAmelCase  :		Tuple="group"				,				_lowerCAmelCase  :		Optional[Any]="gelu"				,				_lowerCAmelCase  :		Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_lowerCAmelCase  :		Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_lowerCAmelCase  :		Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_lowerCAmelCase  :		Any=False				,				_lowerCAmelCase  :		str=128				,				_lowerCAmelCase  :		str=16				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		str=0.05				,				_lowerCAmelCase  :		str=10				,				_lowerCAmelCase  :		Union[str, Any]=2				,				_lowerCAmelCase  :		Optional[int]=0.0				,				_lowerCAmelCase  :		List[str]=10				,				_lowerCAmelCase  :		Optional[Any]=0				,				_lowerCAmelCase  :		Any="mean"				,				_lowerCAmelCase  :		str=False				,				_lowerCAmelCase  :		Dict=False				,				_lowerCAmelCase  :		List[Any]=256				,				_lowerCAmelCase  :		List[Any]=0				,				_lowerCAmelCase  :		Union[str, Any]=1				,				_lowerCAmelCase  :		Dict=2				,				**_lowerCAmelCase  :		str				,				):
       super().__init__(**_lowerCAmelCase				,				pad_token_id=_lowerCAmelCase				,				bos_token_id=_lowerCAmelCase				,				eos_token_id=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			feat_extract_norm
       SCREAMING_SNAKE_CASE_							=			feat_extract_activation
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			conv_bias
       SCREAMING_SNAKE_CASE_							=			num_conv_pos_embeddings
       SCREAMING_SNAKE_CASE_							=			num_conv_pos_embedding_groups
       SCREAMING_SNAKE_CASE_							=			len(self.conv_dim						)
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			intermediate_size
       SCREAMING_SNAKE_CASE_							=			squeeze_factor
       SCREAMING_SNAKE_CASE_							=			max_position_embeddings
       SCREAMING_SNAKE_CASE_							=			position_buckets
       SCREAMING_SNAKE_CASE_							=			share_att_key
       SCREAMING_SNAKE_CASE_							=			relative_attention
       SCREAMING_SNAKE_CASE_							=			norm_rel_ebd
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			hidden_dropout
       SCREAMING_SNAKE_CASE_							=			attention_dropout
       SCREAMING_SNAKE_CASE_							=			activation_dropout
       SCREAMING_SNAKE_CASE_							=			feat_proj_dropout
       SCREAMING_SNAKE_CASE_							=			final_dropout
       SCREAMING_SNAKE_CASE_							=			layer_norm_eps
       SCREAMING_SNAKE_CASE_							=			feature_layer_norm_eps
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			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
       SCREAMING_SNAKE_CASE_							=			apply_spec_augment
       SCREAMING_SNAKE_CASE_							=			mask_time_prob
       SCREAMING_SNAKE_CASE_							=			mask_time_length
       SCREAMING_SNAKE_CASE_							=			mask_time_min_masks
       SCREAMING_SNAKE_CASE_							=			mask_feature_prob
       SCREAMING_SNAKE_CASE_							=			mask_feature_length
       SCREAMING_SNAKE_CASE_							=			mask_feature_min_masks

       # ctc loss
       SCREAMING_SNAKE_CASE_							=			ctc_loss_reduction
       SCREAMING_SNAKE_CASE_							=			ctc_zero_infinity

       # sequence classification
       SCREAMING_SNAKE_CASE_							=			use_weighted_layer_sum
       SCREAMING_SNAKE_CASE_							=			classifier_proj_size




 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return functools.reduce(operator.mul				,				self.conv_stride				,				1						)





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

       # call the recursive helper function
       for i in range(len(self.vertex						)						):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3