from __future__ import annotations

import os
from collections.abc import Mapping

UpperCamelCase__											=					tuple[int, int]





class      A							:


   def __init__(self     :						str       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[Any]		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  vertices
         UpperCAmelCase__								=  {
             (min(_UpperCAmelCase		), max(_UpperCAmelCase		)): weight for edge, weight in edges.items()
         }


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						str		)						->				Union[str, Any]:
         """simple docstring"""



         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         UpperCAmelCase__								=  weight





   def 			lowercase_      (self     :						Optional[int]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  Graph({min(self.vertices		)}       ,  {}		)
         UpperCAmelCase__								=  42
         UpperCAmelCase__								=  42
         UpperCAmelCase__								=  42
         UpperCAmelCase__								=  42

         while len(subgraph.vertices		) < len(self.vertices		):
               UpperCAmelCase__								=  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:
                                 UpperCAmelCase__								=  edge
                                 UpperCAmelCase__								=  weight

               subgraph.add_edge(_UpperCAmelCase       ,  _UpperCAmelCase		)

         return subgraph




def    lowerCAmelCase_ (     __A = "p107_network.txt"							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  os.path.abspath(os.path.dirname(__UpperCAmelCase							)							)
      UpperCAmelCase__								=  os.path.join(__UpperCAmelCase,  __UpperCAmelCase							)
      UpperCAmelCase__								=  {}
      UpperCAmelCase__								=  42
      UpperCAmelCase__								=  42
      UpperCAmelCase__								=  42

      with open(__UpperCAmelCase							) as f:
            UpperCAmelCase__								=  f.read().strip().split("\n"							)

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

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

      UpperCAmelCase__								=  Graph(set(range(len(__UpperCAmelCase							)							)							),  __UpperCAmelCase							)

      UpperCAmelCase__								=  graph.prims_algorithm()

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

      return initial_total - optimal_total


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




"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = MBartConfig
    _UpperCAmelCase	:str						    = {}
    _UpperCAmelCase	:Union[str, Any]						    = "gelu"






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=20    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=0    ,			):
       lowercase__:   List[str]					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   List[Any]					=      seq_length
       lowercase__:   str					=      is_training
       lowercase__:   List[str]					=      use_labels
       lowercase__:   Optional[int]					=      vocab_size
       lowercase__:   int					=      hidden_size
       lowercase__:   Optional[int]					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Tuple					=      intermediate_size
       lowercase__:   Optional[Any]					=      hidden_dropout_prob
       lowercase__:   Optional[int]					=      attention_probs_dropout_prob
       lowercase__:   str					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      eos_token_id
       lowercase__:   int					=      pad_token_id
       lowercase__:   List[str]					=      bos_token_id






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size							)
       lowercase__:   str					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)    ,			1							)
       lowercase__:   List[Any]					=      tf.concat([input_ids, eos_tensor]    ,			axis=1							)

       lowercase__:   List[Any]					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   Optional[Any]					=      self.config_cls(
           vocab_size=self.vocab_size    ,			d_model=self.hidden_size    ,			encoder_layers=self.num_hidden_layers    ,			decoder_layers=self.num_hidden_layers    ,			encoder_attention_heads=self.num_attention_heads    ,			decoder_attention_heads=self.num_attention_heads    ,			encoder_ffn_dim=self.intermediate_size    ,			decoder_ffn_dim=self.intermediate_size    ,			dropout=self.hidden_dropout_prob    ,			attention_dropout=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			eos_token_ids=[2]    ,			bos_token_id=self.bos_token_id    ,			pad_token_id=self.pad_token_id    ,			decoder_start_token_id=self.pad_token_id    ,			**self.config_updates    ,			)
       lowercase__:   Optional[int]					=      prepare_mbart_inputs_dict(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       return config, inputs_dict






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      TFMBartModel(config=_UpperCAmelCase							).get_decoder()
       lowercase__:   Tuple					=      inputs_dict['''input_ids''']

       lowercase__:   Optional[Any]					=      input_ids[:1, :]
       lowercase__:   Optional[int]					=      inputs_dict['''attention_mask'''][:1, :]
       lowercase__:   List[str]					=      inputs_dict['''head_mask''']
       lowercase__:   Optional[int]					=      1

       # first forward pass
       lowercase__:   List[Any]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			use_cache=_UpperCAmelCase							)

       lowercase__, lowercase__:   Any					=      outputs.to_tuple()
       lowercase__:   List[str]					=      past_key_values[1]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    )  ->  int:
   if attention_mask is None:
      lowercase__:   Union[str, Any]					=      tf.cast(tf.math.not_equal(__UpperCAmelCase       ,    config.pad_token_id )       ,    tf.inta )
   if decoder_attention_mask is None:
      lowercase__:   List[str]					=      tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape       ,    dtype=tf.inta ),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,    config.pad_token_id )       ,    tf.inta ),
          ]       ,    axis=-1       ,    )
   if head_mask is None:
      lowercase__:   str					=      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
   if decoder_head_mask is None:
      lowercase__:   List[str]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   if cross_attn_head_mask is None:
      lowercase__:   Union[str, Any]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    _UpperCAmelCase	:List[Any]						    = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    _UpperCAmelCase	:List[str]						    = (
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    _UpperCAmelCase	:str						    = True
    _UpperCAmelCase	:List[Any]						    = False
    _UpperCAmelCase	:str						    = False






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
          # Exception encountered when calling layer '...'
          return True

       return False






    def _snake_case			(   self							):
       lowercase__:   Tuple					=      TFMBartModelTester(self							)
       lowercase__:   Optional[Any]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase							)



@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = [
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    _UpperCAmelCase	:Any						    = [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    _UpperCAmelCase	:Tuple						    = "facebook/mbart-large-en-ro"






    @cached_property
    def _snake_case			(   self							):
       return AutoTokenizer.from_pretrained(self.model_name							)






    @cached_property
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       return model






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      self.translate_src_text(**_UpperCAmelCase							)
       self.assertListEqual(self.expected_text    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   str					=      self.tokenizer(self.src_text    ,			**_UpperCAmelCase    ,			return_tensors='''tf'''							)
       lowercase__:   Optional[int]					=      self.model.generate(
           model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2							)
       lowercase__:   Tuple					=      self.tokenizer.batch_decode(_UpperCAmelCase    ,			skip_special_tokens=_UpperCAmelCase							)
       return generated_words






    @slow
    def _snake_case			(   self							):
       self._assert_generated_batch_equal_expected()
