





'''simple docstring'''






import heapq as hq
import math
from collections.abc import Iterator
class 					__snake_case  :






   '''simple docstring'''





   def __init__(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[Any]    =  str(id_       )
    snake_case__     :					Dict    =  None
    snake_case__     :					List[Any]    =  None
    snake_case__     :					Optional[int]    =  []
    snake_case__     :					Tuple    =  {}  # {vertex:distance}

   def __lt__(							self	,		__SCREAMING_SNAKE_CASE       ):
    return self.key < other.key

   def __repr__(							self       ):
    return self.id

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    self.neighbors.append(__SCREAMING_SNAKE_CASE       )


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Tuple    =  weight



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]		,       __magic_name__  :   List[str]		,       __magic_name__  :   int		,       __magic_name__  :   Dict      )				->			Union[str, Any]:
 '''simple docstring'''






 graph[a - 1].add_neighbor(graph[b - 1]      )
 graph[b - 1].add_neighbor(graph[a - 1]      )
 # add the edges:
 graph[a - 1].add_edge(graph[b - 1]		,       __magic_name__      )
 graph[b - 1].add_edge(graph[a - 1]		,       __magic_name__      )



def        UpperCamelCase__		(						__magic_name__  :   list		,       __magic_name__  :   Vertex      )				->			list:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  []
 for u in graph:
  snake_case__     :					str    =  math.inf
  snake_case__     :					List[Any]    =  None
 snake_case__     :					Dict    =  0
 snake_case__     :					Tuple    =  graph[:]
 while q:
  snake_case__     :					Any    =  min(__magic_name__      )
  q.remove(__magic_name__      )
  for v in u.neighbors:
   if (v in q) and (u.edges[v.id] < v.key):
    snake_case__     :					Optional[int]    =  u
    snake_case__     :					Dict    =  u.edges[v.id]
 for i in range(1		,       len(__magic_name__      )      ):
  a.append((int(graph[i].id      ) + 1, int(graph[i].pi.id      ) + 1)      )
 return a



def        UpperCamelCase__		(						__magic_name__  :   list		,       __magic_name__  :   Vertex      )				->			Iterator[tuple]:
 '''simple docstring'''






 for u in graph:
  snake_case__     :					Tuple    =  math.inf
  snake_case__     :					Tuple    =  None
 snake_case__     :					Optional[int]    =  0

 snake_case__     :					str    =  list(__magic_name__      )
 hq.heapify(__magic_name__      )

 while h:
  snake_case__     :					str    =  hq.heappop(__magic_name__      )
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    snake_case__     :					Union[str, Any]    =  u
    snake_case__     :					Dict    =  u.edges[v.id]
    hq.heapify(__magic_name__      )

 for i in range(1		,       len(__magic_name__      )      ):
  yield (int(graph[i].id      ) + 1, int(graph[i].pi.id      ) + 1)



def        UpperCamelCase__		(						)				->			None:
 '''simple docstring'''








if __name__ == "__main__":
    import doctest

    doctest.testmod()





from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
					import tensorflow as tf

					from transformers import TFBlipTextModel
					from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class     lowerCAmelCase_     :







							def __init__(   self      :     Any ,			_A      :     int ,			_A      :     int=12 ,			_A      :     int=7 ,			_A      :     Tuple=True ,			_A      :     Optional[int]=True ,			_A      :     Union[str, Any]=True ,			_A      :     str=99 ,			_A      :     str=32 ,			_A      :     int=32 ,			_A      :     Optional[Any]=2 ,			_A      :     Dict=4 ,			_A      :     int=37 ,			_A      :     List[Any]=0.1 ,			_A      :     str=0.1 ,			_A      :     Any=512 ,			_A      :     int=0.02 ,			_A      :     Optional[Any]=0 ,			_A      :     Dict=None ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     seq_length
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_input_mask
												_UpperCamelCase			     =     use_labels
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     hidden_size
												_UpperCamelCase			     =     projection_dim
												_UpperCamelCase			     =     num_hidden_layers
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     intermediate_size
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     scope
												_UpperCamelCase			     =     bos_token_id







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

												_UpperCamelCase			     =     None
												if self.use_input_mask:
																	_UpperCamelCase			     =     random_attention_mask([self.batch_size, self.seq_length]		)

												if input_mask is not None:
																	_UpperCamelCase			     =     input_mask.numpy()
																	_UpperCamelCase     ,    _UpperCamelCase			     =     input_mask.shape
																	_UpperCamelCase			     =     np.random.randint(1 ,			seq_length - 1 ,			size=(batch_size,)		)
																	for batch_idx, start_index in enumerate(_A		):
																						_UpperCamelCase			     =     1
																						_UpperCamelCase			     =     0

												_UpperCamelCase			     =     self.get_config()

												return config, input_ids, tf.convert_to_tensor(_A		)







							def  UpperCamelCase_						(   self      :     str		):
												return BlipTextConfig(
												    vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			projection_dim=self.projection_dim ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			dropout=self.dropout ,			attention_dropout=self.attention_dropout ,			max_position_embeddings=self.max_position_embeddings ,			initializer_range=self.initializer_range ,			bos_token_id=self.bos_token_id ,			)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Tuple ,			_A      :     str ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     TFBlipTextModel(config=_A		)
												_UpperCamelCase			     =     model(_A ,			attention_mask=_A ,			training=_A		)
												_UpperCamelCase			     =     model(_A ,			training=_A		)
												self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)		)
												self.parent.assertEqual(result.pooler_output.shape ,			(self.batch_size, self.hidden_size)		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     config_and_inputs
												_UpperCamelCase			     =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict




@require_tf
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (TFBlipTextModel,) if is_tf_available() else ()
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     BlipTextModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			hidden_size=37		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												pass







							def  UpperCamelCase_						(   self      :     Tuple		):
												pass







							@unittest.skip(reason='''Blip does not use inputs_embeds'''		)
							def  UpperCamelCase_						(   self      :     Dict		):
												pass







							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''		)
							def  UpperCamelCase_						(   self      :     Dict		):
												pass







							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''		)
							def  UpperCamelCase_						(   self      :     List[str]		):
												pass







							@slow
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase			     =     TFBlipTextModel.from_pretrained(_A		)
																	self.assertIsNotNone(_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Optional[int]=True		):
												super().test_pt_tf_model_equivalence(allow_missing_keys=_A		)
