





'''simple docstring'''


import functools
import operator

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


lowerCamelCase__    	=     logging.get_logger(__name__)

lowerCamelCase__    	=     {
    'microsoft/unispeech-large-1500h-cv': (
        'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	List[str]          =     "unispeech"




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict=32	,  lowerCamelCase__  :     List[str]=7_68	,  lowerCamelCase__  :     str=12	,  lowerCamelCase__  :     int=12	,  lowerCamelCase__  :     int=30_72	,  lowerCamelCase__  :     Any="gelu"	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     Optional[int]=0.1	,  lowerCamelCase__  :     str=0.0	,  lowerCamelCase__  :     int=0.0	,  lowerCamelCase__  :     Dict=0.1	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     List[str]=0.0_2	,  lowerCamelCase__  :     Tuple=1E-5	,  lowerCamelCase__  :     Optional[int]="group"	,  lowerCamelCase__  :     List[str]="gelu"	,  lowerCamelCase__  :     Optional[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)	,  lowerCamelCase__  :     Optional[int]=(5, 2, 2, 2, 2, 2, 2)	,  lowerCamelCase__  :     List[Any]=(10, 3, 3, 3, 3, 2, 2)	,  lowerCamelCase__  :     int=False	,  lowerCamelCase__  :     Union[str, Any]=1_28	,  lowerCamelCase__  :     List[Any]=16	,  lowerCamelCase__  :     List[str]=False	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     List[Any]=0.0_5	,  lowerCamelCase__  :     Dict=10	,  lowerCamelCase__  :     Union[str, Any]=2	,  lowerCamelCase__  :     List[Any]=0.0	,  lowerCamelCase__  :     Tuple=10	,  lowerCamelCase__  :     Optional[Any]=0	,  lowerCamelCase__  :     str=3_20	,  lowerCamelCase__  :     Dict=2	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Optional[int]=1_00	,  lowerCamelCase__  :     Optional[Any]=2_56	,  lowerCamelCase__  :     Any=2_56	,  lowerCamelCase__  :     Any=0.1	,  lowerCamelCase__  :     str="mean"	,  lowerCamelCase__  :     Optional[Any]=False	,  lowerCamelCase__  :     Tuple=False	,  lowerCamelCase__  :     str=2_56	,  lowerCamelCase__  :     List[str]=80	,  lowerCamelCase__  :     Tuple=0	,  lowerCamelCase__  :     Optional[Any]=1	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     str=0.5	,  **lowerCamelCase__  :     Union[str, Any]	,  )   ->Any:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__	,  pad_token_id=lowerCamelCase__	,  bos_token_id=lowerCamelCase__	,  eos_token_id=lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      hidden_size
					_UpperCAmelCase		:       Any								=      feat_extract_norm
					_UpperCAmelCase		:       Optional[int]								=      feat_extract_activation
					_UpperCAmelCase		:       Tuple								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      conv_bias
					_UpperCAmelCase		:       int								=      num_conv_pos_embeddings
					_UpperCAmelCase		:       Dict								=      num_conv_pos_embedding_groups
					_UpperCAmelCase		:       Union[str, Any]								=      len(self.conv_dim )
					_UpperCAmelCase		:       Optional[int]								=      num_hidden_layers
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       List[Any]								=      hidden_act
					_UpperCAmelCase		:       Optional[int]								=      num_attention_heads
					_UpperCAmelCase		:       Dict								=      hidden_dropout
					_UpperCAmelCase		:       List[str]								=      attention_dropout
					_UpperCAmelCase		:       int								=      activation_dropout
					_UpperCAmelCase		:       Union[str, Any]								=      feat_proj_dropout
					_UpperCAmelCase		:       List[Any]								=      final_dropout
					_UpperCAmelCase		:       List[str]								=      layerdrop
					_UpperCAmelCase		:       Union[str, Any]								=      layer_norm_eps
					_UpperCAmelCase		:       Tuple								=      initializer_range
					_UpperCAmelCase		:       int								=      num_ctc_classes
					_UpperCAmelCase		:       Tuple								=      vocab_size
					_UpperCAmelCase		:       List[str]								=      do_stable_layer_norm
					_UpperCAmelCase		:       Optional[Any]								=      use_weighted_layer_sum
					_UpperCAmelCase		:       Dict								=      classifier_proj_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)`, 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
					_UpperCAmelCase		:       List[str]								=      apply_spec_augment
					_UpperCAmelCase		:       int								=      mask_time_prob
					_UpperCAmelCase		:       Union[str, Any]								=      mask_time_length
					_UpperCAmelCase		:       str								=      mask_time_min_masks
					_UpperCAmelCase		:       Dict								=      mask_feature_prob
					_UpperCAmelCase		:       Any								=      mask_feature_length
					_UpperCAmelCase		:       Union[str, Any]								=      mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					_UpperCAmelCase		:       List[str]								=      num_codevectors_per_group
					_UpperCAmelCase		:       str								=      num_codevector_groups
					_UpperCAmelCase		:       Optional[Any]								=      contrastive_logits_temperature
					_UpperCAmelCase		:       Union[str, Any]								=      feat_quantizer_dropout
					_UpperCAmelCase		:       Any								=      num_negatives
					_UpperCAmelCase		:       str								=      codevector_dim
					_UpperCAmelCase		:       List[str]								=      proj_codevector_dim
					_UpperCAmelCase		:       Dict								=      diversity_loss_weight

					# ctc loss
					_UpperCAmelCase		:       Optional[int]								=      ctc_loss_reduction
					_UpperCAmelCase		:       Tuple								=      ctc_zero_infinity

					# pretraining loss
					_UpperCAmelCase		:       List[str]								=      replace_prob






			@property
			def 							lowerCAmelCase__    (		self  :     str )   ->Union[str, Any]:



					'''simple docstring'''







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







'''simple docstring'''


from __future__ import annotations

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






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     dict[str, list[str]]	,  lowerCamelCase__  :     str )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      graph
					# mapping node to its parent in resulting breadth first tree
					_UpperCAmelCase		:       dict[str, str | None]								=      {}
					_UpperCAmelCase		:       List[Any]								=      source_vertex




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      {self.source_vertex}
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       List[str]								=      [self.source_vertex]  # first in first out queue

					while queue:
							_UpperCAmelCase		:       int								=      queue.pop(0 )
							for adjacent_vertex in self.graph[vertex]:
									if adjacent_vertex not in visited:
											visited.add(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      vertex
											queue.append(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







					if target_vertex == self.source_vertex:
							return self.source_vertex

					_UpperCAmelCase		:       int								=      self.parent.get(lowerCamelCase__ )
					if target_vertex_parent is None:
							_UpperCAmelCase		:       Tuple								=      (
							    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
							)
							raise ValueError(lowerCamelCase__ )

					return self.shortest_path(lowerCamelCase__ ) + F"""->{target_vertex}"""


if __name__ == "__main__":
						lowerCamelCase__    	=     Graph(graph, 'G')
						g.breath_first_search()
						print(g.shortest_path('D'))
						print(g.shortest_path('G'))
						print(g.shortest_path('Foo'))



