


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     ):


     def __init__(  self							,					A_							,					A_=13							,					A_=7							,					A_=True							,					A_=True							,					A_=True							,					A_=True							,					A_=99							,					A_=32							,					A_=5							,					A_=4							,					A_=37							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=16							,					A_=2							,					A_=0.0_2							,					A_=4							,					):
       _UpperCAmelCase		:							Union[str, Any]							=     parent
       _UpperCAmelCase		:							List[str]							=     batch_size
       _UpperCAmelCase		:							int							=     seq_length
       _UpperCAmelCase		:							int							=     is_training
       _UpperCAmelCase		:							List[str]							=     use_attention_mask
       _UpperCAmelCase		:							Tuple							=     use_token_type_ids
       _UpperCAmelCase		:							Dict							=     use_labels
       _UpperCAmelCase		:							List[Any]							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							List[str]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Tuple							=     intermediate_size
       _UpperCAmelCase		:							List[str]							=     hidden_act
       _UpperCAmelCase		:							List[str]							=     hidden_dropout_prob
       _UpperCAmelCase		:							Tuple							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Union[str, Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[int]							=     type_vocab_size
       _UpperCAmelCase		:							str							=     type_sequence_label_size
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Any							=     num_choices


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Dict							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[str]							=     None
       if self.use_attention_mask:
         _UpperCAmelCase		:							int							=     random_attention_mask([self.batch_size, self.seq_length]    )

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

       _UpperCAmelCase		:							Optional[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=A_							,					initializer_range=self.initializer_range							,					)

       return config, input_ids, token_type_ids, attention_mask


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							str							=     self.prepare_config_and_inputs()
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							int							=     config_and_inputs
       _UpperCAmelCase		:							Tuple							=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     self.prepare_config_and_inputs()
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							str							=     config_and_inputs

       _UpperCAmelCase		:							Optional[int]							=     True
       _UpperCAmelCase		:							Optional[Any]							=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
       _UpperCAmelCase		:							Optional[Any]							=     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     ):
     __SCREAMING_SNAKE_CASE     					=    True

     __SCREAMING_SNAKE_CASE     					=    (
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     FlaxBertModelTester(self    )




     @slow
     def        __snake_case(  self    ):
       # Only check this for base model, not necessary for all model classes.
       # This will also help speed-up tests.
       _UpperCAmelCase		:							str							=     FlaxBertModel.from_pretrained("""bert-base-cased"""    )
       _UpperCAmelCase		:							List[Any]							=     model(np.ones((1, 1)    )    )
       self.assertIsNotNone(A_    )









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.




def    a__    (		snake_case__					:							Dict     ):
  _UpperCAmelCase		:							str							=     [False] * len(snake_case__     )
  _UpperCAmelCase		:							str							=     [-1] * len(snake_case__     )

  def dfs(snake_case__					:							Dict						,   snake_case__					:							Optional[Any]     ):
    _UpperCAmelCase		:							str							=     True
    _UpperCAmelCase		:							Optional[Any]							=     c
    for u in graph[v]:
      if not visited[u]:
        dfs(snake_case__						,   1 - c     )

  for i in range(len(snake_case__     )     ):
    if not visited[i]:
      dfs(snake_case__						,   0     )

  for i in range(len(snake_case__     )     ):
    for j in graph[i]:
      if color[i] == color[j]:
        return False

  return True


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))





