



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase_      			=  logging.get_logger(__name__)







class 				a_    (						_snake_case							):
						UpperCamelCase__ :		Union[str, Any]										="mt5"
						UpperCamelCase__ :		Optional[Any]										=["past_key_values"]

						def __init__(       self      :Optional[Any]  ,		_lowercase      :Union[str, Any]=250112  ,		_lowercase      :Dict=512  ,		_lowercase      :Dict=64  ,		_lowercase      :List[str]=1024  ,		_lowercase      :Union[str, Any]=8  ,		_lowercase      :List[str]=None  ,		_lowercase      :Optional[Any]=6  ,		_lowercase      :List[str]=32  ,		_lowercase      :Tuple=128  ,		_lowercase      :Optional[int]=0.1  ,		_lowercase      :int=1E-6  ,		_lowercase      :Dict=1.0  ,		_lowercase      :str="gated-gelu"  ,		_lowercase      :List[Any]=True  ,		_lowercase      :Any=True  ,		_lowercase      :Any="T5Tokenizer"  ,		_lowercase      :int=False  ,		_lowercase      :List[Any]=0  ,		_lowercase      :List[str]=1  ,		_lowercase      :Dict=0  ,		**_lowercase      :Tuple  ,		)						->   Dict:
												super().__init__(
												    is_encoder_decoder=_lowercase  ,		tokenizer_class=_lowercase  ,		tie_word_embeddings=_lowercase  ,		pad_token_id=_lowercase  ,		eos_token_id=_lowercase  ,		decoder_start_token_id=_lowercase  ,		**_lowercase  ,		)
												UpperCAmelCase_					=	vocab_size
												UpperCAmelCase_					=	d_model
												UpperCAmelCase_					=	d_kv
												UpperCAmelCase_					=	d_ff
												UpperCAmelCase_					=	num_layers
												UpperCAmelCase_					=	(
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												UpperCAmelCase_					=	num_heads
												UpperCAmelCase_					=	relative_attention_num_buckets
												UpperCAmelCase_					=	relative_attention_max_distance
												UpperCAmelCase_					=	dropout_rate
												UpperCAmelCase_					=	layer_norm_epsilon
												UpperCAmelCase_					=	initializer_factor
												UpperCAmelCase_					=	feed_forward_proj
												UpperCAmelCase_					=	use_cache

												UpperCAmelCase_					=	self.feed_forward_proj.split('''-''')
												UpperCAmelCase_					=	act_info[-1]
												UpperCAmelCase_					=	act_info[0] == '''gated'''

												if len(_lowercase) > 1 and act_info[0] != "gated" or len(_lowercase) > 2:
																		raise ValueError(
																		    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
																		    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																		    '''\'gated-gelu\' or \'relu\'''')

												# for backwards compatibility
												if feed_forward_proj == "gated-gelu":
																		UpperCAmelCase_					=	'''gelu_new'''

						@property
						def       __a			(       self      :Optional[Any])						->   Optional[Any]:
												return self.d_model

						@property
						def       __a			(       self      :Dict)						->   Optional[int]:
												return self.num_heads




						@property
						def       __a			(       self      :List[str])						->   Optional[int]:
												return self.num_layers







class 				a_    (						_snake_case							):

						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def       __a			(       self      :Optional[Any])						->   Mapping[str, Mapping[int, str]]:
												UpperCAmelCase_					=	{
												    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
												    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
												}
												if self.use_past:
																		UpperCAmelCase_					=	'''past_encoder_sequence + sequence'''
																		UpperCAmelCase_					=	{0: '''batch'''}
																		UpperCAmelCase_					=	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												else:
																		UpperCAmelCase_					=	{0: '''batch''', 1: '''decoder_sequence'''}
																		UpperCAmelCase_					=	{0: '''batch''', 1: '''decoder_sequence'''}

												if self.use_past:
																		self.fill_with_past_key_values_(_lowercase  ,		direction='''inputs''')

												return common_inputs

						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def       __a			(       self      :List[str])						->   int:
												return 13




						@property
						def       __a			(       self      :Optional[int])						->   float:
												return 5E-4










def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						print('''\nVertex Distance'''      )
						for i in range(__UpperCAmelCase      ):
												if dist[i] != float('''inf'''      ):
																		print(__UpperCAmelCase				,   '''\t'''				,   int(dist[i]      )				,   end='''\t'''      )
												else:
																		print(__UpperCAmelCase				,   '''\t'''				,   '''INF'''				,   end='''\t'''      )
												print()







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			str:



						'''simple docstring'''






						UpperCAmelCase_					=	float('''inf'''      )
						UpperCAmelCase_					=	-1
						for i in range(__UpperCAmelCase      ):
												if (not vset[i]) and mdist[i] < min_val:
																		UpperCAmelCase_					=	i
																		UpperCAmelCase_					=	mdist[i]
						return min_ind







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	[float('''inf'''      ) for _ in range(__UpperCAmelCase      )]
						UpperCAmelCase_					=	[False for _ in range(__UpperCAmelCase      )]
						UpperCAmelCase_					=	0.0

						for _ in range(v - 1      ):
												UpperCAmelCase_					=	min_dist(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												UpperCAmelCase_					=	True

												for i in range(__UpperCAmelCase      ):
																		if (
																		    (not vset[i])
																		    and graph[u][i] != float('''inf'''      )
																		    and mdist[u] + graph[u][i] < mdist[i]
																		):
																								UpperCAmelCase_					=	mdist[u] + graph[u][i]

						print_dist(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							UpperCamelCase_      			=  int(input("Enter number of vertices: ").strip())
							UpperCamelCase_      			=  int(input("Enter number of edges: ").strip())

							UpperCamelCase_      			=  [[float("inf") for i in range(V)] for j in range(V)]

							for i in range(V):
														UpperCamelCase_      			=  0.0

							for i in range(E):
														print("\nEdge ", i + 1)
														UpperCamelCase_      			=  int(input("Enter source:").strip())
														UpperCamelCase_      			=  int(input("Enter destination:").strip())
														UpperCamelCase_      			=  float(input("Enter weight:").strip())
														UpperCamelCase_      			=  weight

							UpperCamelCase_      			=  int(input("\nEnter shortest path source:").strip())
							dijkstra(graph, V, gsrc)





