





'''simple docstring'''




from typing import Mapping

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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "umt5"
     snake_case_						      =   ["past_key_values"]



     def __init__(   self							:			List[Any]   ,       __snake_case							:			Any=25_01_12   ,       __snake_case							:			Union[str, Any]=5_12   ,       __snake_case							:			Dict=64   ,       __snake_case							:			Union[str, Any]=10_24   ,       __snake_case							:			List[Any]=8   ,       __snake_case							:			Optional[int]=None   ,       __snake_case							:			List[str]=6   ,       __snake_case							:			List[Any]=32   ,       __snake_case							:			int=1_28   ,       __snake_case							:			Optional[int]=0.1   ,       __snake_case							:			Optional[int]=1e-6   ,       __snake_case							:			Dict=1.0   ,       __snake_case							:			Union[str, Any]="gated-gelu"   ,       __snake_case							:			Optional[int]=True   ,       __snake_case							:			int=True   ,       __snake_case							:			List[str]="T5Tokenizer"   ,       __snake_case							:			int=True   ,       __snake_case							:			Optional[int]=0   ,       __snake_case							:			Optional[int]=1   ,       __snake_case							:			Optional[int]=0   ,       **__snake_case							:			Any   ,       )->	Any:
        super().__init__(
            is_encoder_decoder=__snake_case   ,       tokenizer_class=__snake_case   ,       tie_word_embeddings=__snake_case   ,       pad_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       decoder_start_token_id=__snake_case   ,       **__snake_case   ,       )
        snake_case 						=   vocab_size
        snake_case 						=   d_model
        snake_case 						=   d_kv
        snake_case 						=   d_ff
        snake_case 						=   num_layers
        snake_case 						=   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        snake_case 						=   num_heads
        snake_case 						=   relative_attention_num_buckets
        snake_case 						=   relative_attention_max_distance
        snake_case 						=   dropout_rate
        snake_case 						=   layer_norm_epsilon
        snake_case 						=   initializer_factor
        snake_case 						=   feed_forward_proj
        snake_case 						=   use_cache

        snake_case 						=   self.feed_forward_proj.split("""-"""						)
        snake_case 						=   act_info[-1]
        snake_case 						=   act_info[0] == """gated"""

        if len(__snake_case						) > 1 and act_info[0] != "gated" or len(__snake_case						) > 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'"""						)

        if feed_forward_proj == "gated-gelu":
           snake_case 						=   """gelu_new"""



     @property
     def      lowerCAmelCase			(   self							:			Any						)->	Tuple:
        return self.d_model



     @property
     def      lowerCAmelCase			(   self							:			str						)->	Union[str, Any]:
        return self.num_heads



     @property
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Optional[int]:
        return self.num_layers




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






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

        if self.use_past:
           self.fill_with_past_key_values_(__snake_case   ,       direction="""inputs"""						)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def      lowerCAmelCase			(   self							:			Tuple						)->	int:
        return 13



     @property
     def      lowerCAmelCase			(   self							:			Any						)->	float:
        return 5e-4








'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			set[int]   ,       __snake_case							:			Mapping[EdgeT, int]						)->	None:
        snake_case 						=   vertices
        snake_case 						=   {
            (min(__snake_case						), max(__snake_case						)): weight for edge, weight in edges.items()
        }



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	Graph:
        snake_case 						=   Graph({min(self.vertices						)}   ,       {}						)
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42

        while len(subgraph.vertices						) < len(self.vertices						):
           snake_case 						=   max(self.edges.values()						) + 1
           for edge, weight in self.edges.items():
              if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                 if weight < min_weight:
                    snake_case 						=   edge
                    snake_case 						=   weight

           subgraph.add_edge(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [line.split(""","""		) for line in data]

   for edgea in range(1      ,    len(__lowerCAmelCase		)		):
      for edgea in range(__lowerCAmelCase		):
         if adjaceny_matrix[edgea][edgea] != "-":
            snake_case 						=   int(adjaceny_matrix[edgea][edgea]		)

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   snake_case 						=   graph.prims_algorithm()

   snake_case 						=   sum(graph.edges.values()		)
   snake_case 						=   sum(subgraph.edges.values()		)

   return initial_total - optimal_total


if __name__ == "__main__":
 print(F"""{solution() = }""")

