
'''simple docstring'''

import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        DebertaVaForMaskedLM,
        DebertaVaForMultipleChoice,
        DebertaVaForQuestionAnswering,
        DebertaVaForSequenceClassification,
        DebertaVaForTokenClassification,
        DebertaVaModel,
    )
    from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=7							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=99							,		lowerCAmelCase_=32							,		lowerCAmelCase_=5							,		lowerCAmelCase_=4							,		lowerCAmelCase_=37							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=16							,		lowerCAmelCase_=2							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_="None"							,		lowerCAmelCase_=3							,		lowerCAmelCase_=4							,		lowerCAmelCase_=None							,		):

       '''simple docstring'''

       a_      :	Tuple      =							parent
       a_      :	str      =							batch_size
       a_      :	Optional[Any]      =							seq_length
       a_      :	int      =							is_training
       a_      :	Tuple      =							use_input_mask
       a_      :	Optional[int]      =							use_token_type_ids
       a_      :	List[str]      =							use_labels
       a_      :	str      =							vocab_size
       a_      :	Any      =							hidden_size
       a_      :	str      =							num_hidden_layers
       a_      :	List[Any]      =							num_attention_heads
       a_      :	Union[str, Any]      =							intermediate_size
       a_      :	Dict      =							hidden_act
       a_      :	Union[str, Any]      =							hidden_dropout_prob
       a_      :	Tuple      =							attention_probs_dropout_prob
       a_      :	int      =							max_position_embeddings
       a_      :	Optional[Any]      =							type_vocab_size
       a_      :	List[Any]      =							type_sequence_label_size
       a_      :	Tuple      =							initializer_range
       a_      :	Union[str, Any]      =							num_labels
       a_      :	Dict      =							num_choices
       a_      :	Dict      =							relative_attention
       a_      :	List[Any]      =							position_biased_input
       a_      :	List[Any]      =							pos_att_type
       a_      :	List[Any]      =							scope


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size )

       a_      :	Union[str, Any]      =							None
       if self.use_input_mask:
         a_      :	Dict      =							ids_tensor([self.batch_size, self.seq_length]							,		vocab_size=2 )

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

       a_      :	Tuple      =							None
       a_      :	Tuple      =							None
       a_      :	List[str]      =							None
       if self.use_labels:
         a_      :	Dict      =							ids_tensor([self.batch_size]							,		self.type_sequence_label_size )
         a_      :	int      =							ids_tensor([self.batch_size, self.seq_length]							,		self.num_labels )
         a_      :	Dict      =							ids_tensor([self.batch_size]							,		self.num_choices )

       a_      :	Optional[Any]      =							self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return DebertaVaConfig(
           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							,		initializer_range=self.initializer_range							,		relative_attention=self.relative_attention							,		position_biased_input=self.position_biased_input							,		pos_att_type=self.pos_att_type							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       self.parent.assertListEqual(list(result.loss.size() )							,		[] )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DebertaVaModel(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Optional[Any]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_ )[0]
       a_      :	Optional[int]      =							model(lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_ )[0]
       a_      :	List[str]      =							model(lowerCAmelCase_ )[0]

       self.parent.assertListEqual(list(sequence_output.size() )							,		[self.batch_size, self.seq_length, self.hidden_size] )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							DebertaVaForMaskedLM(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Optional[int]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_ )

       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.num_labels
       a_      :	str      =							DebertaVaForSequenceClassification(lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Dict      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertListEqual(list(result.logits.size() )							,		[self.batch_size, self.num_labels] )
       self.check_loss_output(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.num_labels
       a_      :	str      =							DebertaVaForTokenClassification(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Any      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.num_labels) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Dict      =							DebertaVaForQuestionAnswering(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	int      =							model(
           lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		start_positions=lowerCAmelCase_							,		end_positions=lowerCAmelCase_							,		)
       self.parent.assertEqual(result.start_logits.shape							,		(self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape							,		(self.batch_size, self.seq_length) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DebertaVaForMultipleChoice(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	int      =							input_ids.unsqueeze(1 ).expand(-1							,		self.num_choices							,		-1 ).contiguous()
       a_      :	Tuple      =							token_type_ids.unsqueeze(1 ).expand(-1							,		self.num_choices							,		-1 ).contiguous()
       a_      :	List[str]      =							input_mask.unsqueeze(1 ).expand(-1							,		self.num_choices							,		-1 ).contiguous()
       a_      :	Optional[Any]      =							model(
           lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_							,		)
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_choices) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.prepare_config_and_inputs()
       (
           (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , (
           a_
       )      , 
       )      :	Optional[int]      =							config_and_inputs
       a_      :	Optional[Any]      =							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					(
         (
             DebertaVaModel,
             DebertaVaForMaskedLM,
             DebertaVaForSequenceClassification,
             DebertaVaForTokenClassification,
             DebertaVaForQuestionAnswering,
             DebertaVaForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     a_    	=					(
         {
             "feature-extraction": DebertaVaModel,
             "fill-mask": DebertaVaForMaskedLM,
             "question-answering": DebertaVaForQuestionAnswering,
             "text-classification": DebertaVaForSequenceClassification,
             "token-classification": DebertaVaForTokenClassification,
             "zero-shot": DebertaVaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     a_    	=					True
     a_    	=					False
     a_    	=					False
     a_    	=					False
     a_    	=					False


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							DebertaVaModelTester(self )
       a_      :	Union[str, Any]      =							ConfigTester(self							,		config_class=lowerCAmelCase_							,		hidden_size=37 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.config_tester.run_common_tests()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_ )






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_      :	Tuple      =							DebertaVaModel.from_pretrained(lowerCAmelCase_ )
         self.assertIsNotNone(lowerCAmelCase_ )







@require_torch
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     @unittest.skip(reason="""Model not available yet""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge""" )

       a_      :	Optional[Any]      =							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
       a_      :	List[Any]      =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
       with torch.no_grad():
         a_      :	List[Any]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_ )[0]
       # compare the actual values for a slice.
       a_      :	Any      =							torch.tensor(
           [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,		lowerCAmelCase_							,		atol=1E-4 )							,		f'''{output[:, 1:4, 1:4]}''' )




'''simple docstring'''

def 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

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

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

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


__snake_case:  List[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],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


