



"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							int=0						)   ->	int:  # a graph with Node 0,1,...,N-1
											snake_case_ :List[str]						=			n
											snake_case_ :int						=			[
											    [math.inf for j in range(0						,     snake_case						)] for i in range(0						,     snake_case						)
											]  # adjacency matrix for weight
											snake_case_ :str						=			[
											    [math.inf for j in range(0						,     snake_case						)] for i in range(0						,     snake_case						)
											]  # dp[i][j] stores minimum distance from i to j






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						,     snake_case:							Optional[Any]						,     snake_case:							str						)   ->	Tuple:
											snake_case_ :List[Any]						=			w






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	str:
											for k in range(0						,     self.n						):
																	for i in range(0						,     self.n						):
																							for j in range(0						,     self.n						):
																													snake_case_ :Any						=			min(self.dp[i][j]						,     self.dp[i][k] + self.dp[k][j]						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							List[Any]						,     snake_case:							Optional[Any]						)   ->	Union[str, Any]:
											return self.dp[u][v]


if __name__ == "__main__":
						__a      =				Graph(5)
						graph.add_edge(0, 2, 9)
						graph.add_edge(0, 4, 10)
						graph.add_edge(1, 3, 5)
						graph.add_edge(2, 3, 7)
						graph.add_edge(3, 0, 10)
						graph.add_edge(3, 1, 2)
						graph.add_edge(3, 2, 1)
						graph.add_edge(3, 4, 6)
						graph.add_edge(4, 1, 3)
						graph.add_edge(4, 2, 4)
						graph.add_edge(4, 3, 9)
						graph.floyd_warshall()
						graph.show_min(1, 4)
						graph.show_min(0, 3)

import unittest

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax

      from transformers.models.big_bird.modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
      )



class   snake_case_       (		unittest.TestCase       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     List[Any]	,						lowercase_				:     Union[str, Any]=2	,						lowercase_				:     Union[str, Any]=56	,						lowercase_				:     Tuple=True	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     int=True	,						lowercase_				:     Any=99	,						lowercase_				:     int=32	,						lowercase_				:     str=2	,						lowercase_				:     Union[str, Any]=2	,						lowercase_				:     Dict=7	,						lowercase_				:     Dict="gelu_new"	,						lowercase_				:     Tuple=0.1	,						lowercase_				:     List[Any]=0.1	,						lowercase_				:     Tuple=5_12	,						lowercase_				:     Optional[Any]=16	,						lowercase_				:     List[Any]=2	,						lowercase_				:     Dict=0.02	,						lowercase_				:     int=4	,						lowercase_				:     Tuple="block_sparse"	,						lowercase_				:     Dict=True	,						lowercase_				:     Optional[int]=False	,						lowercase_				:     Dict=2	,						lowercase_				:     int=3	,						)					->						Union[str, Any]:
       lowercase__ :   Dict    					=				parent
       lowercase__ :   Dict    					=				batch_size
       lowercase__ :   Tuple    					=				seq_length
       lowercase__ :   Dict    					=				is_training
       lowercase__ :   Dict    					=				use_attention_mask
       lowercase__ :   Tuple    					=				use_token_type_ids
       lowercase__ :   Optional[int]    					=				use_labels
       lowercase__ :   List[Any]    					=				vocab_size
       lowercase__ :   Any    					=				hidden_size
       lowercase__ :   List[Any]    					=				num_hidden_layers
       lowercase__ :   Union[str, Any]    					=				num_attention_heads
       lowercase__ :   str    					=				intermediate_size
       lowercase__ :   int    					=				hidden_act
       lowercase__ :   str    					=				hidden_dropout_prob
       lowercase__ :   List[str]    					=				attention_probs_dropout_prob
       lowercase__ :   Optional[Any]    					=				max_position_embeddings
       lowercase__ :   Union[str, Any]    					=				type_vocab_size
       lowercase__ :   Dict    					=				type_sequence_label_size
       lowercase__ :   Any    					=				initializer_range
       lowercase__ :   List[str]    					=				num_choices

       lowercase__ :   str    					=				rescale_embeddings
       lowercase__ :   Optional[Any]    					=				attention_type
       lowercase__ :   Optional[int]    					=				use_bias
       lowercase__ :   Optional[int]    					=				block_size
       lowercase__ :   str    					=				num_random_blocks






   def       __UpperCamelCase     (      self				:     str  )					->						Optional[Any]:
       lowercase__ :   List[Any]    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size  )

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

       lowercase__ :   Optional[int]    					=				None
       if self.use_token_type_ids:
           lowercase__ :   Any    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.type_vocab_size  )

       lowercase__ :   int    					=				BigBirdConfig(
           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=lowercase_	,						initializer_range=self.initializer_range	,						attention_type=self.attention_type	,						block_size=self.block_size	,						num_random_blocks=self.num_random_blocks	,						use_bias=self.use_bias	,						rescale_embeddings=self.rescale_embeddings	,						)

       return config, input_ids, token_type_ids, attention_mask







   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						int:
       lowercase__ :   int    					=				self.prepare_config_and_inputs()
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__ :   Dict    					=				config_and_inputs
       lowercase__ :   Union[str, Any]    					=				{
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
           "attention_mask": attention_mask,
       }
       return config, inputs_dict



@require_flax
class   snake_case_       (		__A  ,unittest.TestCase       ):
   __A	:		Optional[int]         =				(
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   __A	:		List[str]         =				False
   __A	:		Any         =				False






   def       __UpperCamelCase     (      self				:     List[str]  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				FlaxBigBirdModelTester(self  )






   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Dict:
       super().test_from_pretrained_save_pretrained()






   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Any:
       super().test_from_pretrained_with_no_automatic_init()






   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __UpperCamelCase     (      self				:     Tuple  )					->						str:
       super().test_no_automatic_init()






   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __UpperCamelCase     (      self				:     Dict  )					->						Union[str, Any]:
       super().test_hidden_states_output()






   @slow
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       for model_class_name in self.all_model_classes:
           lowercase__ :   Optional[Any]    					=				model_class_name.from_pretrained("google/bigbird-roberta-base"  )
           self.assertIsNotNone(lowercase_  )






   def       __UpperCamelCase     (      self				:     int  )					->						Optional[int]:
       if self.test_attn_probs:
           super().test_attention_outputs()






   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __UpperCamelCase     (      self				:     str  )					->						Any:
       lowercase__					,   lowercase__ :   Dict    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
               lowercase__ :   Union[str, Any]    					=				self._prepare_for_class(lowercase_	,						lowercase_  )
               lowercase__ :   Optional[Any]    					=				model_class(lowercase_  )

               @jax.jit
               def model_jitted(lowercase_				:     Tuple	,						lowercase_				:     int=None	,						**lowercase_				:     Dict  ):
                   return model(input_ids=lowercase_	,						attention_mask=lowercase_	,						**lowercase_  )

               with self.subTest("JIT Enabled"  ):
                   lowercase__ :   int    					=				model_jitted(**lowercase_  ).to_tuple()

               with self.subTest("JIT Disabled"  ):
                   with jax.disable_jit():
                       lowercase__ :   Any    					=				model_jitted(**lowercase_  ).to_tuple()

               self.assertEqual(len(lowercase_  )	,						len(lowercase_  )  )
               for jitted_output, output in zip(lowercase_	,						lowercase_  ):
                   self.assertEqual(jitted_output.shape	,						output.shape  )







   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     str	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Optional[int]	,						lowercase_				:     List[Any]=1E-5	,						lowercase_				:     Any="outputs"	,						lowercase_				:     List[str]=None  )					->						List[Any]:
       # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
       # an effort was done to return `attention_probs` (yet to be verified).
       if name.startswith("outputs.attentions"  ):
           return
       else:
           super().check_pt_flax_outputs(lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )



