
'''simple docstring'''







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 UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def __init__(		self      :     Any							,_a      :     Optional[int]							,_a      :     Dict=2							,_a      :     List[str]=56							,_a      :     Tuple=True							,_a      :     Optional[Any]=True							,_a      :     str=True							,_a      :     Union[str, Any]=True							,_a      :     List[str]=99							,_a      :     Any=32							,_a      :     Tuple=2							,_a      :     Optional[Any]=2							,_a      :     str=7							,_a      :     Any="gelu_new"							,_a      :     Tuple=0.1							,_a      :     List[Any]=0.1							,_a      :     Optional[int]=512							,_a      :     List[str]=16							,_a      :     Optional[Any]=2							,_a      :     Dict=0.02							,_a      :     List[Any]=4							,_a      :     str="block_sparse"							,_a      :     Optional[Any]=True							,_a      :     List[str]=False							,_a      :     Dict=2							,_a      :     Optional[Any]=3							,):


     '''simple docstring'''






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

     _a				:	Union[str, Any]			=   rescale_embeddings
     _a				:	Dict			=   attention_type
     _a				:	Any			=   use_bias
     _a				:	Union[str, Any]			=   block_size
     _a				:	List[Any]			=   num_random_blocks


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	List[str]			=   ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size       )

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

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

     _a				:	Optional[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=_a							,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   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Dict			=   self.prepare_config_and_inputs()
     _a,  _a,  _a,  _a				:	List[str]			=   config_and_inputs
     _a				:	str			=   {
         'input_ids': input_ids,
         'token_type_ids': token_type_ids,
         'attention_mask': attention_mask,
     }
     return config, inputs_dict







@require_flax
class UpperCAmelCase__	( lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Union[str, Any]   =					(
      (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdModel,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
      )
      if is_flax_available()
      else ()
  )

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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Dict			=   FlaxBigBirdModelTester(self       )


  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     super().test_from_pretrained_save_pretrained()


  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     super().test_from_pretrained_with_no_automatic_init()


  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     super().test_no_automatic_init()


  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     super().test_hidden_states_output()


  @slow
  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     for model_class_name in self.all_model_classes:
        _a				:	Optional[Any]			=   model_class_name.from_pretrained('google/bigbird-roberta-base'       )
        self.assertIsNotNone(_a       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     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   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a,  _a				:	Optional[int]			=   self.model_tester.prepare_config_and_inputs_for_common()

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

           @jax.jit
           def model_jitted(_a      :     int							,_a      :     Union[str, Any]=None							,**_a      :     Any       ):
              return model(input_ids=_a							,attention_mask=_a							,**_a       )

           with self.subTest('JIT Enabled'       ):
              _a				:	List[str]			=   model_jitted(**_a       ).to_tuple()

           with self.subTest('JIT Disabled'       ):
              with jax.disable_jit():
                 _a				:	List[str]			=   model_jitted(**_a       ).to_tuple()

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


  def   __lowercase		(		self      :     Optional[int]							,_a      :     List[Any]							,_a      :     Any							,_a      :     str							,_a      :     List[Any]=1E-5							,_a      :     Any="outputs"							,_a      :     Optional[Any]=None       ):


     '''simple docstring'''






     if name.startswith('outputs.attentions'       ):
        return
     else:
        super().check_pt_flax_outputs(_a							,_a							,_a							,_a							,_a							,_a       )





'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



