



'''simple docstring'''






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


UpperCamelCase__:     List[str]										=    logging.get_logger(__name__)

UpperCamelCase__:     Optional[Any]										=    {
    "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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """speech_to_text_2"""
	lowerCamelCase__    				= ["""past_key_values"""]
	lowerCamelCase__    				= {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}







	def __init__(		self    :  Any	,    __snake_case    :  str=10000	,    __snake_case    :  int=6	,    __snake_case    :  Any=2048	,    __snake_case    :  Tuple=4	,    __snake_case    :  Optional[int]=0.0	,    __snake_case    :  Any=True	,    __snake_case    :  Tuple="relu"	,    __snake_case    :  Optional[int]=256	,    __snake_case    :  Any=0.1	,    __snake_case    :  Dict=0.0	,    __snake_case    :  Tuple=0.0	,    __snake_case    :  int=0.02	,    __snake_case    :  Optional[int]=2	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  List[Any]=1	,    __snake_case    :  Dict=0	,    __snake_case    :  Any=2	,    __snake_case    :  List[Any]=1024	,    **__snake_case    :  Dict	,    )    ->	Tuple:
								UpperCAmelCase				: Optional[int]											=	vocab_size
								UpperCAmelCase				: int											=	d_model
								UpperCAmelCase				: Optional[int]											=	decoder_ffn_dim
								UpperCAmelCase				: Dict											=	decoder_layers
								UpperCAmelCase				: List[Any]											=	decoder_attention_heads
								UpperCAmelCase				: Dict											=	dropout
								UpperCAmelCase				: Tuple											=	attention_dropout
								UpperCAmelCase				: Tuple											=	activation_dropout
								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Optional[Any]											=	init_std
								UpperCAmelCase				: int											=	decoder_layerdrop
								UpperCAmelCase				: List[Any]											=	use_cache
								UpperCAmelCase				: List[Any]											=	decoder_layers
								UpperCAmelCase				: Tuple											=	scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase				: int											=	max_target_positions

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










'''simple docstring'''






def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     Union[str, Any]:
							# Return True if there is node that has not iterated.
							UpperCAmelCase				: Optional[Any]											=	[False] * len(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	[]
							queue.append(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	True

							while queue:
														UpperCAmelCase				: Optional[int]											=	queue.pop(0						)
														for ind in range(len(graph[u]						)						):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(_lowerCAmelCase						)
																												UpperCAmelCase				: Union[str, Any]											=	True
																												UpperCAmelCase				: Optional[int]											=	u

							return visited[t]


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]						)   ->     Any:
							# This array is filled by BFS and to store path
							UpperCAmelCase				: Dict											=	[-1] * (len(_lowerCAmelCase						))
							UpperCAmelCase				: Any											=	0
							while bfs(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: List[str]											=	float('''Inf'''						)
														UpperCAmelCase				: str											=	sink

														while s != source:
																					# Find the minimum value in select path
																					UpperCAmelCase				: int											=	min(_lowerCAmelCase     ,   graph[parent[s]][s]						)
																					UpperCAmelCase				: Tuple											=	parent[s]

														max_flow += path_flow
														UpperCAmelCase				: Optional[Any]											=	sink

														while v != source:
																					UpperCAmelCase				: Dict											=	parent[v]
																					graph[u][v] -= path_flow
																					graph[v][u] += path_flow
																					UpperCAmelCase				: Dict											=	parent[v]
							return max_flow


UpperCamelCase__:     Tuple										=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCamelCase__			,      UpperCamelCase__:     int										=    0, 5




print(ford_fulkerson(graph, source, sink))





