

import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )



class SCREAMING_SNAKE_CASE      (    unittest.TestCase					):
  """simple docstring"""






  def __init__(     self					:       str			,				lowerCAmelCase					:       List[Any]			,				lowerCAmelCase					:       str=13			,				lowerCAmelCase					:       List[str]=7			,				lowerCAmelCase					:       List[Any]=True			,				lowerCAmelCase					:       int=True			,				lowerCAmelCase					:       str=True			,				lowerCAmelCase					:       List[Any]=True			,				lowerCAmelCase					:       Union[str, Any]=99			,				lowerCAmelCase					:       str=32			,				lowerCAmelCase					:       int=5			,				lowerCAmelCase					:       List[str]=4			,				lowerCAmelCase					:       Any=37			,				lowerCAmelCase					:       Optional[Any]="gelu"			,				lowerCAmelCase					:       int=0.1			,				lowerCAmelCase					:       Optional[int]=0.1			,				lowerCAmelCase					:       List[str]=5_12			,				lowerCAmelCase					:       str=16			,				lowerCAmelCase					:       Union[str, Any]=2			,				lowerCAmelCase					:       List[Any]=0.02			,				lowerCAmelCase					:       Tuple=4			,				)				->      Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    :     Union[str, Any]          =		parent
     __lowerCAmelCase    :     int          =		batch_size
     __lowerCAmelCase    :     Optional[int]          =		seq_length
     __lowerCAmelCase    :     List[str]          =		is_training
     __lowerCAmelCase    :     Optional[int]          =		use_attention_mask
     __lowerCAmelCase    :     Optional[Any]          =		use_token_type_ids
     __lowerCAmelCase    :     Dict          =		use_labels
     __lowerCAmelCase    :     List[Any]          =		vocab_size
     __lowerCAmelCase    :     Any          =		hidden_size
     __lowerCAmelCase    :     Union[str, Any]          =		num_hidden_layers
     __lowerCAmelCase    :     int          =		num_attention_heads
     __lowerCAmelCase    :     List[str]          =		intermediate_size
     __lowerCAmelCase    :     List[str]          =		hidden_act
     __lowerCAmelCase    :     Optional[Any]          =		hidden_dropout_prob
     __lowerCAmelCase    :     List[str]          =		attention_probs_dropout_prob
     __lowerCAmelCase    :     str          =		max_position_embeddings
     __lowerCAmelCase    :     Dict          =		type_vocab_size
     __lowerCAmelCase    :     Optional[int]          =		type_sequence_label_size
     __lowerCAmelCase    :     Union[str, Any]          =		initializer_range
     __lowerCAmelCase    :     Any          =		num_choices







  def 			SCREAMING_SNAKE_CASE       (     self					:       Any       )				->      str:






     """simple docstring"""







     __lowerCAmelCase    :     Tuple          =		ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size       )

     __lowerCAmelCase    :     Any          =		None
     if self.use_attention_mask:
        __lowerCAmelCase    :     int          =		random_attention_mask([self.batch_size, self.seq_length]       )

     __lowerCAmelCase    :     Union[str, Any]          =		None
     if self.use_token_type_ids:
        __lowerCAmelCase    :     List[Any]          =		ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size       )

     __lowerCAmelCase    :     Union[str, Any]          =		BertConfig(
         vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				type_vocab_size=self.type_vocab_size			,				is_decoder=_snake_case			,				initializer_range=self.initializer_range			,				)

     return config, input_ids, token_type_ids, attention_mask







  def 			SCREAMING_SNAKE_CASE       (     self					:       str       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     Any          =		self.prepare_config_and_inputs()
     __lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase    :     Any          =		config_and_inputs
     __lowerCAmelCase    :     List[Any]          =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
     return config, inputs_dict







  def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      List[str]:






     """simple docstring"""







     __lowerCAmelCase    :     List[Any]          =		self.prepare_config_and_inputs()
     __lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase       ,__lowerCAmelCase    :     Optional[Any]          =		config_and_inputs

     __lowerCAmelCase    :     List[Any]          =		True
     __lowerCAmelCase    :     Union[str, Any]          =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
     __lowerCAmelCase    :     Optional[int]          =		ids_tensor([self.batch_size, self.seq_length]			,				vocab_size=2       )

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )






@require_flax
class SCREAMING_SNAKE_CASE      (    a__  ,				unittest.TestCase					):
  """simple docstring"""






  lowerCamelCase       :     Optional[int]		 =True

  lowerCamelCase       :     int		 =(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[int]       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[int]          =		FlaxBertModelTester(self       )







  @slow
  def 			SCREAMING_SNAKE_CASE       (     self					:       Dict       )				->      int:






     """simple docstring"""







     __lowerCAmelCase    :     int          =		FlaxBertModel.from_pretrained("""bert-base-cased"""       )
     __lowerCAmelCase    :     Optional[int]          =		model(np.ones((1, 1)       )       )
     self.assertIsNotNone(_snake_case       )









"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase     :						Optional[Any]						       =  tuple[int, int]







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

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

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

 lowerCAmelCase    				= [line.split(','			) for line in data]

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

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

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

 return initial_total - optimal_total


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





