




'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
    )

class 	_snake_case(			unittest.TestCase      ):






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






    """simple docstring"""


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






 def 						_UpperCamelCase (self       :  Dict    )       ->   int:






    """simple docstring"""


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

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

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

    A__       =     RoFormerConfig(
        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=lowerCamelCase_    ,		initializer_range=self.initializer_range    ,		)

    return config, input_ids, token_type_ids, attention_mask






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Optional[Any]:






    """simple docstring"""


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




@require_flax
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							List[str]						=					True

 __snake_case:							str						=					(
     (
         FlaxRoFormerModel,
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    A__       =     FlaxRoFormerModelTester(self    )






 @slow
 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Optional[int]:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__       =     model_class_name.from_pretrained('junnyu/roformer_chinese_small'    ,		from_pt=lowerCamelCase_    )
       A__       =     model(np.ones((1, 1)    )    )
       self.assertIsNotNone(lowerCamelCase_    )




@require_flax
class 	_snake_case(			unittest.TestCase      ):






 @slow
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
    A__       =     jnp.array([[0, 1, 2, 3, 4, 5]]    )
    A__       =     model(lowerCamelCase_    )[0]

    A__       =     5_00_00

    A__       =     (1, 6, vocab_size)
    self.assertEqual(output.shape    ,		lowerCamelCase_    )

    A__       =     jnp.array(
        [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]    )

    self.assertTrue(jnp.allclose(output[:, :3, :3]    ,		lowerCamelCase_    ,		atol=1e-4    )    )





"""simple docstring"""


UpperCAmelCase													=[
    [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],
]



def   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

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

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		[-1] * (len(_a				))
      A				  =		0
      A				  =		[]
      A				  =		[i[:] for i in graph]  # Record original cut, copy.
      while bfs(_a     ,   _a     ,   _a     ,   _a				):
            A				  =		float("""Inf"""				)
            A				  =		sink

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

            max_flow += path_flow
            A				  =		sink

            while v != source:
                  A				  =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  A				  =		parent[v]

      for i in range(len(_a				)				):
            for j in range(len(graph[0]				)				):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)				)

      return res


if __name__ == "__main__":
  print(mincut(test_graph, source=0, sink=5))




