

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowerCamelCase       :     int           =							logging.get_logger(__name__)

_lowerCamelCase       :     Dict           =							{
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}





class       __UpperCAmelCase  ( a__		):
   UpperCamelCase       =  "gpt2"
   UpperCamelCase       =  ["past_key_values"]
   UpperCamelCase       =  {
       "hidden_size": "n_embd",
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }



   def __init__(							self							:  List[Any],	__A							:  int=5_0_2_5_7,	__A							:  str=1_0_2_4,	__A							:  int=7_6_8,	__A							:  Tuple=1_2,	__A							:  Any=1_2,	__A							:  List[Any]=None,	__A							:  str="gelu_new",	__A							:  Union[str, Any]=0.1,	__A							:  Optional[Any]=0.1,	__A							:  Dict=0.1,	__A							:  Union[str, Any]=1E-5,	__A							:  Any=0.0_2,	__A							:  int="cls_index",	__A							:  List[Any]=True,	__A							:  str=None,	__A							:  Tuple=True,	__A							:  List[str]=0.1,	__A							:  List[str]=True,	__A							:  Optional[int]=True,	__A							:  str=5_0_2_5_6,	__A							:  List[Any]=5_0_2_5_6,	__A							:  List[str]=False,	__A							:  List[Any]=False,	**__A							:  Optional[Any],	):
     UpperCAmelCase :							int								=				vocab_size
     UpperCAmelCase :							List[str]								=				n_positions
     UpperCAmelCase :							Any								=				n_embd
     UpperCAmelCase :							Optional[Any]								=				n_layer
     UpperCAmelCase :							Optional[int]								=				n_head
     UpperCAmelCase :							Optional[Any]								=				n_inner
     UpperCAmelCase :							str								=				activation_function
     UpperCAmelCase :							Any								=				resid_pdrop
     UpperCAmelCase :							Tuple								=				embd_pdrop
     UpperCAmelCase :							Any								=				attn_pdrop
     UpperCAmelCase :							Dict								=				layer_norm_epsilon
     UpperCAmelCase :							Tuple								=				initializer_range
     UpperCAmelCase :							str								=				summary_type
     UpperCAmelCase :							int								=				summary_use_proj
     UpperCAmelCase :							Union[str, Any]								=				summary_activation
     UpperCAmelCase :							List[str]								=				summary_first_dropout
     UpperCAmelCase :							List[str]								=				summary_proj_to_labels
     UpperCAmelCase :							Dict								=				scale_attn_weights
     UpperCAmelCase :							List[Any]								=				use_cache
     UpperCAmelCase :							Optional[Any]								=				scale_attn_by_inverse_layer_idx
     UpperCAmelCase :							int								=				reorder_and_upcast_attn

     UpperCAmelCase :							Union[str, Any]								=				bos_token_id
     UpperCAmelCase :							Optional[int]								=				eos_token_id

     super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_,	eos_token_id=SCREAMING_SNAKE_CASE_,	**SCREAMING_SNAKE_CASE_		)






class       __UpperCAmelCase  ( a__		):



   def __init__(							self							:  Optional[Any],	__A							:  Dict,	__A							:  int = "default",	__A							:  str = None,	__A							:  Union[str, Any] = False,	):
     super().__init__(SCREAMING_SNAKE_CASE_,	task=SCREAMING_SNAKE_CASE_,	patching_specs=SCREAMING_SNAKE_CASE_,	use_past=SCREAMING_SNAKE_CASE_		)
     if not getattr(self._config,	'''pad_token_id''',	SCREAMING_SNAKE_CASE_		):
       # TODO: how to do that better?
       UpperCAmelCase :							Optional[Any]								=				0



   @property
   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Union[str, Any]								=				OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
     if self.use_past:
       self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,	direction='''inputs'''		)
       UpperCAmelCase :							str								=				{0: 'batch', 1: 'past_sequence + sequence'}
     else:
       UpperCAmelCase :							List[str]								=				{0: 'batch', 1: 'sequence'}

     return common_inputs



   @property
   def  __magic_name__					(							self							:  Optional[int]		):
     return self._config.n_layer



   @property
   def  __magic_name__					(							self							:  str		):
     return self._config.n_head



   def  __magic_name__					(							self							:  Dict,	__A							:  List[str],	__A							:  int = -1,	__A							:  List[str] = -1,	__A							:  List[Any] = False,	__A							:  int = None,	):
     UpperCAmelCase :							Optional[int]								=				super(SCREAMING_SNAKE_CASE_,	self		).generate_dummy_inputs(
         SCREAMING_SNAKE_CASE_,	batch_size=SCREAMING_SNAKE_CASE_,	seq_length=SCREAMING_SNAKE_CASE_,	is_pair=SCREAMING_SNAKE_CASE_,	framework=SCREAMING_SNAKE_CASE_		)

     # We need to order the input in the way they appears in the forward()
     UpperCAmelCase :							str								=				OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

     # Need to add the past_keys
     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
       else:
         import torch

         UpperCAmelCase :							str								=				common_inputs['input_ids'].shape
         # Not using the same length for past_key_values
         UpperCAmelCase :							str								=				seqlen + 2
         UpperCAmelCase :							int								=				(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         UpperCAmelCase :							List[str]								=				[
             (torch.zeros(SCREAMING_SNAKE_CASE_		), torch.zeros(SCREAMING_SNAKE_CASE_		)) for _ in range(self.num_layers		)
         ]

     UpperCAmelCase :							int								=				common_inputs['attention_mask']
     if self.use_past:
       UpperCAmelCase :							int								=				ordered_inputs['attention_mask'].dtype
       UpperCAmelCase :							Optional[Any]								=				torch.cat(
           [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dtype=SCREAMING_SNAKE_CASE_		)],	dim=1		)

     return ordered_inputs





   @property
   def  __magic_name__					(							self							:  Optional[Any]		):
     return 1_3








def     a__  (	UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    List[Any]    )						->		Optional[int]:
  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							:    Tuple       ,				UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    Optional[int]    )						->		Any:
  UpperCAmelCase :							Union[str, Any]								=				float('''inf'''    )
  UpperCAmelCase :							str								=				-1
  for i in range(UpperCAmelCase    ):
    if (not vset[i]) and mdist[i] < min_val:
      UpperCAmelCase :							int								=				i
      UpperCAmelCase :							Union[str, Any]								=				mdist[i]
  return min_ind

def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Optional[Any]       ,				UpperCAmelCase							:    str    )						->		Tuple:
  UpperCAmelCase :							str								=				[float('''inf'''    ) for _ in range(UpperCAmelCase    )]
  UpperCAmelCase :							Optional[int]								=				[False for _ in range(UpperCAmelCase    )]
  UpperCAmelCase :							List[Any]								=				0.0

  for _ in range(v - 1    ):
    UpperCAmelCase :							Any								=				min_dist(UpperCAmelCase       ,				UpperCAmelCase       ,				UpperCAmelCase    )
    UpperCAmelCase :							List[Any]								=				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 :							str								=				mdist[u] + graph[u][i]

  print_dist(UpperCAmelCase       ,				UpperCAmelCase    )


if __name__ == "__main__":
     _lowerCamelCase       :     Tuple           =							int(input("Enter number of vertices: ").strip())
     _lowerCamelCase       :     List[str]           =							int(input("Enter number of edges: ").strip())

     _lowerCamelCase       :     Optional[int]           =							[[float("inf") for i in range(V)] for j in range(V)]

     for i in range(V):
          _lowerCamelCase       :     Union[str, Any]           =							0.0

     for i in range(E):
          print("\nEdge ", i + 1)
          _lowerCamelCase       :     List[str]           =							int(input("Enter source:").strip())
          _lowerCamelCase       :     List[str]           =							int(input("Enter destination:").strip())
          _lowerCamelCase       :     List[Any]           =							float(input("Enter weight:").strip())
          _lowerCamelCase       :     Any           =							weight

     _lowerCamelCase       :     List[str]           =							int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)





