


"""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   _a      (      unittest.TestCase ):
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=13							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=7							,					SCREAMING_SNAKE_CASE__							:					Tuple=True							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					int=True							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					int=99							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=32							,					SCREAMING_SNAKE_CASE__							:					List[str]=5							,					SCREAMING_SNAKE_CASE__							:					Dict=4							,					SCREAMING_SNAKE_CASE__							:					Any=37							,					SCREAMING_SNAKE_CASE__							:					str="gelu"							,					SCREAMING_SNAKE_CASE__							:					List[str]=0.1							,					SCREAMING_SNAKE_CASE__							:					Any=0.1							,					SCREAMING_SNAKE_CASE__							:					Dict=5_12							,					SCREAMING_SNAKE_CASE__							:					str=16							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=2							,					SCREAMING_SNAKE_CASE__							:					Dict=0.02							,					SCREAMING_SNAKE_CASE__							:					Any=4							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		seq_length
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_attention_mask
          lowerCamelCase__           =		use_token_type_ids
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		type_vocab_size
          lowerCamelCase__           =		type_sequence_label_size
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		num_choices
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)

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

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

          lowerCamelCase__           =		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=SCREAMING_SNAKE_CASE__							,					initializer_range=self.initializer_range							,					)

          return config, input_ids, token_type_ids, attention_mask


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





@require_flax
class   _a      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Any             =    (
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		FlaxAlbertModelTester(self				)


    @slow
    def      _UpperCamelCase       ( self							:					int				):
          for model_class_name in self.all_model_classes:
                lowerCamelCase__           =		model_class_name.from_pretrained('albert-base-v2'				)
                lowerCamelCase__           =		model(np.ones((1, 1)				)				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)





@require_flax
class   _a      (      unittest.TestCase ):
    @slow
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		FlaxAlbertModel.from_pretrained('albert-base-v2'				)
          lowerCamelCase__           =		np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]				)
          lowerCamelCase__           =		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__				)[0]
          lowerCamelCase__           =		(1, 11, 7_68)
          self.assertEqual(output.shape							,					SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		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]							,					SCREAMING_SNAKE_CASE__							,					atol=1e-4				)				)







"""simple docstring"""


def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     str		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[]
      queue.append(_a							)
      lowerCamelCase__           =		True

      while queue:
            lowerCamelCase__           =		queue.pop(0							)
            for ind in range(len(graph[u]							)							):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a							)
                        lowerCamelCase__           =		True
                        lowerCamelCase__           =		u

      return visited[t]



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[-1] * (len(_a							))
      lowerCamelCase__           =		0
      while bfs(_a		,   _a		,   _a		,   _a							):
            lowerCamelCase__           =		float('Inf'							)
            lowerCamelCase__           =		sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase__           =		min(_a		,   graph[parent[s]][s]							)
                  lowerCamelCase__           =		parent[s]

            max_flow += path_flow
            lowerCamelCase__           =		sink

            while v != source:
                  lowerCamelCase__           =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase__           =		parent[v]
      return max_flow


_snake_case          =							[
    [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],
]

_snake_case      ,					_snake_case          =							0, 5






print(ford_fulkerson(graph, source, sink))



