


"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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 (
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForMultipleChoice,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerModel,
       )
       from transformers.models.roformer.modeling_tf_roformer import (
           TFRoFormerSelfAttention,
           TFRoFormerSinusoidalPositionalEmbedding,
       )





class    lowerCamelCase__						:
      def __init__(		self	:	Dict      ,       A_	:	List[str]      ,       A_	:	str=1_3      ,       A_	:	str=7      ,       A_	:	Any=True      ,       A_	:	Optional[int]=True      ,       A_	:	Optional[int]=True      ,       A_	:	Optional[Any]=True      ,       A_	:	Optional[Any]=9_9      ,       A_	:	List[Any]=3_2      ,       A_	:	Tuple=2      ,       A_	:	Optional[int]=4      ,       A_	:	Optional[Any]=3_7      ,       A_	:	List[str]="gelu"      ,       A_	:	Optional[Any]=0.1      ,       A_	:	Tuple=0.1      ,       A_	:	int=5_1_2      ,       A_	:	str=1_6      ,       A_	:	Tuple=2      ,       A_	:	List[str]=0.02      ,       A_	:	int=3      ,       A_	:	List[str]=4      ,       A_	:	Tuple=None      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	1_3
       __lowercase									=	7
       __lowercase									=	True
       __lowercase									=	True
       __lowercase									=	True
       __lowercase									=	True
       __lowercase									=	9_9
       __lowercase									=	3_2
       __lowercase									=	2
       __lowercase									=	4
       __lowercase									=	3_7
       __lowercase									=	"""gelu"""
       __lowercase									=	0.1
       __lowercase									=	0.1
       __lowercase									=	5_1_2
       __lowercase									=	1_6
       __lowercase									=	2
       __lowercase									=	0.02
       __lowercase									=	3
       __lowercase									=	4
       __lowercase									=	None
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


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

       __lowercase									=	None
       if self.use_input_mask:
        __lowercase									=	random_attention_mask([self.batch_size, self.seq_length]							)

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

       __lowercase									=	None
       __lowercase									=	None
       __lowercase									=	None
       if self.use_labels:
        __lowercase									=	ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)
        __lowercase									=	ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels							)
        __lowercase									=	ids_tensor([self.batch_size]      ,       self.num_choices							)

       __lowercase									=	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      ,       initializer_range=self.initializer_range      ,       return_dict=A_      ,       )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Any      ,       A_	:	str      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]      ,       A_	:	Optional[Any]      ,       A_	:	List[str]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	TFRoFormerModel(config=A_							)
       __lowercase									=	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

       __lowercase									=	[input_ids, input_mask]
       __lowercase									=	model(A_							)

       __lowercase									=	model(A_							)

       self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Dict      ,       A_	:	List[Any]      ,       A_	:	Optional[int]      ,       A_	:	List[str]      ,       A_	:	str      ,       A_	:	List[str]      ,       A_	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	True
       __lowercase									=	TFRoFormerForCausalLM(config=A_							)
       __lowercase									=	{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       __lowercase									=	model(A_							)["""logits"""]
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape							)      ,       [self.batch_size, self.seq_length, self.vocab_size]							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[Any]      ,       A_	:	Any      ,       A_	:	Optional[Any]      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]      ,       A_	:	int      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	TFRoFormerForMaskedLM(config=A_							)
       __lowercase									=	{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	str      ,       A_	:	Optional[Any]      ,       A_	:	int      ,       A_	:	Tuple      ,       A_	:	Optional[int]      ,       A_	:	Any      ,       A_	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	TFRoFormerForSequenceClassification(config=A_							)
       __lowercase									=	{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }

       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       A_	:	List[str]      ,       A_	:	Dict      ,       A_	:	Optional[int]      ,       A_	:	Tuple      ,       A_	:	Optional[Any]      ,       A_	:	Optional[int]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	self.num_choices
       __lowercase									=	TFRoFormerForMultipleChoice(config=A_							)
       __lowercase									=	tf.tile(tf.expand_dims(A_      ,       1							)      ,       (1, self.num_choices, 1)							)
       __lowercase									=	tf.tile(tf.expand_dims(A_      ,       1							)      ,       (1, self.num_choices, 1)							)
       __lowercase									=	tf.tile(tf.expand_dims(A_      ,       1							)      ,       (1, self.num_choices, 1)							)
       __lowercase									=	{
           """input_ids""": multiple_choice_inputs_ids,
           """attention_mask""": multiple_choice_input_mask,
           """token_type_ids""": multiple_choice_token_type_ids,
       }
       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_choices)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Any      ,       A_	:	Dict      ,       A_	:	List[str]      ,       A_	:	Optional[Any]      ,       A_	:	Tuple      ,       A_	:	Optional[int]      ,       A_	:	str							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	TFRoFormerForTokenClassification(config=A_							)
       __lowercase									=	{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }
       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Tuple      ,       A_	:	Optional[int]      ,       A_	:	Tuple      ,       A_	:	Optional[int]      ,       A_	:	Union[str, Any]      ,       A_	:	Any      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	TFRoFormerForQuestionAnswering(config=A_							)
       __lowercase									=	{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
       }

       __lowercase									=	model(A_							)
       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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.prepare_config_and_inputs()
       (
           (
           __lowercase
       )							,		(
           __lowercase
       )							,		(
           __lowercase
       )							,		(
           __lowercase
       )							,		(
           __lowercase
       )							,		(
           __lowercase
       )							,		(
           __lowercase
       )							,		
       )									=	config_and_inputs
       __lowercase									=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_tf
class    lowerCamelCase__						(		_a			,   _a			,   unittest.TestCase    ):
      a :     Optional[Any]											=		(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      a :     int											=		(
          {
              """feature-extraction""": TFRoFormerModel,
              """fill-mask""": TFRoFormerForMaskedLM,
              """question-answering""": TFRoFormerForQuestionAnswering,
              """text-classification""": TFRoFormerForSequenceClassification,
              """text-generation""": TFRoFormerForCausalLM,
              """token-classification""": TFRoFormerForTokenClassification,
              """zero-shot""": TFRoFormerForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      a :     Optional[Any]											=		False
      a :     List[Any]											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       A_	:	Tuple      ,       A_	:	Dict      ,       A_	:	str      ,       A_	:	List[str]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

       return False
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	TFRoFormerModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=A_      ,       hidden_size=3_7							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""							)
       self.assertIsNotNone(A_							)






@require_tf
class    lowerCamelCase__						(		unittest.TestCase    ):
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""							)
       __lowercase									=	tf.constant([[0, 1, 2, 3, 4, 5]]							)
       __lowercase									=	model(A_							)[0]

       # TODO Replace vocab size
       __lowercase									=	5_0_0_0_0

       __lowercase									=	[1, 6, vocab_size]
       self.assertEqual(output.shape      ,       A_							)

       print(output[:, :3, :3]							)

       # TODO Replace values below with what was printed above.
       __lowercase									=	tf.constant(
           [
               [
                   [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
                   [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
                   [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
               ]
           ]							)
       tf.debugging.assert_near(output[:, :3, :3]      ,       A_      ,       atol=1e-4							)






@require_tf
class    lowerCamelCase__						(		unittest.TestCase    ):
      a :     Any											=		1e-4
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	tf.constant([[4, 1_0]]							)
       __lowercase									=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      ,       embedding_dim=6							)

       __lowercase									=	emba(input_ids.shape							)
       __lowercase									=	tf.constant(
           [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]							)

       tf.debugging.assert_near(A_      ,       A_      ,       atol=self.tolerance							)






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	tf.constant(
           [
               [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
               [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
               [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
           ]							)
       __lowercase									=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2      ,       embedding_dim=5_1_2							)
       emba([2, 1_6, 5_1_2]							)
       __lowercase									=	emba.weight[:3, :5]

       tf.debugging.assert_near(A_      ,       A_      ,       atol=self.tolerance							)






@require_tf
class    lowerCamelCase__						(		unittest.TestCase    ):
      a :     Union[str, Any]											=		1e-4
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4      ,       dtype=tf.floataa							)      ,       shape=(2, 1_2, 1_6, 6_4)							) / 1_0_0

       __lowercase									=	-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4      ,       dtype=tf.floataa							)      ,       shape=(2, 1_2, 1_6, 6_4)							) / 1_0_0

       __lowercase									=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2      ,       embedding_dim=6_4							)
       __lowercase									=	embed_positions([2, 1_6, 7_6_8]							)[None, None, :, :]

       __lowercase							,		__lowercase									=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           A_      ,       A_      ,       A_							)

       __lowercase									=	tf.constant(
           [
               [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
               [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
               [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
               [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
               [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
               [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
           ]							)
       __lowercase									=	tf.constant(
           [
               [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
               [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
               [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
               [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
               [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
               [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
           ]							)

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]      ,       A_      ,       atol=self.tolerance							)
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]      ,       A_      ,       atol=self.tolerance							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[False] * len(UpperCamelCase__ )
 __lowercase									=	[]
 queue.append(UpperCamelCase__ )
 __lowercase									=	True

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

 return visited[t]


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 __lowercase									=	[-1] * (len(UpperCamelCase__ ))
 __lowercase									=	0
 while bfs(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	float("""Inf""" )
  __lowercase									=	sink

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

  max_flow += path_flow
  __lowercase									=	sink

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


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],
]

UpperCAmelCase__							,      UpperCAmelCase__  							=0, 5

print(ford_fulkerson(graph, source, sink))
