






'''simple docstring'''

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 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def __init__(					self      : Optional[int]	,    A__      : Optional[int]	,    A__      : Any=13	,    A__      : int=7	,    A__      : Optional[int]=True	,    A__      : List[Any]=True	,    A__      : List[Any]=True	,    A__      : int=True	,    A__      : Union[str, Any]=99	,    A__      : Union[str, Any]=32	,    A__      : Optional[int]=5	,    A__      : Any=4	,    A__      : str=37	,    A__      : List[Any]="gelu"	,    A__      : Optional[int]=0.1	,    A__      : Optional[int]=0.1	,    A__      : Any=512	,    A__      : Tuple=16	,    A__      : List[str]=2	,    A__      : Dict=0.02	,    A__      : int=4	,    ):


            """simple docstring"""







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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size					)

            __lowerCamelCase						:       Optional[Any]				   =				None
            if self.use_attention_mask:
                 __lowerCamelCase						:       Dict				   =				random_attention_mask([self.batch_size, self.seq_length]					)

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

            __lowerCamelCase						:       Dict				   =				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=A__	,    initializer_range=self.initializer_range	,    )

            return config, input_ids, token_type_ids, attention_mask




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.prepare_config_and_inputs()
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				config_and_inputs
            __lowerCamelCase						:       Union[str, Any]				   =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict







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




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				FlaxAlbertModelTester(self					)




       @slow
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowerCamelCase						:       Dict				   =				model_class_name.from_pretrained("""albert-base-v2"""					)
                 __lowerCamelCase						:       Union[str, Any]				   =				model(np.ones((1, 1)					)					)
                 self.assertIsNotNone(A__					)







@require_flax
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       @slow
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				FlaxAlbertModel.from_pretrained("""albert-base-v2"""					)
            __lowerCamelCase						:       int				   =				np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
            __lowerCamelCase						:       Dict				   =				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
            __lowerCamelCase						:       List[Any]				   =				model(A__	,    attention_mask=A__					)[0]
            __lowerCamelCase						:       List[Any]				   =				(1, 11, 768)
            self.assertEqual(output.shape	,    A__					)
            __lowerCamelCase						:       Dict				   =				np.array(
                [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]					)

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











'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

     while queue:
          __lowerCamelCase						:       Optional[Any]				   =				queue.pop(0     )
          for ind in range(len(graph[u]     )     ):
               if visited[ind] is False and graph[u][ind] > 0:
                    queue.append(_lowercase     )
                    __lowerCamelCase						:       List[str]				   =				True
                    __lowerCamelCase						:       str				   =				u

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

          while s != source:
               # Find the minimum value in select path
               __lowerCamelCase						:       Optional[int]				   =				min(_lowercase,						graph[parent[s]][s]     )
               __lowerCamelCase						:       Union[str, Any]				   =				parent[s]

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

          while v != source:
               __lowerCamelCase						:       List[Any]				   =				parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase						:       Dict				   =				parent[v]
     return max_flow


UpperCAmelCase__						:str          =			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



