


from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   str	,  a	:   str	,  a	:   Dict	,  a	:   Dict	,  a	:   Union[str, Any]	,  a	:   List[str]	,  a	:   Any	,  a	:   Any	,  )    ->	float | int:





			"""simple docstring"""







			for nxt, d in graph[v]:
						if nxt in visited_forward:
									continue
						a__ :str						  =    cst_fwd.get(__snake_case	,  np.inf				)
						a__ :Any						  =    cst_fwd[v] + d
						if new_cost_f < old_cost_f:
									queue.put((new_cost_f, nxt)				)
									a__ :Tuple						  =    new_cost_f
									a__ :Dict						  =    v
						if nxt in visited_backward:
									if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
												a__ :Any						  =    cst_fwd[v] + d + cst_bwd[nxt]
			return shortest_distance


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   List[str]	,  a	:   List[str]	,  a	:   List[str]				)    ->	int:





			"""simple docstring"""
			a__ :str						  =    -1

			a__ :Dict						  =    set()
			a__ :List[Any]						  =    set()
			a__ :List[str]						  =    {source: 0}
			a__ :List[Any]						  =    {destination: 0}
			a__ :Dict						  =    {source: None}
			a__ :Tuple						  =    {destination: None}
			a__ :Union[str, Any]						  =    PriorityQueue()
			a__ :Optional[Any]						  =    PriorityQueue()

			a__ :str						  =    np.inf

			queue_forward.put((0, source)				)
			queue_backward.put((0, destination)				)

			if source == destination:
						return 0

			while not queue_forward.empty() and not queue_backward.empty():
						a__  ,  a__ :Tuple						  =    queue_forward.get()
						visited_forward.add(__snake_case				)

						a__  ,  a__ :Any						  =    queue_backward.get()
						visited_backward.add(__snake_case				)

						a__ :Optional[int]						  =    pass_and_relaxation(
						    __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  )

						a__ :Optional[int]						  =    pass_and_relaxation(
						    __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  )

						if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
									break

			if shortest_distance != np.inf:
						a__ :List[str]						  =    shortest_distance
			return shortest_path_distance


snake_case__						 =						{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
snake_case__						 =						{
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'speech_to_text_2'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]=10000   ,     __A						:   Tuple=6   ,     __A						:   Tuple=2048   ,     __A						:   Any=4   ,     __A						:   str=0.0   ,     __A						:   Tuple=True   ,     __A						:   List[Any]="relu"   ,     __A						:   Union[str, Any]=256   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]=0.0   ,     __A						:   Tuple=0.0   ,     __A						:   Any=0.02   ,     __A						:   int=2   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple=1   ,     __A						:   List[str]=0   ,     __A						:   List[str]=2   ,     __A						:   Union[str, Any]=1024   ,     **__A						:   Tuple   ,     )      ->List[str]:






									"""simple docstring"""






									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    d_model
									a__ :Optional[int]						  =    decoder_ffn_dim
									a__ :Optional[int]						  =    decoder_layers
									a__ :Optional[int]						  =    decoder_attention_heads
									a__ :Dict						  =    dropout
									a__ :Optional[Any]						  =    attention_dropout
									a__ :Any						  =    activation_dropout
									a__ :int						  =    activation_function
									a__ :Optional[int]						  =    init_std
									a__ :List[Any]						  =    decoder_layerdrop
									a__ :Optional[Any]						  =    use_cache
									a__ :List[str]						  =    decoder_layers
									a__ :int						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									a__ :Optional[int]						  =    max_target_positions

									super().__init__(
									    pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     decoder_start_token_id=__A   ,     **__A   ,     )


