import unittest

import numpy as np

from transformers import AlbertConfig, 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.numpy as jnp

   from transformers.models.albert.modeling_flax_albert import (
       FlaxAlbertForMaskedLM,
       FlaxAlbertForMultipleChoice,
       FlaxAlbertForPreTraining,
       FlaxAlbertForQuestionAnswering,
       FlaxAlbertForSequenceClassification,
       FlaxAlbertForTokenClassification,
       FlaxAlbertModel,
   )







class __snake_case    (						unittest.TestCase		):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				seq_length
     SCREAMING_SNAKE_CASE__      							=				is_training
     SCREAMING_SNAKE_CASE__      							=				use_attention_mask
     SCREAMING_SNAKE_CASE__      							=				use_token_type_ids
     SCREAMING_SNAKE_CASE__      							=				use_labels
     SCREAMING_SNAKE_CASE__      							=				vocab_size
     SCREAMING_SNAKE_CASE__      							=				hidden_size
     SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
     SCREAMING_SNAKE_CASE__      							=				num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				intermediate_size
     SCREAMING_SNAKE_CASE__      							=				hidden_act
     SCREAMING_SNAKE_CASE__      							=				hidden_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				max_position_embeddings
     SCREAMING_SNAKE_CASE__      							=				type_vocab_size
     SCREAMING_SNAKE_CASE__      							=				type_sequence_label_size
     SCREAMING_SNAKE_CASE__      							=				initializer_range
     SCREAMING_SNAKE_CASE__      							=				num_choices




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.seq_length]	,     self.vocab_size				)

     SCREAMING_SNAKE_CASE__      							=				None
     if self.use_attention_mask:
       SCREAMING_SNAKE_CASE__      							=				random_attention_mask([self.batch_size, self.seq_length]				)

     SCREAMING_SNAKE_CASE__      							=				None
     if self.use_token_type_ids:
       SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.seq_length]	,     self.type_vocab_size				)

     SCREAMING_SNAKE_CASE__      							=				AlbertConfig(
         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	,     )

     return config, input_ids, token_type_ids, attention_mask




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				config_and_inputs
     SCREAMING_SNAKE_CASE__      							=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
     return config, inputs_dict







@require_flax
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							(
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				FlaxAlbertModelTester(self				)




   @slow
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE__      							=				model_class_name.from_pretrained("""albert-base-v2"""				)
       SCREAMING_SNAKE_CASE__      							=				model(np.ones((1, 1)				)				)
       self.assertIsNotNone(_lowercase				)







@require_flax
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				FlaxAlbertModel.from_pretrained("""albert-base-v2"""				)
     SCREAMING_SNAKE_CASE__      							=				np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]				)
     SCREAMING_SNAKE_CASE__      							=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     attention_mask=_lowercase				)[0]
     SCREAMING_SNAKE_CASE__      							=				(1, 11, 7_68)
     self.assertEqual(output.shape	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				np.array(
         [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]				)

     self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]	,     _lowercase	,     atol=1E-4				)				)

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[0] * len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[1] * len(__UpperCamelCase     )

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(__UpperCamelCase     )     ):
    if indegree[i] == 0:
      queue.append(__UpperCamelCase     )

  while queue:
    SCREAMING_SNAKE_CASE__      							=				queue.pop(0     )
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        SCREAMING_SNAKE_CASE__      							=				long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(__UpperCamelCase     )

  print(max(__UpperCamelCase     )     )


# Adjacency list of Graph
__lowerCamelCase      :			int							       =		{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)
