






"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						)







class   lowercase__     :


			def __init__( self	:    int  ,			snake_case__	:    List[str]  ,			snake_case__	:    List[str]=13  ,			snake_case__	:    Optional[int]=7  ,			snake_case__	:    int=True  ,			snake_case__	:    Tuple=True  ,			snake_case__	:    Tuple=True  ,			snake_case__	:    Any=True  ,			snake_case__	:    Optional[Any]=99  ,			snake_case__	:    Tuple=32  ,			snake_case__	:    Optional[Any]=2  ,			snake_case__	:    int=4  ,			snake_case__	:    Union[str, Any]=37  ,			snake_case__	:    Optional[Any]="gelu"  ,			snake_case__	:    int=0.1  ,			snake_case__	:    Optional[int]=0.1  ,			snake_case__	:    int=512  ,			snake_case__	:    int=16  ,			snake_case__	:    Any=2  ,			snake_case__	:    Optional[Any]=0.02  ,			snake_case__	:    Optional[int]=False  ,			snake_case__	:    Optional[int]=True  ,			snake_case__	:    List[str]="None"  ,			snake_case__	:    Optional[Any]=3  ,			snake_case__	:    Tuple=4  ,			snake_case__	:    Union[str, Any]=None  ,			):
										lowerCamelCase_			:				Any										=parent
										lowerCamelCase_			:				List[Any]										=batch_size
										lowerCamelCase_			:				Tuple										=seq_length
										lowerCamelCase_			:				List[str]										=is_training
										lowerCamelCase_			:				Optional[Any]										=use_input_mask
										lowerCamelCase_			:				Dict										=use_token_type_ids
										lowerCamelCase_			:				Dict										=use_labels
										lowerCamelCase_			:				Union[str, Any]										=vocab_size
										lowerCamelCase_			:				str										=hidden_size
										lowerCamelCase_			:				Optional[int]										=num_hidden_layers
										lowerCamelCase_			:				Optional[int]										=num_attention_heads
										lowerCamelCase_			:				int										=intermediate_size
										lowerCamelCase_			:				Optional[int]										=hidden_act
										lowerCamelCase_			:				int										=hidden_dropout_prob
										lowerCamelCase_			:				List[str]										=attention_probs_dropout_prob
										lowerCamelCase_			:				List[str]										=max_position_embeddings
										lowerCamelCase_			:				Union[str, Any]										=type_vocab_size
										lowerCamelCase_			:				List[str]										=type_sequence_label_size
										lowerCamelCase_			:				Tuple										=initializer_range
										lowerCamelCase_			:				int										=num_labels
										lowerCamelCase_			:				List[str]										=num_choices
										lowerCamelCase_			:				Optional[Any]										=relative_attention
										lowerCamelCase_			:				Optional[Any]										=position_biased_input
										lowerCamelCase_			:				str										=pos_att_type
										lowerCamelCase_			:				str										=scope


			def       UpperCAmelCase__		( self	:    Optional[int]   ):
										lowerCamelCase_			:				Tuple										=ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size   )

										lowerCamelCase_			:				Any										=None
										if self.use_input_mask:
																	lowerCamelCase_			:				Optional[Any]										=random_attention_mask([self.batch_size, self.seq_length]   )

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

										lowerCamelCase_			:				Optional[int]										=None
										lowerCamelCase_			:				List[str]										=None
										lowerCamelCase_			:				Tuple										=None
										if self.use_labels:
																	lowerCamelCase_			:				Union[str, Any]										=ids_tensor([self.batch_size]  ,			self.type_sequence_label_size   )
																	lowerCamelCase_			:				List[str]										=ids_tensor([self.batch_size, self.seq_length]  ,			self.num_labels   )

										lowerCamelCase_			:				List[str]										=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  ,			relative_attention=self.relative_attention  ,			position_biased_input=self.position_biased_input  ,			initializer_range=self.initializer_range  ,			return_dict=snake_case__  ,			)

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


			def       UpperCAmelCase__		( self	:    str  ,			snake_case__	:    str  ,			snake_case__	:    Tuple  ,			snake_case__	:    Optional[int]  ,			snake_case__	:    Tuple  ,			snake_case__	:    Optional[Any]  ,			snake_case__	:    Dict  ,			snake_case__	:    List[str]   ):
										lowerCamelCase_			:				Union[str, Any]										=TFDebertaVaModel(config=snake_case__   )
										lowerCamelCase_			:				Optional[Any]										={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										lowerCamelCase_			:				Optional[Any]										=[input_ids, input_mask]
										lowerCamelCase_			:				List[str]										=model(snake_case__   )

										lowerCamelCase_			:				Optional[Any]										=model(snake_case__   )

										self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.hidden_size)   )


			def       UpperCAmelCase__		( self	:    Union[str, Any]  ,			snake_case__	:    int  ,			snake_case__	:    Dict  ,			snake_case__	:    Optional[Any]  ,			snake_case__	:    List[str]  ,			snake_case__	:    Tuple  ,			snake_case__	:    Tuple  ,			snake_case__	:    str   ):
										lowerCamelCase_			:				Optional[Any]										=TFDebertaVaForMaskedLM(config=snake_case__   )
										lowerCamelCase_			:				Optional[Any]										={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										lowerCamelCase_			:				Tuple										=model(snake_case__   )
										self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.vocab_size)   )


			def       UpperCAmelCase__		( self	:    Union[str, Any]  ,			snake_case__	:    int  ,			snake_case__	:    Union[str, Any]  ,			snake_case__	:    int  ,			snake_case__	:    List[Any]  ,			snake_case__	:    Tuple  ,			snake_case__	:    Tuple  ,			snake_case__	:    Tuple   ):
										lowerCamelCase_			:				int										=self.num_labels
										lowerCamelCase_			:				Tuple										=TFDebertaVaForSequenceClassification(config=snake_case__   )
										lowerCamelCase_			:				Tuple										={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										lowerCamelCase_			:				List[Any]										=model(snake_case__   )
										self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)   )


			def       UpperCAmelCase__		( self	:    Union[str, Any]  ,			snake_case__	:    str  ,			snake_case__	:    int  ,			snake_case__	:    Dict  ,			snake_case__	:    List[Any]  ,			snake_case__	:    int  ,			snake_case__	:    Tuple  ,			snake_case__	:    Optional[Any]   ):
										lowerCamelCase_			:				str										=self.num_labels
										lowerCamelCase_			:				int										=TFDebertaVaForTokenClassification(config=snake_case__   )
										lowerCamelCase_			:				Optional[Any]										={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										lowerCamelCase_			:				Tuple										=model(snake_case__   )
										self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.num_labels)   )


			def       UpperCAmelCase__		( self	:    Dict  ,			snake_case__	:    Tuple  ,			snake_case__	:    List[Any]  ,			snake_case__	:    List[str]  ,			snake_case__	:    Optional[int]  ,			snake_case__	:    Optional[int]  ,			snake_case__	:    Optional[Any]  ,			snake_case__	:    Optional[Any]   ):
										lowerCamelCase_			:				Union[str, Any]										=TFDebertaVaForQuestionAnswering(config=snake_case__   )
										lowerCamelCase_			:				Optional[int]										={
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										lowerCamelCase_			:				List[str]										=model(snake_case__   )
										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       UpperCAmelCase__		( self	:    Tuple   ):
										lowerCamelCase_			:				Union[str, Any]										=self.prepare_config_and_inputs()
										(
										    lowerCamelCase_				
										)			:				Optional[int]										=config_and_inputs
										lowerCamelCase_			:				Tuple										={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_tf
class   lowercase__     (				snake_case__,  snake_case__,  unittest.TestCase  ):
			_UpperCAmelCase						:Any       =					(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			_UpperCAmelCase						:Any       =					(
			    {
			        """feature-extraction""": TFDebertaVaModel,
			        """fill-mask""": TFDebertaVaForMaskedLM,
			        """question-answering""": TFDebertaVaForQuestionAnswering,
			        """text-classification""": TFDebertaVaForSequenceClassification,
			        """token-classification""": TFDebertaVaForTokenClassification,
			        """zero-shot""": TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			_UpperCAmelCase						:Any       =					False
			_UpperCAmelCase						:Tuple       =					False


			def       UpperCAmelCase__		( self	:    Any   ):
										lowerCamelCase_			:				Any										=TFDebertaVaModelTester(self   )
										lowerCamelCase_			:				List[Any]										=ConfigTester(self  ,			config_class=snake_case__  ,			hidden_size=37   )


			def       UpperCAmelCase__		( self	:    Union[str, Any]   ):
										self.config_tester.run_common_tests()


			def       UpperCAmelCase__		( self	:    Optional[Any]   ):
										lowerCamelCase_			:				str										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__   )


			def       UpperCAmelCase__		( self	:    List[Any]   ):
										lowerCamelCase_			:				Tuple										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*snake_case__   )


			def       UpperCAmelCase__		( self	:    Optional[Any]   ):
										lowerCamelCase_			:				Dict										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*snake_case__   )


			def       UpperCAmelCase__		( self	:    Optional[Any]   ):
										lowerCamelCase_			:				Union[str, Any]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*snake_case__   )


			def       UpperCAmelCase__		( self	:    Tuple   ):
										lowerCamelCase_			:				int										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*snake_case__   )


			@slow
			def       UpperCAmelCase__		( self	:    Optional[Any]   ):
										lowerCamelCase_			:				Tuple										=TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
										self.assertIsNotNone(snake_case__   )







@require_tf
class   lowercase__     (				unittest.TestCase  ):


			@unittest.skip(reason="Model not available yet"   )
			def       UpperCAmelCase__		( self	:    Optional[int]   ):
										pass


			@slow
			def       UpperCAmelCase__		( self	:    Any   ):
										lowerCamelCase_			:				Dict										=TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
										lowerCamelCase_			:				Any										=tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
										lowerCamelCase_			:				Any										=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
										lowerCamelCase_			:				List[Any]										=model(snake_case__  ,			attention_mask=snake_case__   )[0]

										lowerCamelCase_			:				List[str]										=tf.constant(
										    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]   )
										tf.debugging.assert_near(output[:, 1:4, 1:4]  ,			snake_case__  ,			atol=1E-4   )







def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[False] * len(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						[]
		queue.append(a )
		SCREAMING_SNAKE_CASE_				:int				     =						True

		while queue:
				SCREAMING_SNAKE_CASE_				:int				     =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(a )
								SCREAMING_SNAKE_CASE_				:Tuple				     =						True
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						u

		return visited[t]


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						[-1] * (len(a ))
		SCREAMING_SNAKE_CASE_				:Tuple				     =						0
		while bfs(a     , a     , a     , a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float("Inf" )
				SCREAMING_SNAKE_CASE_				:str				     =						sink

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

				max_flow += path_flow
				SCREAMING_SNAKE_CASE_				:Dict				     =						sink

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


SCREAMING_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],
]

SCREAMING_SNAKE_CASE__					,    SCREAMING_SNAKE_CASE__     					=			0, 5
print(ford_fulkerson(graph, source, sink))






