




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
							from ...onnx.config import PatchingSpec
							from ...tokenization_utils_base import PreTrainedTokenizerBase


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Dict          =  {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """longformer"""

						def __init__(		self :Optional[int]     ,			__snake_case :Union[List[int], int] = 5_12     ,			__snake_case :int = 2     ,			__snake_case :int = 1     ,			__snake_case :int = 0     ,			__snake_case :int = 2     ,			__snake_case :int = 3_05_22     ,			__snake_case :int = 7_68     ,			__snake_case :int = 12     ,			__snake_case :int = 12     ,			__snake_case :int = 30_72     ,			__snake_case :str = "gelu"     ,			__snake_case :float = 0.1     ,			__snake_case :float = 0.1     ,			__snake_case :int = 5_12     ,			__snake_case :int = 2     ,			__snake_case :float = 0.02     ,			__snake_case :float = 1E-12     ,			__snake_case :bool = False     ,			**__snake_case :List[str]     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			**__snake_case			)

										__magic_name__						:						int										=attention_window
										__magic_name__						:						Dict										=sep_token_id
										__magic_name__						:						Dict										=bos_token_id
										__magic_name__						:						Tuple										=eos_token_id
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						Dict										=hidden_size
										__magic_name__						:						Any										=num_hidden_layers
										__magic_name__						:						Union[str, Any]										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						int										=intermediate_size
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						List[str]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[int]										=max_position_embeddings
										__magic_name__						:						Dict										=type_vocab_size
										__magic_name__						:						int										=initializer_range
										__magic_name__						:						Optional[Any]										=layer_norm_eps
										__magic_name__						:						int										=onnx_export




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :Optional[Any]     ,			__snake_case :"PretrainedConfig"     ,			__snake_case :str = "default"     ,			__snake_case :"List[PatchingSpec]" = None			):
										'''simple docstring'''

										super().__init__(__snake_case     ,			__snake_case     ,			__snake_case			)
										__magic_name__						:						Optional[Any]										=True

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						Dict										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						List[str]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""global_attention_mask""", dynamic_axis),
										    ]			)

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=super().outputs
										if self.task == "default":
														__magic_name__						:						List[str]										={0: """batch"""}
										return outputs

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return 1E-4

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return max(super().default_onnx_opset     ,			14			)


						def      A__    (		self :int     ,			__snake_case :"PreTrainedTokenizerBase"     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=super().generate_dummy_inputs(
										    preprocessor=__snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)
										import torch

										# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
										# makes the export fail randomly
										__magic_name__						:						List[Any]										=torch.zeros_like(inputs["""input_ids"""]			)
										# make every second token global
										__magic_name__						:						Optional[int]										=1

										return inputs







from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

